[E2] Main Analysis, N=50 – Effect of Aggregation on Generalizations Study Results
Francis Nguyen, Xiaoli Qiao, Jeffrey Heer, Jessica Hullman
12/22/2019
Data Preliminaries
We begin by cleaning up our data, removing responses where users chose not to include a generalization. We used the same power analysis from Experiment 1 to conclude we need 90 participants worth of data. Overall, we recruited 102 participants. Four participants were excluded because more than half their generalizations consisted of no generalizations. Eight were excluded because the majority of their generalizations were not about the data they saw, per our pre-registered exclusion criteria.
We subset from a total of 1880 to 1608, removing a total of 272 generalizations, representing trials where a participant did not provide a generalization for the presented stimuli or generalizations that misinterpreted the presented stimuli.
d_50 <- read.csv("./data/[E2]N=50-Full-Cleaned.tsv", sep="\t")
d_50$confidence <- suppressWarnings(as.numeric(as.character(paste(d_50$confidence))))
d_50$initSliderValue <- suppressWarnings(as.numeric(as.character(paste(d_50$initSliderValue))))
sapply(d_50, class)
df_50 <- subset(d_50, d_50$correct!="NA")
nrow(df_50)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2787.60 -173.83 22.41 18.32 283.64 2564.36## [1] 861.1865Descriptive Statistics
We look at overall summaries of the aggregation strategy. In addition, we coded each generalization into a class as specified by our pre-registration, and summarize the results.
There are less generalizations made in the mean aggregation condition. In addition, the most common generalizations were categorized into the mean or shape classes, with the next most frequent being correlation and rank. There were extremely few variance generalizations, likely because of the strict nature of the coding for this generalization class — participants had to explicitly mention the variance of data in a view. On average per participant we encoded 3.09 ± 1.86 correlation, 4.69 ± 3.22 mean, 2.93 ± 2.73 rank, 7.27 ± 4.60 shape, and 0.22 ± 0 variance generalizations. (Compared to 3.13 ± 1.76 correlation, 6.92 ± 4.23 mean, 2.44 ± 2.13 rank, 6.81 ± 5.12 shape, 0.04 ± 0 variance in E1).
To see this better, we plot the distribution of generalizations across generalization class. We observe that participants made the most shape class generalizations with the disaggregation condition, the most rank class generalizations with the mean condition and the most mean class generalizations with the mean aggregation condition.
summary(df_50$aggStrat)## disagg disagg+mean mean
## 550 558 500# summary(df_50$)
suppressWarnings(ddply(df_50, c("insightClass", "workerId"), summarise,
n=nrow(df_50),
k=sum(df_50$insightClass == insightClass & df_50$workerId == workerId))) %>%
ddply(c("insightClass"), summarise,
generalizationClassTotal=sum(k),
percentTotal=sum(k)/nrow(df_50),
avgNumberPerParticipant=sum(k) / 90,
sd=sd(k))## insightClass generalizationClassTotal percentTotal avgNumberPerParticipant
## 1 correlation 276 0.171641791 3.06666667
## 2 mean 420 0.261194030 4.66666667
## 3 rank 262 0.162935323 2.91111111
## 4 shape 648 0.402985075 7.20000000
## 5 variance 2 0.001243781 0.02222222
## sd
## 1 1.973880
## 2 3.172175
## 3 2.733048
## 4 4.683115
## 5 0.000000suppressWarnings(ddply(df_50, c("aggStrat","insightClass"), summarise,
n=sum(correct==TRUE)+sum(correct==FALSE),
k=sum(df_50$aggStrat == aggStrat),
percAggStrat=n/k))## aggStrat insightClass n k percAggStrat
## 1 disagg correlation 81 550 0.147272727
## 2 disagg mean 57 550 0.103636364
## 3 disagg rank 76 550 0.138181818
## 4 disagg shape 335 550 0.609090909
## 5 disagg variance 1 550 0.001818182
## 6 disagg+mean correlation 112 558 0.200716846
## 7 disagg+mean mean 136 558 0.243727599
## 8 disagg+mean rank 65 558 0.116487455
## 9 disagg+mean shape 245 558 0.439068100
## 10 mean correlation 83 500 0.166000000
## 11 mean mean 227 500 0.454000000
## 12 mean rank 121 500 0.242000000
## 13 mean shape 68 500 0.136000000
## 14 mean variance 1 500 0.002000000suppressWarnings(ddply(df_50, c("aggStrat"), summarise,
n=nrow(df_50),
k=sum(df_50$aggStrat == aggStrat),
pbar = k/n,
se = sqrt(pbar*(1 - pbar)/n)))## aggStrat n k pbar se
## 1 disagg 1608 550 0.3420398 0.01183028
## 2 disagg+mean 1608 558 0.3470149 0.01187087
## 3 mean 1608 500 0.3109453 0.01154319suppressWarnings(ddply(df_50, c("aggStrat", "insightClass"), summarise,
n=nrow(df_50[df_50$aggStrat == aggStrat,]),
k=sum(df_50$aggStrat == aggStrat & df_50$insightClass == insightClass),
pbar = k/n,
se = sqrt(pbar*(1 - pbar)/n),
min=pbar-1.96*se,
max=pbar+1.96*se))## aggStrat insightClass n k pbar se min
## 1 disagg correlation 550 81 0.147272727 0.015110711 0.117655734
## 2 disagg mean 550 57 0.103636364 0.012996214 0.078163784
## 3 disagg rank 550 76 0.138181818 0.014714718 0.109340972
## 4 disagg shape 550 335 0.609090909 0.020806431 0.568310304
## 5 disagg variance 550 1 0.001818182 0.001816528 -0.001742213
## 6 disagg+mean correlation 558 112 0.200716846 0.016956068 0.167482953
## 7 disagg+mean mean 558 136 0.243727599 0.018174998 0.208104602
## 8 disagg+mean rank 558 65 0.116487455 0.013580909 0.089868874
## 9 disagg+mean shape 558 245 0.439068100 0.021008929 0.397890600
## 10 mean correlation 500 83 0.166000000 0.016639952 0.133385694
## 11 mean mean 500 227 0.454000000 0.022265848 0.410358937
## 12 mean rank 500 121 0.242000000 0.019153903 0.204458350
## 13 mean shape 500 68 0.136000000 0.015329971 0.105953258
## 14 mean variance 500 1 0.002000000 0.001997999 -0.001916078
## max
## 1 0.176889721
## 2 0.129108943
## 3 0.167022665
## 4 0.649871515
## 5 0.005378577
## 6 0.233950739
## 7 0.279350595
## 8 0.143106037
## 9 0.480245601
## 10 0.198614306
## 11 0.497641063
## 12 0.279541650
## 13 0.166046742
## 14 0.005916078summary(df_50$insightClass)## correlation mean misinterpretation rank
## 276 420 0 262
## shape variance
## 648 2
Accuracy
Accuracy by Aggregation Strategy
We calculate summary statistics for accuracy for each of our aggregation strategies to get a better sense of our data. While the mean aggregate condition generalizations have a lower frequency, there is no significant difference between accuracies of aggregation strategy.
df_50 %>%
ddply(~aggStrat, summarise,
Correct=sum(correct==TRUE),
Incorrect=sum(correct==FALSE),
Total=Incorrect+Correct,
Accuracy=Correct/(Incorrect+Correct)
)## aggStrat Correct Incorrect Total Accuracy
## 1 disagg 277 273 550 0.5036364
## 2 disagg+mean 303 255 558 0.5430108
## 3 mean 249 251 500 0.4980000# Accounting for differences in workers
df_50_agg_accuracy <- df_50 %>%
ddply(.(aggStrat, workerId), summarise,
Correct=sum(correct==TRUE),
Incorrect=sum(correct==FALSE),
PercCorrect=Correct/(Incorrect+Correct),
Total=Incorrect+Correct) %>%
ddply(~aggStrat, summarise,
N = sum((Total)),
meanAcc = mean(PercCorrect),
sd = sd(PercCorrect),
se = sd / sqrt(N))
df_50_agg_accuracy## aggStrat N meanAcc sd se
## 1 disagg 550 0.4946830 0.2303731 0.009823141
## 2 disagg+mean 558 0.5316272 0.2474540 0.010475565
## 3 mean 500 0.4979605 0.2770429 0.012389737
Accuracy by Aggregation Strategy, Faceted by Generalization Class
Next, we look at the the accuracy of aggregation strategy, faceted by generalization class. We also look at the breakdown in percentages per aggregation condition (PercTotalofAggStrat). We plot the results for visual aid. We observe that participants had low accuracy for rank classed generalizations. This is likely because the N=50 sample size of E2 expressed a difference in means or values that wasn’t observed in the population.
## aggStrat insightClass Correct Incorrect Total Accuracy
## 1 disagg correlation 54 27 81 0.6666667
## 2 disagg mean 28 29 57 0.4912281
## 3 disagg rank 12 64 76 0.1578947
## 4 disagg shape 182 153 335 0.5432836
## 5 disagg variance 1 0 1 1.0000000
## 6 disagg+mean correlation 83 29 112 0.7410714
## 7 disagg+mean mean 61 75 136 0.4485294
## 8 disagg+mean rank 15 50 65 0.2307692
## 9 disagg+mean shape 144 101 245 0.5877551
## 10 mean correlation 61 22 83 0.7349398
## 11 mean mean 98 129 227 0.4317181
## 12 mean rank 52 69 121 0.4297521
## 13 mean shape 37 31 68 0.5441176
## 14 mean variance 1 0 1 1.0000000
## PercTotalofAggStrat
## 1 0.147272727
## 2 0.103636364
## 3 0.138181818
## 4 0.609090909
## 5 0.001818182
## 6 0.200716846
## 7 0.243727599
## 8 0.116487455
## 9 0.439068100
## 10 0.166000000
## 11 0.454000000
## 12 0.242000000
## 13 0.136000000
## 14 0.002000000
Accuracy by Data Type Combination
We analyze accuracy with respect to data type combination (univariate, 1 quantitative x 1 nominal, 2 quantitative). We observe that the 1 quantitative x 1 nominal data type combination fares worse compared to the univariate and 2 quantitative data type combinations: 39.8% for 1 quantitative x 1 nominal vs 65.6% accurate for 2 quantiative and 57.6% accurate for the univariate data type.
## dataTypeCombination Correct Incorrect Accuracy Total
## 1 nominalBivariate 254 392 0.3931889 646
## 2 quantBivariate 296 195 0.6028513 491
## 3 univariate 279 192 0.5923567 471## dataTypeCombination aggStrat Correct Incorrect Accuracy Total
## 1 nominalBivariate disagg 75 157 0.3232759 232
## 2 nominalBivariate disagg+mean 77 135 0.3632075 212
## 3 nominalBivariate mean 102 100 0.5049505 202
## 4 quantBivariate disagg 96 57 0.6274510 153
## 5 quantBivariate disagg+mean 112 51 0.6871166 163
## 6 quantBivariate mean 88 87 0.5028571 175
## 7 univariate disagg 106 59 0.6424242 165
## 8 univariate disagg+mean 114 69 0.6229508 183
## 9 univariate mean 59 64 0.4796748 123
## dataTypeCombination N meanAcc sd se
## 1 nominalBivariate 646 0.3982476 0.2216479 0.00872062
## 2 quantBivariate 491 0.6564071 0.2640941 0.01191840
## 3 univariate 471 0.5769841 0.2590357 0.01193573## dataTypeCombination aggStrat N meanAcc sd se
## 1 nominalBivariate disagg 232 0.2781893 0.3289175 0.02159449
## 2 nominalBivariate disagg+mean 212 0.3204123 0.3725691 0.02558815
## 3 nominalBivariate mean 202 0.4747619 0.3820878 0.02688360
## 4 quantBivariate disagg 153 0.6487179 0.3627670 0.02932797
## 5 quantBivariate disagg+mean 163 0.6757143 0.3902360 0.03056564
## 6 quantBivariate mean 175 0.5178912 0.4197166 0.03172759
## 7 univariate disagg 165 0.6526316 0.3729865 0.02903696
## 8 univariate disagg+mean 183 0.6117778 0.3834584 0.02834606
## 9 univariate mean 123 0.4294872 0.4474710 0.04034710
Bayesian Models Setup
Next, we’ll run some Bayesian regressions. First let’s set up the data for the modeling.
Accuracy Bayesian Model
We run a hierarchical logistic regression model to evaluate the impact of aggregation strategy on accuracy as per our pre-registration. We report the results as the distribution of posterior mean estimates for effects of both aggregation strategies and trial and the standard eviation for varying intercepts of participant ID and view ID. We find that there doesn’t seem to be an evidence of effect, as all intervals are centered near 0.
Let’s plot results
## Mean StdDev lower 0.95 upper 0.95 n_eff Rhat
## bnoagg 0.0625821 0.1358390 -0.19168594 0.3412335 5065.185 1.000581
## bmean 0.1955359 0.1341102 -0.07491764 0.4526900 5222.715 1.000493## Mean StdDev lower 0.95 upper 0.95 n_eff Rhat
## bnoagg 1.064582 1.145497 0.8255661 1.406682 Inf 2.719863
## bmean 1.215962 1.143519 0.9278199 1.572537 Inf 2.719621## a_worker[1] a_worker[2] a_worker[3] a_worker[4] a_worker[5] a_worker[6]
## 0.9212476 0.7532744 1.3710510 1.7718874 1.2772375 0.9051077
## a_worker[7] a_worker[8] a_worker[9] a_worker[10] a_worker[11] a_worker[12]
## 0.9894245 1.0042120 0.8283907 0.3463557 1.0710001 1.1409556
## a_worker[13] a_worker[14] a_worker[15] a_worker[16] a_worker[17] a_worker[18]
## 0.7503414 1.0740854 1.1964530 0.9118292 1.0450933 1.1459058
## a_worker[19] a_worker[20] a_worker[21] a_worker[22] a_worker[23] a_worker[24]
## 0.5369385 1.2024097 0.5265641 1.0829262 1.1237699 0.5732189
## a_worker[25] a_worker[26] a_worker[27] a_worker[28] a_worker[29] a_worker[30]
## 1.1678408 1.5083073 1.4179238 1.1368169 0.9134855 0.8914409
## a_worker[31] a_worker[32] a_worker[33] a_worker[34] a_worker[35] a_worker[36]
## 1.3710077 1.0986110 0.8165960 0.9970933 1.0179577 0.8683981
## a_worker[37] a_worker[38] a_worker[39] a_worker[40] a_worker[41] a_worker[42]
## 1.2159083 0.8757022 0.7716069 0.7306902 0.8694430 1.5654633
## a_worker[43] a_worker[44] a_worker[45] a_worker[46] a_worker[47] a_worker[48]
## 0.9555779 0.7407391 1.0561815 0.9252856 0.7736677 1.2276515
## a_worker[49] a_worker[50] a_worker[51] a_worker[52] a_worker[53] a_worker[54]
## 1.6377248 1.0202545 1.4116290 0.8851575 0.8435409 1.5444123
## a_worker[55] a_worker[56] a_worker[57] a_worker[58] a_worker[59] a_worker[60]
## 0.9333509 1.9360158 0.9998592 0.9844474 1.1348232 0.8604492
## a_worker[61] a_worker[62] a_worker[63] a_worker[64] a_worker[65] a_worker[66]
## 1.2599605 1.5890532 0.7018526 1.1210095 1.3613753 1.1447793
## a_worker[67] a_worker[68] a_worker[69] a_worker[70] a_worker[71] a_worker[72]
## 0.9553029 0.9538050 0.7855254 1.0017778 0.9192865 0.9732434
## a_worker[73] a_worker[74] a_worker[75] a_worker[76] a_worker[77] a_worker[78]
## 0.4913457 0.8736546 0.9925756 0.8968843 0.6227254 0.9340170
## a_worker[79] a_worker[80] a_worker[81] a_worker[82] a_worker[83] a_worker[84]
## 1.1074646 1.4538049 1.2012196 1.1015462 0.7657488 1.0537494
## a_worker[85] a_worker[86] a_worker[87] a_worker[88] a_worker[89] a_worker[90]
## 1.0269195 1.4950630 1.1138372 0.7308957 1.1760639 0.9774271
## a_spec[1] a_spec[2] a_spec[3] a_spec[4] a_spec[5] a_spec[6]
## 1.8680418 2.3200978 0.9138829 0.9685228 0.9659162 0.7939936
## a_spec[7] a_spec[8] a_spec[9] a_spec[10] a_spec[11] a_spec[12]
## 1.4820130 0.1466576 1.3253937 0.2861036 0.6994356 2.7701466
## a_spec[13] a_spec[14] a_spec[15] a bnoagg bmean
## 0.7389797 0.8118924 3.3378833 1.0234388 1.0645819 1.2159624
## btrial sigma_worker sigma_spec
## 0.9967779 1.5693017 2.4461468## 105 vector or matrix parameters omitted in display. Use depth=2 to show them.## item value
## 1 bnoagg 2.378249e-02
## 2 bnoagg -1.836837e-01
## 3 bnoagg 2.048361e-01
## 4 bnoagg 3.132291e-01
## 5 bnoagg -2.553755e-03
## 6 bnoagg 3.992789e-02
## 7 bnoagg 9.315147e-02
## 8 bnoagg 1.244863e-01
## 9 bnoagg -6.248207e-02
## 10 bnoagg -6.794239e-02
## 11 bnoagg 3.718299e-01
## 12 bnoagg 2.368651e-01
## 13 bnoagg 1.848713e-01
## 14 bnoagg -2.277066e-01
## 15 bnoagg 3.800091e-02
## 16 bnoagg 3.406166e-01
## 17 bnoagg -1.719569e-01
## 18 bnoagg 8.953619e-02
## 19 bnoagg 1.055602e-01
## 20 bnoagg 1.817440e-02
## 21 bnoagg -1.544493e-01
## 22 bnoagg 1.726138e-01
## 23 bnoagg 4.064241e-02
## 24 bnoagg 1.059747e-01
## 25 bnoagg 9.819356e-02
## 26 bnoagg -1.330655e-01
## 27 bnoagg 3.994140e-02
## 28 bnoagg 1.363516e-01
## 29 bnoagg 4.051032e-02
## 30 bnoagg -3.326679e-02
## 31 bnoagg 1.746923e-01
## 32 bnoagg -2.885323e-01
## 33 bnoagg -2.360157e-02
## 34 bnoagg 9.015212e-02
## 35 bnoagg 2.712936e-01
## 36 bnoagg 1.289933e-01
## 37 bnoagg 3.811947e-02
## 38 bnoagg -5.669782e-02
## 39 bnoagg -8.563751e-02
## 40 bnoagg 2.640458e-01
## 41 bnoagg 5.550277e-02
## 42 bnoagg 7.996829e-02
## 43 bnoagg -1.090641e-01
## 44 bnoagg -5.180900e-02
## 45 bnoagg 2.448623e-01
## 46 bnoagg 1.157423e-01
## 47 bnoagg 1.157595e-01
## 48 bnoagg 5.937297e-02
## 49 bnoagg 6.798480e-02
## 50 bnoagg 3.168082e-01
## 51 bnoagg 2.412991e-01
## 52 bnoagg 2.819756e-01
## 53 bnoagg 4.333840e-02
## 54 bnoagg 2.204326e-01
## 55 bnoagg -4.421278e-03
## 56 bnoagg 3.243801e-01
## 57 bnoagg 2.443919e-02
## 58 bnoagg -7.326561e-02
## 59 bnoagg 1.167076e-01
## 60 bnoagg 3.000442e-01
## 61 bnoagg 6.407432e-02
## 62 bnoagg 8.124872e-02
## 63 bnoagg 2.105292e-01
## 64 bnoagg -1.465004e-01
## 65 bnoagg -9.536538e-02
## 66 bnoagg 8.903688e-02
## 67 bnoagg -9.298787e-02
## 68 bnoagg -2.055467e-01
## 69 bnoagg 2.066764e-01
## 70 bnoagg 1.142879e-01
## 71 bnoagg -3.441330e-02
## 72 bnoagg 1.354069e-01
## 73 bnoagg 3.018861e-01
## 74 bnoagg 1.212066e-01
## 75 bnoagg 3.831864e-02
## 76 bnoagg -4.252531e-02
## 77 bnoagg -1.701181e-01
## 78 bnoagg 8.206388e-02
## 79 bnoagg 1.609319e-01
## 80 bnoagg 2.177459e-01
## 81 bnoagg 1.142965e-01
## 82 bnoagg 9.931053e-02
## 83 bnoagg 2.144706e-01
## 84 bnoagg 1.763716e-01
## 85 bnoagg -1.168425e-02
## 86 bnoagg -1.020906e-01
## 87 bnoagg 2.655609e-01
## 88 bnoagg -7.453186e-02
## 89 bnoagg -2.006289e-01
## 90 bnoagg -1.277166e-01
## 91 bnoagg 3.516670e-02
## 92 bnoagg 1.289291e-01
## 93 bnoagg 2.937417e-01
## 94 bnoagg 2.925122e-01
## 95 bnoagg 2.922576e-01
## 96 bnoagg 2.692097e-01
## 97 bnoagg 1.467080e-01
## 98 bnoagg 1.587270e-01
## 99 bnoagg 2.410461e-01
## 100 bnoagg 2.752348e-02
## 101 bnoagg 1.565402e-01
## 102 bnoagg -1.826703e-01
## 103 bnoagg 1.475711e-01
## 104 bnoagg -1.092373e-01
## 105 bnoagg -5.822293e-02
## 106 bnoagg -8.769639e-02
## 107 bnoagg 6.099073e-02
## 108 bnoagg 1.928870e-01
## 109 bnoagg 2.598338e-02
## 110 bnoagg 7.064053e-03
## 111 bnoagg 6.674574e-02
## 112 bnoagg -1.134932e-02
## 113 bnoagg -3.261853e-01
## 114 bnoagg 3.952537e-02
## 115 bnoagg 5.751233e-02
## 116 bnoagg 2.432229e-01
## 117 bnoagg 1.545705e-01
## 118 bnoagg -1.321426e-01
## 119 bnoagg 4.262532e-02
## 120 bnoagg -1.475241e-01
## 121 bnoagg 1.372629e-01
## 122 bnoagg 2.722127e-01
## 123 bnoagg -1.112791e-01
## 124 bnoagg 7.941606e-02
## 125 bnoagg -1.563203e-01
## 126 bnoagg 1.151913e-01
## 127 bnoagg 5.558524e-02
## 128 bnoagg 1.977725e-01
## 129 bnoagg -2.875728e-01
## 130 bnoagg 2.820595e-01
## 131 bnoagg -2.961592e-01
## 132 bnoagg 5.385884e-02
## 133 bnoagg 2.371976e-02
## 134 bnoagg -4.276040e-03
## 135 bnoagg -2.271984e-01
## 136 bnoagg -2.227397e-01
## 137 bnoagg 1.049983e-01
## 138 bnoagg 4.047383e-02
## 139 bnoagg 3.531467e-02
## 140 bnoagg 1.463910e-01
## 141 bnoagg 4.570426e-02
## 142 bnoagg -1.034827e-01
## 143 bnoagg 4.371505e-02
## 144 bnoagg 7.550977e-03
## 145 bnoagg 8.945075e-02
## 146 bnoagg 1.267933e-01
## 147 bnoagg -1.431472e-02
## 148 bnoagg 1.587036e-01
## 149 bnoagg 1.281470e-01
## 150 bnoagg -1.687715e-01
## 151 bnoagg 9.181429e-02
## 152 bnoagg 5.361766e-03
## 153 bnoagg 7.580080e-03
## 154 bnoagg -7.145586e-02
## 155 bnoagg 1.068119e-01
## 156 bnoagg -8.648397e-02
## 157 bnoagg 2.791330e-01
## 158 bnoagg 1.643455e-01
## 159 bnoagg 7.453866e-02
## 160 bnoagg 3.439989e-01
## 161 bnoagg 1.811032e-01
## 162 bnoagg 1.755518e-01
## 163 bnoagg -1.748016e-01
## 164 bnoagg -4.677758e-02
## 165 bnoagg 4.352444e-02
## 166 bnoagg 6.977217e-03
## 167 bnoagg 2.728808e-01
## 168 bnoagg 1.999453e-01
## 169 bnoagg -1.096401e-01
## 170 bnoagg 1.613152e-01
## 171 bnoagg 1.725193e-01
## 172 bnoagg -8.711362e-02
## 173 bnoagg -9.433603e-04
## 174 bnoagg -4.994907e-02
## 175 bnoagg 1.048808e-01
## 176 bnoagg 3.498392e-01
## 177 bnoagg 1.144609e-01
## 178 bnoagg 3.344175e-01
## 179 bnoagg 2.521103e-01
## 180 bnoagg 1.304191e-01
## 181 bnoagg -1.171141e-01
## 182 bnoagg -3.978454e-02
## 183 bnoagg 1.390580e-01
## 184 bnoagg -1.573014e-01
## 185 bnoagg 6.675485e-02
## 186 bnoagg 2.398787e-01
## 187 bnoagg 9.676041e-02
## 188 bnoagg 8.022973e-02
## 189 bnoagg 1.307689e-01
## 190 bnoagg 1.123918e-01
## 191 bnoagg 4.050813e-02
## 192 bnoagg -5.299973e-02
## 193 bnoagg -6.273602e-02
## 194 bnoagg 1.723338e-02
## 195 bnoagg 7.691556e-02
## 196 bnoagg 1.597764e-01
## 197 bnoagg 2.759165e-01
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## 1863 bnoagg 1.241135e-01
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## 1865 bnoagg -1.441891e-01
## 1866 bnoagg 7.257498e-02
## 1867 bnoagg 8.160414e-04
## 1868 bnoagg -1.262667e-01
## 1869 bnoagg 9.768889e-02
## 1870 bnoagg 1.288234e-01
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## 1873 bnoagg -2.504878e-02
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## 1877 bnoagg 8.715080e-03
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## 1879 bnoagg -8.893328e-02
## 1880 bnoagg 1.683190e-01
## 1881 bnoagg 2.202410e-01
## 1882 bnoagg 4.323615e-02
## 1883 bnoagg 1.782394e-01
## 1884 bnoagg 2.148959e-01
## 1885 bnoagg -4.269898e-02
## 1886 bnoagg -4.553350e-02
## 1887 bnoagg -4.913289e-02
## 1888 bnoagg 2.263831e-01
## 1889 bnoagg 1.614423e-01
## 1890 bnoagg 2.132137e-01
## 1891 bnoagg -1.029998e-01
## 1892 bnoagg 1.820050e-01
## 1893 bnoagg 2.406396e-01
## 1894 bnoagg 6.231028e-02
## 1895 bnoagg 1.129515e-01
## 1896 bnoagg 2.015351e-01
## 1897 bnoagg 4.676479e-01
## 1898 bnoagg 4.074446e-01
## 1899 bnoagg 1.830841e-01
## 1900 bnoagg 3.080024e-02
## 1901 bnoagg -8.393200e-02
## 1902 bnoagg -9.827591e-02
## 1903 bnoagg 6.319233e-02
## 1904 bnoagg -8.896763e-02
## 1905 bnoagg -1.793864e-01
## 1906 bnoagg 1.648368e-01
## 1907 bnoagg -5.532759e-02
## 1908 bnoagg -1.176164e-01
## 1909 bnoagg 1.893018e-02
## 1910 bnoagg 1.571052e-01
## 1911 bnoagg -1.313122e-01
## 1912 bnoagg -2.719086e-02
## 1913 bnoagg -5.078544e-02
## 1914 bnoagg 1.210673e-01
## 1915 bnoagg -1.335106e-01
## 1916 bnoagg 6.154890e-02
## 1917 bnoagg 3.813664e-01
## 1918 bnoagg 1.733092e-01
## 1919 bnoagg 4.699220e-02
## 1920 bnoagg 3.593529e-02
## 1921 bnoagg 1.586590e-02
## 1922 bnoagg 1.878376e-01
## 1923 bnoagg 2.104222e-01
## 1924 bnoagg -1.038516e-01
## 1925 bnoagg 4.062005e-02
## 1926 bnoagg -1.334006e-01
## 1927 bnoagg -1.753962e-01
## 1928 bnoagg -8.883392e-02
## 1929 bnoagg -1.181269e-01
## 1930 bnoagg -2.559548e-02
## 1931 bnoagg -1.040972e-01
## 1932 bnoagg 2.891062e-01
## 1933 bnoagg 2.586641e-01
## 1934 bnoagg 1.276957e-01
## 1935 bnoagg 3.598059e-01
## 1936 bnoagg 2.268267e-02
## 1937 bnoagg -1.060682e-01
## 1938 bnoagg 3.764451e-02
## 1939 bnoagg 2.785354e-03
## 1940 bnoagg 1.050662e-01
## 1941 bnoagg 2.339869e-01
## 1942 bnoagg 7.991134e-02
## 1943 bnoagg -7.375075e-02
## 1944 bnoagg -5.510990e-02
## 1945 bnoagg 2.686776e-01
## 1946 bnoagg 7.557064e-02
## 1947 bnoagg 3.281026e-01
## 1948 bnoagg 9.262060e-02
## 1949 bnoagg 1.140634e-01
## 1950 bnoagg 2.113760e-02
## 1951 bnoagg 3.200180e-02
## 1952 bnoagg 9.014448e-03
## 1953 bnoagg -3.017514e-02
## 1954 bnoagg 1.290360e-01
## 1955 bnoagg 1.403462e-02
## 1956 bnoagg 1.404496e-01
## 1957 bnoagg -3.500741e-02
## 1958 bnoagg 4.200957e-02
## 1959 bnoagg 1.662267e-01
## 1960 bnoagg 9.344116e-02
## 1961 bnoagg 4.398995e-02
## 1962 bnoagg 6.926571e-02
## 1963 bnoagg 2.568226e-01
## 1964 bnoagg 3.381193e-02
## 1965 bnoagg 2.060988e-01
## 1966 bnoagg 1.856937e-01
## 1967 bnoagg 1.494994e-01
## 1968 bnoagg 3.419492e-02
## 1969 bnoagg -3.911789e-02
## 1970 bnoagg 2.522606e-01
## 1971 bnoagg 1.542316e-01
## 1972 bnoagg 4.411225e-02
## 1973 bnoagg 7.942091e-02
## 1974 bnoagg -3.057522e-02
## 1975 bnoagg 7.499705e-02
## 1976 bnoagg -1.279844e-01
## 1977 bnoagg 6.654288e-02
## 1978 bnoagg 2.144068e-01
## 1979 bnoagg -1.140253e-01
## 1980 bnoagg 2.993443e-02
## 1981 bnoagg 8.649973e-02
## 1982 bnoagg 8.763298e-02
## 1983 bnoagg -5.664019e-02
## 1984 bnoagg -8.277188e-02
## 1985 bnoagg 6.442298e-02
## 1986 bnoagg 9.077469e-02
## 1987 bnoagg 1.966861e-01
## 1988 bnoagg 1.269059e-01
## 1989 bnoagg -4.101661e-02
## 1990 bnoagg -7.559447e-02
## 1991 bnoagg 4.045685e-02
## 1992 bnoagg 2.970488e-01
## 1993 bnoagg -1.332848e-01
## 1994 bnoagg 6.286378e-02
## 1995 bnoagg 2.812603e-02
## 1996 bnoagg 9.978991e-02
## 1997 bnoagg 2.212766e-01
## 1998 bnoagg 1.585882e-01
## 1999 bnoagg 1.922070e-01
## 2000 bnoagg 2.235907e-02
## 2001 bnoagg 1.400042e-01
## 2002 bnoagg 1.436419e-01
## 2003 bnoagg -3.173543e-02
## 2004 bnoagg 1.593722e-01
## 2005 bnoagg -4.067766e-02
## 2006 bnoagg 8.233186e-02
## 2007 bnoagg 2.881152e-01
## 2008 bnoagg 9.166813e-02
## 2009 bnoagg -6.473448e-02
## 2010 bnoagg 1.269504e-01
## 2011 bnoagg 4.136613e-02
## 2012 bnoagg -1.150618e-01
## 2013 bnoagg 1.168925e-01
## 2014 bnoagg 1.427407e-01
## 2015 bnoagg 5.743796e-02
## 2016 bnoagg -1.067840e-01
## 2017 bnoagg -2.787091e-01
## 2018 bnoagg 1.256059e-01
## 2019 bnoagg 2.692387e-01
## 2020 bnoagg -2.207292e-01
## 2021 bnoagg 1.332384e-01
## 2022 bnoagg 2.200533e-03
## 2023 bnoagg 1.288018e-01
## 2024 bnoagg 3.779156e-03
## 2025 bnoagg 6.302824e-02
## 2026 bnoagg 1.146187e-01
## 2027 bnoagg -1.212280e-01
## 2028 bnoagg 1.830403e-01
## 2029 bnoagg 4.315768e-02
## 2030 bnoagg -5.175559e-02
## 2031 bnoagg 2.866307e-01
## 2032 bnoagg 2.083592e-01
## 2033 bnoagg -6.616682e-02
## 2034 bnoagg 1.553370e-01
## 2035 bnoagg -4.101054e-03
## 2036 bnoagg 4.769627e-02
## 2037 bnoagg 1.277079e-01
## 2038 bnoagg 9.373543e-02
## 2039 bnoagg 1.768849e-01
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## 1799 bnoagg -6.823976e-02
## 1800 bnoagg 3.675244e-02
## 1801 bnoagg 3.585813e-01
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## 1803 bnoagg 3.679156e-01
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## 1811 bnoagg 1.469984e-01
## 1812 bnoagg 2.588579e-02
## 1813 bnoagg -9.826148e-03
## 1814 bnoagg 1.310786e-01
## 1815 bnoagg 7.917672e-02
## 1816 bnoagg -5.769657e-02
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## 1824 bnoagg 4.369686e-02
## 1825 bnoagg 2.131635e-01
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## 1827 bnoagg -1.069350e-03
## 1828 bnoagg -3.187062e-02
## 1829 bnoagg 1.961960e-01
## 1830 bnoagg 5.197328e-02
## 1831 bnoagg 2.457304e-01
## 1832 bnoagg 1.516521e-01
## 1833 bnoagg 1.912820e-01
## 1834 bnoagg 1.189852e-02
## 1835 bnoagg 1.809798e-01
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## 1837 bnoagg -3.274028e-02
## 1838 bnoagg 2.571902e-03
## 1839 bnoagg -3.027765e-02
## 1840 bnoagg 3.665799e-03
## 1841 bnoagg -1.270219e-01
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## 1843 bnoagg 1.448026e-01
## 1844 bnoagg 4.778922e-02
## 1845 bnoagg 1.899872e-01
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## 1847 bnoagg -1.565746e-01
## 1848 bnoagg 1.287250e-01
## 1849 bnoagg 8.051571e-02
## 1850 bnoagg -4.898812e-02
## 1851 bnoagg -2.064217e-04
## 1852 bnoagg 9.708354e-02
## 1853 bnoagg -7.453853e-03
## 1854 bnoagg 1.246570e-01
## 1855 bnoagg 2.271509e-01
## 1856 bnoagg 1.575063e-01
## 1857 bnoagg 1.134200e-01
## 1858 bnoagg 1.431888e-01
## 1859 bnoagg 4.308406e-02
## 1860 bnoagg 1.750170e-01
## 1861 bnoagg 4.754076e-02
## 1862 bnoagg 2.778923e-01
## 1863 bnoagg 1.241135e-01
## 1864 bnoagg -3.223875e-02
## 1865 bnoagg -1.441891e-01
## 1866 bnoagg 7.257498e-02
## 1867 bnoagg 8.160414e-04
## 1868 bnoagg -1.262667e-01
## 1869 bnoagg 9.768889e-02
## 1870 bnoagg 1.288234e-01
## 1871 bnoagg 4.101829e-01
## 1872 bnoagg -7.601609e-02
## 1873 bnoagg -2.504878e-02
## 1874 bnoagg 2.351020e-01
## 1875 bnoagg -8.131048e-03
## 1876 bnoagg 1.990800e-02
## 1877 bnoagg 8.715080e-03
## 1878 bnoagg 1.733658e-01
## 1879 bnoagg -8.893328e-02
## 1880 bnoagg 1.683190e-01
## 1881 bnoagg 2.202410e-01
## 1882 bnoagg 4.323615e-02
## 1883 bnoagg 1.782394e-01
## 1884 bnoagg 2.148959e-01
## 1885 bnoagg -4.269898e-02
## 1886 bnoagg -4.553350e-02
## 1887 bnoagg -4.913289e-02
## 1888 bnoagg 2.263831e-01
## 1889 bnoagg 1.614423e-01
## 1890 bnoagg 2.132137e-01
## 1891 bnoagg -1.029998e-01
## 1892 bnoagg 1.820050e-01
## 1893 bnoagg 2.406396e-01
## 1894 bnoagg 6.231028e-02
## 1895 bnoagg 1.129515e-01
## 1896 bnoagg 2.015351e-01
## 1897 bnoagg 4.676479e-01
## 1898 bnoagg 4.074446e-01
## 1899 bnoagg 1.830841e-01
## 1900 bnoagg 3.080024e-02
## 1901 bnoagg -8.393200e-02
## 1902 bnoagg -9.827591e-02
## 1903 bnoagg 6.319233e-02
## 1904 bnoagg -8.896763e-02
## 1905 bnoagg -1.793864e-01
## 1906 bnoagg 1.648368e-01
## 1907 bnoagg -5.532759e-02
## 1908 bnoagg -1.176164e-01
## 1909 bnoagg 1.893018e-02
## 1910 bnoagg 1.571052e-01
## 1911 bnoagg -1.313122e-01
## 1912 bnoagg -2.719086e-02
## 1913 bnoagg -5.078544e-02
## 1914 bnoagg 1.210673e-01
## 1915 bnoagg -1.335106e-01
## 1916 bnoagg 6.154890e-02
## 1917 bnoagg 3.813664e-01
## 1918 bnoagg 1.733092e-01
## 1919 bnoagg 4.699220e-02
## 1920 bnoagg 3.593529e-02
## 1921 bnoagg 1.586590e-02
## 1922 bnoagg 1.878376e-01
## 1923 bnoagg 2.104222e-01
## 1924 bnoagg -1.038516e-01
## 1925 bnoagg 4.062005e-02
## 1926 bnoagg -1.334006e-01
## 1927 bnoagg -1.753962e-01
## 1928 bnoagg -8.883392e-02
## 1929 bnoagg -1.181269e-01
## 1930 bnoagg -2.559548e-02
## 1931 bnoagg -1.040972e-01
## 1932 bnoagg 2.891062e-01
## 1933 bnoagg 2.586641e-01
## 1934 bnoagg 1.276957e-01
## 1935 bnoagg 3.598059e-01
## 1936 bnoagg 2.268267e-02
## 1937 bnoagg -1.060682e-01
## 1938 bnoagg 3.764451e-02
## 1939 bnoagg 2.785354e-03
## 1940 bnoagg 1.050662e-01
## 1941 bnoagg 2.339869e-01
## 1942 bnoagg 7.991134e-02
## 1943 bnoagg -7.375075e-02
## 1944 bnoagg -5.510990e-02
## 1945 bnoagg 2.686776e-01
## 1946 bnoagg 7.557064e-02
## 1947 bnoagg 3.281026e-01
## 1948 bnoagg 9.262060e-02
## 1949 bnoagg 1.140634e-01
## 1950 bnoagg 2.113760e-02
## 1951 bnoagg 3.200180e-02
## 1952 bnoagg 9.014448e-03
## 1953 bnoagg -3.017514e-02
## 1954 bnoagg 1.290360e-01
## 1955 bnoagg 1.403462e-02
## 1956 bnoagg 1.404496e-01
## 1957 bnoagg -3.500741e-02
## 1958 bnoagg 4.200957e-02
## 1959 bnoagg 1.662267e-01
## 1960 bnoagg 9.344116e-02
## 1961 bnoagg 4.398995e-02
## 1962 bnoagg 6.926571e-02
## 1963 bnoagg 2.568226e-01
## 1964 bnoagg 3.381193e-02
## 1965 bnoagg 2.060988e-01
## 1966 bnoagg 1.856937e-01
## 1967 bnoagg 1.494994e-01
## 1968 bnoagg 3.419492e-02
## 1969 bnoagg -3.911789e-02
## 1970 bnoagg 2.522606e-01
## 1971 bnoagg 1.542316e-01
## 1972 bnoagg 4.411225e-02
## 1973 bnoagg 7.942091e-02
## 1974 bnoagg -3.057522e-02
## 1975 bnoagg 7.499705e-02
## 1976 bnoagg -1.279844e-01
## 1977 bnoagg 6.654288e-02
## 1978 bnoagg 2.144068e-01
## 1979 bnoagg -1.140253e-01
## 1980 bnoagg 2.993443e-02
## 1981 bnoagg 8.649973e-02
## 1982 bnoagg 8.763298e-02
## 1983 bnoagg -5.664019e-02
## 1984 bnoagg -8.277188e-02
## 1985 bnoagg 6.442298e-02
## 1986 bnoagg 9.077469e-02
## 1987 bnoagg 1.966861e-01
## 1988 bnoagg 1.269059e-01
## 1989 bnoagg -4.101661e-02
## 1990 bnoagg -7.559447e-02
## 1991 bnoagg 4.045685e-02
## 1992 bnoagg 2.970488e-01
## 1993 bnoagg -1.332848e-01
## 1994 bnoagg 6.286378e-02
## 1995 bnoagg 2.812603e-02
## 1996 bnoagg 9.978991e-02
## 1997 bnoagg 2.212766e-01
## 1998 bnoagg 1.585882e-01
## 1999 bnoagg 1.922070e-01
## 2000 bnoagg 2.235907e-02
## 2001 bnoagg 1.400042e-01
## 2002 bnoagg 1.436419e-01
## 2003 bnoagg -3.173543e-02
## 2004 bnoagg 1.593722e-01
## 2005 bnoagg -4.067766e-02
## 2006 bnoagg 8.233186e-02
## 2007 bnoagg 2.881152e-01
## 2008 bnoagg 9.166813e-02
## 2009 bnoagg -6.473448e-02
## 2010 bnoagg 1.269504e-01
## 2011 bnoagg 4.136613e-02
## 2012 bnoagg -1.150618e-01
## 2013 bnoagg 1.168925e-01
## 2014 bnoagg 1.427407e-01
## 2015 bnoagg 5.743796e-02
## 2016 bnoagg -1.067840e-01
## 2017 bnoagg -2.787091e-01
## 2018 bnoagg 1.256059e-01
## 2019 bnoagg 2.692387e-01
## 2020 bnoagg -2.207292e-01
## 2021 bnoagg 1.332384e-01
## 2022 bnoagg 2.200533e-03
## 2023 bnoagg 1.288018e-01
## 2024 bnoagg 3.779156e-03
## 2025 bnoagg 6.302824e-02
## 2026 bnoagg 1.146187e-01
## 2027 bnoagg -1.212280e-01
## 2028 bnoagg 1.830403e-01
## 2029 bnoagg 4.315768e-02
## 2030 bnoagg -5.175559e-02
## 2031 bnoagg 2.866307e-01
## 2032 bnoagg 2.083592e-01
## 2033 bnoagg -6.616682e-02
## 2034 bnoagg 1.553370e-01
## 2035 bnoagg -4.101054e-03
## 2036 bnoagg 4.769627e-02
## 2037 bnoagg 1.277079e-01
## 2038 bnoagg 9.373543e-02
## 2039 bnoagg 1.768849e-01
## 2040 bnoagg 1.681504e-02
## 2041 bnoagg -4.069801e-02
## 2042 bnoagg -5.199544e-02
## 2043 bnoagg 1.217835e-01
## 2044 bnoagg 8.873313e-02
## 2045 bnoagg 2.222411e-01
## 2046 bnoagg 1.249895e-01
## 2047 bnoagg 1.131684e-01
## 2048 bnoagg 1.601520e-01
## 2049 bnoagg -8.583849e-02
## 2050 bnoagg 3.630843e-01
## 2051 bnoagg -5.586853e-03
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## 2053 bnoagg 4.032510e-02
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## 2055 bnoagg 2.147032e-01
## 2056 bnoagg 3.153749e-01
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## 2058 bnoagg -1.427225e-01
## 2059 bnoagg -6.552646e-02
## 2060 bnoagg -1.328815e-01
## 2061 bnoagg 9.990204e-02
## 2062 bnoagg 1.324795e-01
## 2063 bnoagg -1.954243e-01
## 2064 bnoagg 1.865877e-01
## 2065 bnoagg 2.095225e-01
## 2066 bnoagg -2.975270e-01
## 2067 bnoagg -1.516464e-01
## 2068 bnoagg 1.998760e-01
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## 2071 bnoagg -2.009928e-02
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## 2076 bnoagg 3.507477e-03
## 2077 bnoagg 1.943071e-01
## 2078 bnoagg 1.709543e-01
## 2079 bnoagg -3.542896e-02
## 2080 bnoagg -9.021163e-03
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## 2090 bnoagg 2.221138e-01
## 2091 bnoagg 1.434934e-01
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## 2097 bnoagg 3.531492e-01
## 2098 bnoagg -1.543383e-01
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## 2100 bnoagg 7.373560e-03
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## 7728 bmean 4.256938e-01
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## 7743 bmean 4.424436e-01
## 7744 bmean 4.134058e-01
## 7745 bmean 1.253230e-01
## 7746 bmean 1.411598e-01
## 7747 bmean 2.930617e-01
## 7748 bmean 2.691647e-01
## 7749 bmean 2.235776e-01
## 7750 bmean 4.315247e-01
## 7751 bmean 2.731600e-01
## 7752 bmean 3.192241e-01
## 7753 bmean 5.283786e-01
## 7754 bmean 5.370651e-02
## 7755 bmean 9.472999e-02
## 7756 bmean 2.472056e-01
## 7757 bmean 2.789840e-01
## 7758 bmean 9.685883e-03
## 7759 bmean -5.037767e-02
## 7760 bmean 2.132478e-01
## 7761 bmean 2.213455e-01
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## 7763 bmean 2.433003e-01
## 7764 bmean 4.090260e-02
## 7765 bmean 1.882201e-01
## 7766 bmean 2.066776e-01
## 7767 bmean 2.587877e-01
## 7768 bmean 3.873553e-01
## 7769 bmean 3.859704e-01
## 7770 bmean 9.722050e-02
## 7771 bmean 3.007710e-02
## 7772 bmean 2.938316e-01
## 7773 bmean 1.044635e-01
## 7774 bmean 2.678118e-01
## 7775 bmean -1.195005e-01
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## 7782 bmean 3.259437e-01
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## 7784 bmean -1.136256e-01
## 7785 bmean 4.552216e-01
## 7786 bmean 2.740001e-01
## 7787 bmean 2.148171e-01
## 7788 bmean 2.133018e-01
## 7789 bmean 7.069712e-02
## 7790 bmean 3.783840e-01
## 7791 bmean 1.555555e-01
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## 7817 bmean 1.601778e-01
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## 7820 bmean 2.036451e-01
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## 7886 bmean -6.173709e-03
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## 7969 bmean 1.934437e-01
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## 7971 bmean 2.110306e-01
## 7972 bmean 1.608727e-01
## 7973 bmean 3.410170e-01
## 7974 bmean -2.309307e-02
## 7975 bmean 1.496892e-01
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## 7980 bmean 5.046579e-01
## 7981 bmean 1.713035e-01
## 7982 bmean 9.127985e-02
## 7983 bmean 1.579599e-01
## 7984 bmean 3.998920e-01
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## 7986 bmean 1.057272e-03
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## 7988 bmean 1.348185e-01
## 7989 bmean 1.536386e-01
## 7990 bmean 3.138719e-01
## 7991 bmean 2.654866e-01
## 7992 bmean 2.440315e-01
## 7993 bmean 1.494629e-01
## 7994 bmean 2.764597e-01
## 7995 bmean 7.785096e-02
## 7996 bmean 3.932386e-01
## 7997 bmean -3.476736e-02
## 7998 bmean 1.781756e-01
## 7999 bmean 3.929655e-01
## 8000 bmean 2.664662e-01
First Trial Accuracy
It is possible that there is a learning effect, or our choice of mark type led participants to focus on a particular stimulus over another. As a result, we analyze accuracy by aggregation strategy and generalization class for the first trial of each particiapnt to better understand the extent of this effect. We find that there appears to be a small difference between the disaggregated+mean and other conditions, although results are not reliably different.
df_50[df_50$trial==1,] %>%
ddply(c("aggStrat", "insightClass"), summarise,
Correct=sum(correct==TRUE),
Incorrect=sum(correct==FALSE),
Accuracy=Correct/(Incorrect+Correct),
Total=Incorrect+Correct)## aggStrat insightClass Correct Incorrect Accuracy Total
## 1 disagg correlation 2 2 0.5000000 4
## 2 disagg mean 1 1 0.5000000 2
## 3 disagg rank 1 8 0.1111111 9
## 4 disagg shape 17 13 0.5666667 30
## 5 disagg+mean correlation 3 0 1.0000000 3
## 6 disagg+mean mean 4 7 0.3636364 11
## 7 disagg+mean rank 1 0 1.0000000 1
## 8 disagg+mean shape 12 12 0.5000000 24
## 9 mean correlation 1 2 0.3333333 3
## 10 mean mean 5 11 0.3125000 16
## 11 mean rank 0 8 0.0000000 8
## 12 mean shape 3 1 0.7500000 4
# Gives us 80 observations, since 10 participants didn't make observations on tthe first trial
df_50_firstTrial_facetWorker_stats <- df_50[df_50$trial==1,] %>%
ddply(.(aggStrat, workerId), summarise,
Correct=sum(correct==TRUE),
Incorrect=sum(correct==FALSE),
PercCorrect=Correct/(Incorrect+Correct),
Total=Incorrect+Correct) %>%
ddply(~aggStrat, summarise,
N = sum((Total)),
meanAcc = mean(PercCorrect),
sd = sd(PercCorrect),
se = sd / sqrt(N))
df_50_firstTrial_facetWorker_stats## aggStrat N meanAcc sd se
## 1 disagg 45 0.4848485 0.4590215 0.06842689
## 2 disagg+mean 39 0.5595238 0.4586038 0.07343538
## 3 mean 31 0.2800000 0.4349329 0.07811626
Confidence
A summary of reported confidence from participants. We first investigate general summary statistics for confidence taking into account individual differences between participants. We find that between aggregation strategies, there are small differences in total mean confidence. Compared to E1, we find that the avg confidence for the disaggregation+mean condition increases slightly - disaggregation - 68%, disaggregation+mean - 75%, mean - 74%. Compare to E1: disaggregation - 67%, disaggregation+mean - 69%, mean - 72%.
Confidence by Participant - Aggregation Strategy combination
df_50 %>%
ddply(.(aggStrat, workerId), summarise, ## to get confidence per participant
N = sum(correct==TRUE, na.rm=TRUE) + sum(correct==FALSE, na.rm=TRUE),
correct = sum(correct==TRUE),
meanConf = mean(confidence, na.rm=TRUE),
sd = sd(confidence),
se = sd / sqrt(N)) %>%
ddply(~aggStrat, summarise, ## then to average the average confidence per participant
Total = sum(N),
correct = sum(correct, na.rm=TRUE),
meanTotalConf = mean(meanConf, na.rm=TRUE),
sdTotal = sd(meanConf),
seTotal = sdTotal / sqrt(Total))## aggStrat Total correct meanTotalConf sdTotal seTotal
## 1 disagg 550 277 67.91896 16.71986 0.7129372
## 2 disagg+mean 558 303 74.69777 14.77752 0.6255824
## 3 mean 500 249 73.74947 17.53188 0.7840493## Warning: Removed 2 rows containing missing values (geom_errorbar).
Confidence Bayesian Model
We examine how aggregation strategy impacts confidence by again running a Bayesian hierarchical model. We find that there is an effect of the mean aggregation condition on confidence compared to the disaggregated and disaggregated with means condition.
## 105 vector or matrix parameters omitted in display. Use depth=2 to show them.## Mean StdDev lower 0.95 upper 0.95 n_eff Rhat
## bnoagg -6.0551346 1.385323 -8.858803 -3.445955 1993.206 1.001268
## bmean 0.8267635 1.341236 -1.947706 3.263343 1995.880 1.001123## 105 vector or matrix parameters omitted in display. Use depth=2 to show them.## item value
## 1 bnoagg -5.453509e+00
## 2 bnoagg -5.668354e+00
## 3 bnoagg -5.441975e+00
## 4 bnoagg -5.229288e+00
## 5 bnoagg -6.520777e+00
## 6 bnoagg -7.256641e+00
## 7 bnoagg -6.856671e+00
## 8 bnoagg -5.613781e+00
## 9 bnoagg -8.000266e+00
## 10 bnoagg -5.645590e+00
## 11 bnoagg -6.536677e+00
## 12 bnoagg -6.384889e+00
## 13 bnoagg -6.288214e+00
## 14 bnoagg -4.945556e+00
## 15 bnoagg -5.304813e+00
## 16 bnoagg -8.091874e+00
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One potential confound for the effect that we observe in confidence is the intial confidence slider value each participant saw when they were asked to record their confidence. For each trial, we randomized the initial confidence slider position, as giving a default value could influence the confidence a participant gave. However, it is possible that sampling caused a higher rate of a range of values for a given aggregation condition, which could potentially confound any results we found in the confidence effect. We investigate the effects of this potential confound by plotting the distribution of initial confidence slider positions. We select a bin size of 4, resulting in 25 bins to see the data in a somewhat high resolution. Given the histograms of the distribution for each aggregation strategy, we see that though there is variance, generally initial slider value is the same.
df_50 %>%
ddply(.(aggStrat), summarise,
total = sum(confidence!=-1),
meanInitialSliderValue = mean(initSliderValue, na.rm=TRUE),
sd = sd(initSliderValue),
se = sd / sqrt(total))## aggStrat total meanInitialSliderValue sd se
## 1 disagg 550 49.47818 29.97526 1.278149
## 2 disagg+mean 558 50.89606 28.95424 1.225731
## 3 mean 500 49.34400 28.63028 1.280385
Accuracy-Confidence Relationship
We perform an exploratory analysis to investigate the relationship between accuracy, confidence and aggregation strategy. We plot the the average confidence of each worker per aggregation strategy to get a better understanding of the distribution of confidence.
## aggStrat Correct Incorrect Accuracy Total
## 1 disagg 72 74 0.4931507 146
## 2 disagg+mean 107 79 0.5752688 186
## 3 mean 101 83 0.5489130 184## aggStrat Correct Incorrect Accuracy Total
## 1 disagg 138 130 0.5149254 268
## 2 disagg+mean 176 141 0.5552050 317
## 3 mean 154 128 0.5460993 282## aggStrat Correct Incorrect Accuracy Total
## 1 disagg 179 163 0.5233918 342
## 2 disagg+mean 227 167 0.5761421 394
## 3 mean 182 161 0.5306122 343## aggStrat Correct Incorrect Accuracy Total
## 1 disagg 202 192 0.5126904 394
## 2 disagg+mean 251 193 0.5653153 444
## 3 mean 200 183 0.5221932 383## aggStrat Correct Incorrect Accuracy Total
## 1 disagg 219 210 0.5104895 429
## 2 disagg+mean 267 216 0.5527950 483
## 3 mean 216 207 0.5106383 423Because there may be individual differences between how participants use confidence, we first find the average point biserial correlation between confidence and accuracy for each participant-aggregation strategy pair. Then we average these correlations across participants for each aggregation strategy. We interpret when a worker gets all of their observations correct (biserial.cor gives NaN) as 0 correlation. We find that the average correlation between accuracy and confidence across aggregation strategies are not reliably different.
ddply(df_50_worker_CorConf[df_50_worker_CorConf$biserial!=0,], ~aggStrat, summarize,
avgConf=mean(conf),
avgBiserial=mean(biserial),
sdConf=sd(conf),
sdCorr=sd(biserial),
seConf=sdConf/sqrt(length(aggStrat)),
seCorr=sdCorr/sqrt(length(aggStrat)))## aggStrat avgConf avgBiserial sdConf sdCorr seConf seCorr
## 1 mean 73.33731 -0.110984163 17.40377 0.4894502 2.051054 0.05768226
## 2 disagg 67.37752 0.037651088 16.72993 0.5163066 1.870463 0.05772484
## 3 disagg+mean 75.00932 0.003018117 14.82299 0.4516647 1.700313 0.05180949
First Trial Confidence
As per our pre-registration, we analyze first trial confidence before participants are aware they will be asked for confidence, in order to to compare confidence between aggregation strategy. We find that on the first trial, aggregation as a mean mark is slightly less accurate and less confident in their generalizations. We plot these results for aid.
Effect Magnitude (EM) and Quantitative Prediction (QP) Summary
Descriptive Statistics
Now let’s look at the two new codes for effect magnitude estimate and quantitative predictions. There are 103 EM generalizations and 783 QP generalizations. There is a greater amount of EM generalizations for disaggregated aggregation conditions, although the result is not significant. Looking at distributions of both, we find that there are more QP generalizations for the mean and shape generalization classes. Most of these are likely because shape class generalizations included those where the participant noted the shape of a distribution or the size of a bin (i.e. “Ages range from 18 to 68.” or “The majority of purchases are between 75 and 175 dollars.”)
df_50 %>%
ddply(~aggStrat, summarise,
em = sum(effectSizeMagnitude==TRUE, na.rm=TRUE),
qp = sum(quantitativePrediction==TRUE, na.rm=TRUE),
em_null = sum(effectSizeMagnitude==TRUE & effectSizeMagnitudeRemoveNulls==FALSE, na.rm=TRUE),
total = sum(effectSizeMagnitude==TRUE | effectSizeMagnitude==FALSE, na.rm=TRUE),
PercES = em/total,
PercQP = qp/total,
PercES_null = em_null/total,
PercES_null_ofEM = em_null/em)## aggStrat em qp em_null total PercES PercQP PercES_null
## 1 disagg 35 269 22 550 0.06363636 0.4890909 0.04000000
## 2 disagg+mean 43 270 23 558 0.07706093 0.4838710 0.04121864
## 3 mean 27 238 21 500 0.05400000 0.4760000 0.04200000
## PercES_null_ofEM
## 1 0.6285714
## 2 0.5348837
## 3 0.7777778df_50 %>%
ddply(~aggStrat, summarise,
em = sum(effectSizeMagnitude==TRUE, na.rm=TRUE),
qp = sum(quantitativePrediction==TRUE, na.rm=TRUE),
em_null = sum(effectSizeMagnitude==TRUE & effectSizeMagnitudeRemoveNulls==FALSE, na.rm=TRUE),
total = sum(effectSizeMagnitude==TRUE | effectSizeMagnitude==FALSE, na.rm=TRUE),
PercES = em/total,
PercQP = qp/total,
PercES_null = em_null/total,
PercES_null_ofEM = em_null/em)## aggStrat em qp em_null total PercES PercQP PercES_null
## 1 disagg 35 269 22 550 0.06363636 0.4890909 0.04000000
## 2 disagg+mean 43 270 23 558 0.07706093 0.4838710 0.04121864
## 3 mean 27 238 21 500 0.05400000 0.4760000 0.04200000
## PercES_null_ofEM
## 1 0.6285714
## 2 0.5348837
## 3 0.7777778## Using aggStrat as id variables
Per our preregistration, we run models for both QP and EM to see if there is any effect between aggregation condition and either effect. We see a slight effect in the effect magnitude estimates model, where the disagg condition in particular appears to result in less effect magnitude estimates, however the 95% CI slightly crosses 0 so this effect is not entirely reliable. Similarly, we find no reliable difference in aggregation condition for predicting the quantitative prediction code.
Quantitative Predictions Bayesian Model
## Mean StdDev lower 0.95 upper 0.95 n_eff Rhat
## bnoagg -0.2224533 0.1994546 -0.6178543 0.17540596 5170.437 1.000085
## bmean -0.3345958 0.1965517 -0.7285063 0.04769121 5214.198 0.999750## 105 vector or matrix parameters omitted in display. Use depth=2 to show them.## Mean StdDev lower 0.95 upper 0.95 n_eff Rhat
## bnoagg 0.8005524 1.220737 0.5390999 1.191730 Inf 2.718512
## bmean 0.7156273 1.217198 0.4826294 1.048847 Inf 2.717602## 105 vector or matrix parameters omitted in display. Use depth=2 to show them.## item value
## 1 bnoagg -4.434886e-01
## 2 bnoagg -3.451352e-01
## 3 bnoagg -2.115660e-01
## 4 bnoagg 7.036484e-02
## 5 bnoagg -4.185184e-01
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## 1893 bnoagg 6.339522e-02
## 1894 bnoagg 1.523923e-02
## 1895 bnoagg -1.258078e-01
## 1896 bnoagg 2.497963e-01
## 1897 bnoagg -1.960277e-01
## 1898 bnoagg -2.731839e-01
## 1899 bnoagg -1.875293e-01
## 1900 bnoagg -4.449820e-01
## 1901 bnoagg -4.703343e-02
## 1902 bnoagg -2.530680e-01
## 1903 bnoagg -3.027222e-01
## 1904 bnoagg -1.667640e-01
## 1905 bnoagg 2.631346e-01
## 1906 bnoagg -4.881670e-02
## 1907 bnoagg -1.766059e-01
## 1908 bnoagg -3.010600e-01
## 1909 bnoagg -1.003381e-01
## 1910 bnoagg -1.432799e-01
## 1911 bnoagg -3.596258e-01
## 1912 bnoagg -3.269649e-01
## 1913 bnoagg 6.155088e-02
## 1914 bnoagg -4.036278e-01
## 1915 bnoagg -2.646800e-01
## 1916 bnoagg -8.474906e-01
## 1917 bnoagg -3.660078e-01
## 1918 bnoagg 1.078934e-02
## 1919 bnoagg 8.656680e-02
## 1920 bnoagg -7.219323e-02
## 1921 bnoagg 1.750917e-01
## 1922 bnoagg -2.401194e-01
## 1923 bnoagg -7.272306e-02
## 1924 bnoagg -3.425450e-01
## 1925 bnoagg -9.342560e-02
## 1926 bnoagg -2.434023e-01
## 1927 bnoagg -3.355152e-01
## 1928 bnoagg -1.079290e-01
## 1929 bnoagg -4.573387e-01
## 1930 bnoagg -2.463892e-02
## 1931 bnoagg -6.307346e-02
## 1932 bnoagg -2.572792e-01
## 1933 bnoagg -7.733351e-02
## 1934 bnoagg 1.593286e-01
## 1935 bnoagg -3.394260e-01
## 1936 bnoagg -4.152239e-01
## 1937 bnoagg -3.779163e-02
## 1938 bnoagg -1.334922e-01
## 1939 bnoagg -7.387683e-01
## 1940 bnoagg -4.392279e-02
## 1941 bnoagg -5.561072e-01
## 1942 bnoagg -2.208691e-01
## 1943 bnoagg -3.153732e-01
## 1944 bnoagg -1.090982e-01
## 1945 bnoagg 3.062117e-02
## 1946 bnoagg -3.344553e-01
## 1947 bnoagg -3.330072e-01
## 1948 bnoagg -1.919374e-01
## 1949 bnoagg 1.640211e-03
## 1950 bnoagg -4.119613e-01
## 1951 bnoagg -4.172554e-02
## 1952 bnoagg -4.698441e-02
## 1953 bnoagg -8.579373e-02
## 1954 bnoagg -2.046893e-01
## 1955 bnoagg -2.857886e-01
## 1956 bnoagg -1.879148e-01
## 1957 bnoagg -1.867224e-01
## 1958 bnoagg -1.005787e-01
## 1959 bnoagg -3.114621e-01
## 1960 bnoagg -2.017515e-01
## 1961 bnoagg -3.739075e-01
## 1962 bnoagg -1.428567e-01
## 1963 bnoagg -1.056768e-01
## 1964 bnoagg 1.754060e-01
## 1965 bnoagg -2.754410e-01
## 1966 bnoagg -6.566614e-02
## 1967 bnoagg -4.763649e-01
## 1968 bnoagg -4.031839e-01
## 1969 bnoagg -2.600476e-01
## 1970 bnoagg -4.138840e-01
## 1971 bnoagg -6.477775e-01
## 1972 bnoagg -1.931703e-01
## 1973 bnoagg 1.474416e-01
## 1974 bnoagg -2.122369e-01
## 1975 bnoagg -3.481024e-01
## 1976 bnoagg -1.183622e-01
## 1977 bnoagg -3.288501e-01
## 1978 bnoagg -1.011777e-01
## 1979 bnoagg -1.781417e-01
## 1980 bnoagg -2.220935e-01
## 1981 bnoagg -1.749525e-01
## 1982 bnoagg 1.916474e-02
## 1983 bnoagg -1.019029e-01
## 1984 bnoagg -4.195317e-01
## 1985 bnoagg -5.737503e-01
## 1986 bnoagg 9.263389e-02
## 1987 bnoagg -1.072583e-01
## 1988 bnoagg -1.035629e-01
## 1989 bnoagg -1.387957e-01
## 1990 bnoagg -1.473451e-01
## 1991 bnoagg -4.874768e-01
## 1992 bnoagg -2.519952e-01
## 1993 bnoagg -4.146334e-01
## 1994 bnoagg -4.275217e-01
## 1995 bnoagg -4.915930e-01
## 1996 bnoagg -2.208566e-01
## 1997 bnoagg -2.676094e-01
## 1998 bnoagg -1.093788e-01
## 1999 bnoagg -3.766793e-01
## 2000 bnoagg -4.048415e-02
## 2001 bnoagg -6.635400e-02
## 2002 bnoagg -2.544163e-01
## 2003 bnoagg -2.079321e-01
## 2004 bnoagg -2.791521e-01
## 2005 bnoagg -1.988625e-01
## 2006 bnoagg -1.440563e-01
## 2007 bnoagg -1.715734e-01
## 2008 bnoagg 6.811006e-02
## 2009 bnoagg -5.003145e-01
## 2010 bnoagg 7.168005e-03
## 2011 bnoagg -1.360841e-01
## 2012 bnoagg -5.054055e-01
## 2013 bnoagg 1.903195e-01
## 2014 bnoagg -6.074758e-02
## 2015 bnoagg -2.386117e-01
## 2016 bnoagg -9.117818e-02
## 2017 bnoagg -2.351999e-01
## 2018 bnoagg -1.623171e-01
## 2019 bnoagg -3.004712e-01
## 2020 bnoagg -3.040256e-01
## 2021 bnoagg 5.372661e-02
## 2022 bnoagg 6.895986e-02
## 2023 bnoagg -1.014723e-01
## 2024 bnoagg -4.104055e-01
## 2025 bnoagg -4.727317e-01
## 2026 bnoagg 1.478404e-02
## 2027 bnoagg -5.621173e-01
## 2028 bnoagg -2.192235e-01
## 2029 bnoagg -2.642875e-01
## 2030 bnoagg -3.372120e-01
## 2031 bnoagg -5.775366e-01
## 2032 bnoagg -2.922736e-01
## 2033 bnoagg -1.454981e-01
## 2034 bnoagg 5.980220e-02
## 2035 bnoagg -4.787299e-01
## 2036 bnoagg -4.294118e-01
## 2037 bnoagg 8.375774e-02
## 2038 bnoagg -1.214971e-01
## 2039 bnoagg -2.285499e-01
## 2040 bnoagg 2.660288e-01
## 2041 bnoagg -5.901215e-03
## 2042 bnoagg -4.742423e-01
## 2043 bnoagg -2.383519e-01
## 2044 bnoagg -1.019321e-01
## 2045 bnoagg -1.862389e-01
## 2046 bnoagg -4.492981e-01
## 2047 bnoagg -9.687148e-02
## 2048 bnoagg -4.140595e-01
## 2049 bnoagg -2.496931e-01
## 2050 bnoagg -4.233462e-01
## 2051 bnoagg -3.170309e-03
## 2052 bnoagg -4.414173e-01
## 2053 bnoagg -3.687358e-01
## 2054 bnoagg 1.480843e-01
## 2055 bnoagg -4.159814e-01
## 2056 bnoagg -3.176250e-01
## 2057 bnoagg -1.824660e-01
## 2058 bnoagg -3.760878e-01
## 2059 bnoagg -2.385898e-01
## 2060 bnoagg 1.763930e-01
## 2061 bnoagg 1.997958e-02
## 2062 bnoagg -3.035908e-01
## 2063 bnoagg -1.495563e-02
## 2064 bnoagg -1.316087e-01
## 2065 bnoagg 1.462940e-01
## 2066 bnoagg -3.689471e-01
## 2067 bnoagg 1.867016e-01
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## 2069 bnoagg -2.812809e-01
## 2070 bnoagg -2.825536e-01
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## 2072 bnoagg 6.047388e-02
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## 2075 bnoagg -5.347568e-01
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## 2077 bnoagg -1.635596e-01
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## 2080 bnoagg 1.487381e-01
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## 2084 bnoagg -1.354535e-01
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## 2089 bnoagg -3.338917e-01
## 2090 bnoagg -2.225150e-01
## 2091 bnoagg -6.944806e-03
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## 2093 bnoagg -2.792672e-01
## 2094 bnoagg -4.461448e-01
## 2095 bnoagg 1.234753e-01
## 2096 bnoagg -2.288722e-01
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Effect Magnitude Bayesian Model
## Mean StdDev lower 0.95 upper 0.95 n_eff Rhat
## bnoagg 0.4450233 0.3189393 -0.17730272 1.072275 3774.804 0.9998070
## bmean 0.6692616 0.3117747 0.07903347 1.289099 3782.946 0.9997586## Mean StdDev lower 0.95 upper 0.95 n_eff Rhat
## bnoagg 1.560527 1.375668 0.8375262 2.922020 Inf 2.717757
## bmean 1.952795 1.365847 1.0822405 3.629516 Inf 2.717626## a_worker[1] a_worker[2] a_worker[3] a_worker[4] a_worker[5] a_worker[6]
## 0.49044897 0.38521313 0.71974260 3.08003192 0.77264715 0.65543646
## a_worker[7] a_worker[8] a_worker[9] a_worker[10] a_worker[11] a_worker[12]
## 0.81197169 0.98653589 1.15319295 0.32468233 1.98863481 0.78434105
## a_worker[13] a_worker[14] a_worker[15] a_worker[16] a_worker[17] a_worker[18]
## 0.73618473 0.84298030 0.37401093 0.51517908 1.94744024 7.04511636
## a_worker[19] a_worker[20] a_worker[21] a_worker[22] a_worker[23] a_worker[24]
## 0.36674008 0.72929760 2.16728285 3.58301496 0.52399417 0.43792583
## a_worker[25] a_worker[26] a_worker[27] a_worker[28] a_worker[29] a_worker[30]
## 1.68835573 1.12607997 0.82873875 1.84673440 1.59573607 3.14001702
## a_worker[31] a_worker[32] a_worker[33] a_worker[34] a_worker[35] a_worker[36]
## 0.93976673 1.35735237 1.55460603 1.45739731 0.66623700 0.49863375
## a_worker[37] a_worker[38] a_worker[39] a_worker[40] a_worker[41] a_worker[42]
## 0.83721832 1.97712005 5.91173231 0.41930603 1.70450118 0.49020195
## a_worker[43] a_worker[44] a_worker[45] a_worker[46] a_worker[47] a_worker[48]
## 1.17292159 0.41455846 0.76089776 0.65223722 0.50784667 2.91108972
## a_worker[49] a_worker[50] a_worker[51] a_worker[52] a_worker[53] a_worker[54]
## 0.70531001 3.16392096 1.40139799 0.66776404 1.04777370 1.39506791
## a_worker[55] a_worker[56] a_worker[57] a_worker[58] a_worker[59] a_worker[60]
## 2.16934795 5.42527561 0.46330205 0.55221903 1.50435803 0.88912579
## a_worker[61] a_worker[62] a_worker[63] a_worker[64] a_worker[65] a_worker[66]
## 1.23109427 2.27855575 0.89985176 0.66555091 0.56458204 0.44293875
## a_worker[67] a_worker[68] a_worker[69] a_worker[70] a_worker[71] a_worker[72]
## 1.81215190 3.45838643 0.46181499 0.76220280 1.67152007 2.57144498
## a_worker[73] a_worker[74] a_worker[75] a_worker[76] a_worker[77] a_worker[78]
## 0.40460301 0.76660113 1.01012244 1.05740687 0.39804657 0.99834464
## a_worker[79] a_worker[80] a_worker[81] a_worker[82] a_worker[83] a_worker[84]
## 1.27082940 0.74856566 0.75108401 2.06855284 0.38378018 0.41649586
## a_worker[85] a_worker[86] a_worker[87] a_worker[88] a_worker[89] a_worker[90]
## 1.72253252 4.50729321 0.36821347 0.96434366 0.32349159 0.44462452
## a_spec[1] a_spec[2] a_spec[3] a_spec[4] a_spec[5] a_spec[6]
## 0.07915926 0.07433649 0.09833638 0.06852226 0.33527258 0.42636821
## a_spec[7] a_spec[8] a_spec[9] a_spec[10] a_spec[11] a_spec[12]
## 19.07570921 1.09086631 2.22474638 0.24442946 8.74278676 0.92288404
## a_spec[13] a_spec[14] a_spec[15] a bnoagg bmean
## 8.48991823 32.86843215 4.07720803 0.01110107 1.56052654 1.95279476
## btrial sigma_worker sigma_spec
## 0.99326779 3.03082994 10.50323120## 105 vector or matrix parameters omitted in display. Use depth=2 to show them.## item value
## 1 bnoagg 0.9487412241
## 2 bnoagg 0.1149100024
## 3 bnoagg 0.4834389309
## 4 bnoagg 0.7143997320
## 5 bnoagg 0.3752994462
## 6 bnoagg 0.2747148221
## 7 bnoagg -0.0848620292
## 8 bnoagg 0.9961021059
## 9 bnoagg 0.9527633254
## 10 bnoagg 0.2565497073
## 11 bnoagg 0.3095915758
## 12 bnoagg 0.0405424411
## 13 bnoagg 0.4574212349
## 14 bnoagg 0.2694223216
## 15 bnoagg 0.4855842367
## 16 bnoagg 0.6018822670
## 17 bnoagg 0.3440960208
## 18 bnoagg 0.8464322096
## 19 bnoagg 0.6943223158
## 20 bnoagg 0.5322762160
## 21 bnoagg 0.8522320233
## 22 bnoagg 0.6170524541
## 23 bnoagg 0.4639101046
## 24 bnoagg 0.5615405790
## 25 bnoagg 0.8572597473
## 26 bnoagg -0.1190076792
## 27 bnoagg 0.1802529544
## 28 bnoagg 0.2574311658
## 29 bnoagg 0.3997979074
## 30 bnoagg 0.7339592989
## 31 bnoagg 0.2533531051
## 32 bnoagg 0.6919944074
## 33 bnoagg 0.8999554651
## 34 bnoagg 0.2038722467
## 35 bnoagg 0.4880143019
## 36 bnoagg 0.3802461587
## 37 bnoagg 0.4795950394
## 38 bnoagg 0.1135840635
## 39 bnoagg 0.2669753008
## 40 bnoagg 0.2756386861
## 41 bnoagg 0.8378332306
## 42 bnoagg 0.6832054506
## 43 bnoagg -0.2499934069
## 44 bnoagg 0.7055287059
## 45 bnoagg -0.2221703052
## 46 bnoagg 0.0204197475
## 47 bnoagg 0.6258425045
## 48 bnoagg 0.3995606147
## 49 bnoagg -0.0042027980
## 50 bnoagg 0.8629120581
## 51 bnoagg -0.0033362846
## 52 bnoagg 0.1903619306
## 53 bnoagg -0.0582832951
## 54 bnoagg 0.5585063032
## 55 bnoagg 0.0903902583
## 56 bnoagg 1.1839205312
## 57 bnoagg 0.9095027294
## 58 bnoagg 0.6100710103
## 59 bnoagg -0.1794842022
## 60 bnoagg 0.7010014694
## 61 bnoagg 0.1337470675
## 62 bnoagg 1.0398315849
## 63 bnoagg 0.5046213944
## 64 bnoagg 0.1150106604
## 65 bnoagg 0.5281735891
## 66 bnoagg 0.3295192331
## 67 bnoagg 0.1385546252
## 68 bnoagg 0.2930043441
## 69 bnoagg -0.1322036249
## 70 bnoagg 0.6464615893
## 71 bnoagg 0.3390928774
## 72 bnoagg 0.6491591345
## 73 bnoagg 0.0316041620
## 74 bnoagg 0.7799053030
## 75 bnoagg 0.5797309567
## 76 bnoagg 0.9154827417
## 77 bnoagg 0.0976437838
## 78 bnoagg 0.6642203038
## 79 bnoagg 0.7488922722
## 80 bnoagg 0.5077630733
## 81 bnoagg 0.4552599448
## 82 bnoagg 0.7909381116
## 83 bnoagg 0.4546074388
## 84 bnoagg 0.5257739573
## 85 bnoagg 0.5844944655
## 86 bnoagg -0.0813538141
## 87 bnoagg 0.9200682278
## 88 bnoagg 0.4593670478
## 89 bnoagg 0.2816762283
## 90 bnoagg 0.2652168328
## 91 bnoagg 0.9170141559
## 92 bnoagg 0.1538639844
## 93 bnoagg -0.1192006065
## 94 bnoagg 0.3098543397
## 95 bnoagg 0.3709375112
## 96 bnoagg 0.5755665954
## 97 bnoagg 0.7832003378
## 98 bnoagg 0.8608418000
## 99 bnoagg 0.6435807108
## 100 bnoagg 0.3485247135
## 101 bnoagg 0.0957761745
## 102 bnoagg 0.6917141077
## 103 bnoagg 0.2140871218
## 104 bnoagg 0.7832188132
## 105 bnoagg -0.1019634447
## 106 bnoagg 0.1354103261
## 107 bnoagg 0.3293102508
## 108 bnoagg 0.2694133748
## 109 bnoagg 0.5698803643
## 110 bnoagg 0.8028141342
## 111 bnoagg -0.1768554833
## 112 bnoagg 0.2615266851
## 113 bnoagg 0.2673242439
## 114 bnoagg 0.3219640194
## 115 bnoagg 0.4768402670
## 116 bnoagg 0.8936966675
## 117 bnoagg 0.4464372984
## 118 bnoagg 1.1297949556
## 119 bnoagg 0.2856866446
## 120 bnoagg 0.6430020910
## 121 bnoagg -0.0633185899
## 122 bnoagg 0.8870871157
## 123 bnoagg 0.7953717654
## 124 bnoagg 0.0870145374
## 125 bnoagg 0.8652153228
## 126 bnoagg 0.2128213113
## 127 bnoagg 0.1744925873
## 128 bnoagg 1.0021735810
## 129 bnoagg 0.3221186122
## 130 bnoagg 0.2418348968
## 131 bnoagg 0.4864398627
## 132 bnoagg 0.2986318573
## 133 bnoagg 0.2948770911
## 134 bnoagg 0.2261175695
## 135 bnoagg 0.1645260209
## 136 bnoagg 0.4911664771
## 137 bnoagg -0.1733749525
## 138 bnoagg -0.0073310815
## 139 bnoagg 0.1871987742
## 140 bnoagg 0.4875924995
## 141 bnoagg 0.7110581166
## 142 bnoagg 0.4766884629
## 143 bnoagg 0.6754672317
## 144 bnoagg 0.3064236523
## 145 bnoagg 0.1382558497
## 146 bnoagg 0.4501301613
## 147 bnoagg 0.6007890220
## 148 bnoagg 1.0020661685
## 149 bnoagg 0.6529988565
## 150 bnoagg 0.6511585929
## 151 bnoagg 1.1528992078
## 152 bnoagg 0.6545844020
## 153 bnoagg 1.0171054366
## 154 bnoagg 1.0721139325
## 155 bnoagg 0.4544570974
## 156 bnoagg 0.3225127983
## 157 bnoagg 0.2683074718
## 158 bnoagg 0.0174326478
## 159 bnoagg 0.3739400025
## 160 bnoagg 0.3074297450
## 161 bnoagg 0.2953204472
## 162 bnoagg 0.7411935164
## 163 bnoagg 0.1333193747
## 164 bnoagg -0.2746460616
## 165 bnoagg 0.0182263030
## 166 bnoagg 0.6333314361
## 167 bnoagg 0.8411631299
## 168 bnoagg 0.6855913341
## 169 bnoagg 0.4724439227
## 170 bnoagg 0.2208804784
## 171 bnoagg 0.3035832007
## 172 bnoagg 1.1534627023
## 173 bnoagg 0.2828417569
## 174 bnoagg 0.5541700082
## 175 bnoagg 0.1795200222
## 176 bnoagg 0.2283233632
## 177 bnoagg 0.4619210626
## 178 bnoagg 0.0818656954
## 179 bnoagg 0.8855723857
## 180 bnoagg 0.8613799266
## 181 bnoagg 0.4612955821
## 182 bnoagg 0.6005098027
## 183 bnoagg 0.4305015124
## 184 bnoagg 0.2671918501
## 185 bnoagg 0.3115726802
## 186 bnoagg 0.6580555340
## 187 bnoagg -0.1590731535
## 188 bnoagg 0.4197504186
## 189 bnoagg 0.0588812358
## 190 bnoagg 0.6739651931
## 191 bnoagg 0.3787530501
## 192 bnoagg 0.0170490108
## 193 bnoagg 1.0041729795
## 194 bnoagg 0.2309480337
## 195 bnoagg 0.3646604148
## 196 bnoagg 0.6324462253
## 197 bnoagg 0.5481413328
## 198 bnoagg -0.0892909311
## 199 bnoagg 0.6142835024
## 200 bnoagg 0.0487030561
## 201 bnoagg 0.0918942704
## 202 bnoagg 0.6803256852
## 203 bnoagg 0.2412973233
## 204 bnoagg 0.9862519227
## 205 bnoagg 0.3663164174
## 206 bnoagg -0.1013373703
## 207 bnoagg 0.5383128779
## 208 bnoagg 0.9794160289
## 209 bnoagg 0.8781134254
## 210 bnoagg 0.4248416584
## 211 bnoagg 0.1080441721
## 212 bnoagg 0.1833998404
## 213 bnoagg 0.7415353663
## 214 bnoagg 0.4929214886
## 215 bnoagg 0.8865319284
## 216 bnoagg 0.5991262383
## 217 bnoagg 0.5396458858
## 218 bnoagg 0.7418408465
## 219 bnoagg 0.0972984767
## 220 bnoagg 0.3495189924
## 221 bnoagg 0.5092930097
## 222 bnoagg 0.3487556696
## 223 bnoagg 0.4333780441
## 224 bnoagg 0.1074094977
## 225 bnoagg 0.6841030873
## 226 bnoagg 0.2291560952
## 227 bnoagg 0.2577920786
## 228 bnoagg 0.2301641042
## 229 bnoagg 0.2980723662
## 230 bnoagg -0.0231988523
## 231 bnoagg 0.3800027265
## 232 bnoagg 0.4605463353
## 233 bnoagg 0.6237451291
## 234 bnoagg 0.2780851981
## 235 bnoagg 0.6748916786
## 236 bnoagg 0.5932824911
## 237 bnoagg 1.0353763218
## 238 bnoagg 0.3818964882
## 239 bnoagg 0.6558792141
## 240 bnoagg 0.6283882105
## 241 bnoagg 0.0623975644
## 242 bnoagg 0.2139407527
## 243 bnoagg 0.3066000392
## 244 bnoagg -0.0896765396
## 245 bnoagg 0.4082586543
## 246 bnoagg 0.1895835401
## 247 bnoagg 0.7146585016
## 248 bnoagg 0.6736319304
## 249 bnoagg 0.5399941022
## 250 bnoagg 1.2482586680
## 251 bnoagg 0.4117873453
## 252 bnoagg 0.3160097350
## 253 bnoagg 0.1577017155
## 254 bnoagg 0.0962550742
## 255 bnoagg 0.4312590772
## 256 bnoagg 0.7292721931
## 257 bnoagg -0.2709895341
## 258 bnoagg 0.4329609073
## 259 bnoagg 0.7007843144
## 260 bnoagg 0.8824775959
## 261 bnoagg 0.5789041252
## 262 bnoagg 0.9078993172
## 263 bnoagg 0.1020496794
## 264 bnoagg 0.3193673119
## 265 bnoagg 0.4761869337
## 266 bnoagg 0.4926497128
## 267 bnoagg 0.7267241710
## 268 bnoagg 0.4998170178
## 269 bnoagg 0.4166433642
## 270 bnoagg 0.4758472153
## 271 bnoagg -0.0524664160
## 272 bnoagg 0.0485798841
## 273 bnoagg 1.0699275016
## 274 bnoagg 0.7020331676
## 275 bnoagg 0.6405040227
## 276 bnoagg 0.5573959353
## 277 bnoagg 0.4898637508
## 278 bnoagg 0.7948777492
## 279 bnoagg 0.4987433118
## 280 bnoagg 0.9668607474
## 281 bnoagg 0.1800346195
## 282 bnoagg 0.6819650170
## 283 bnoagg -0.6349096490
## 284 bnoagg 0.1352517113
## 285 bnoagg 0.1956176624
## 286 bnoagg 0.5139070048
## 287 bnoagg 0.5115425969
## 288 bnoagg -0.0231857589
## 289 bnoagg 0.7894045944
## 290 bnoagg 0.3952411107
## 291 bnoagg 0.4001666303
## 292 bnoagg 0.3931096256
## 293 bnoagg 0.9980482345
## 294 bnoagg 0.2114020187
## 295 bnoagg 0.0836286540
## 296 bnoagg 0.5332480100
## 297 bnoagg 0.5707485635
## 298 bnoagg 0.4584076008
## 299 bnoagg 0.6646681054
## 300 bnoagg 0.3772474462
## 301 bnoagg 0.7440593892
## 302 bnoagg 0.6156205007
## 303 bnoagg 0.2599952651
## 304 bnoagg 0.4841607677
## 305 bnoagg -0.1067413626
## 306 bnoagg 0.0321866618
## 307 bnoagg 0.5939658418
## 308 bnoagg 0.4909633881
## 309 bnoagg 0.4754644386
## 310 bnoagg 0.2575941615
## 311 bnoagg 0.8459436534
## 312 bnoagg 0.3387547669
## 313 bnoagg 0.4257667573
## 314 bnoagg 0.8820834585
## 315 bnoagg 0.3215999130
## 316 bnoagg 0.5861939095
## 317 bnoagg 0.3014618950
## 318 bnoagg 0.4884418855
## 319 bnoagg 0.0101914437
## 320 bnoagg 0.4822332831
## 321 bnoagg 0.3313087291
## 322 bnoagg 0.0947731989
## 323 bnoagg -0.0309813253
## 324 bnoagg 0.5903997125
## 325 bnoagg 0.2861856524
## 326 bnoagg -0.1886165737
## 327 bnoagg 0.2274157828
## 328 bnoagg 0.8799333906
## 329 bnoagg 0.1328056362
## 330 bnoagg 0.4522024339
## 331 bnoagg 0.8387119665
## 332 bnoagg 0.2866369956
## 333 bnoagg 0.5246957459
## 334 bnoagg 0.1324933573
## 335 bnoagg 0.0919736402
## 336 bnoagg -0.1068828050
## 337 bnoagg -0.1769656922
## 338 bnoagg 0.7371036653
## 339 bnoagg 0.0262321667
## 340 bnoagg 0.7364030406
## 341 bnoagg 0.6664807564
## 342 bnoagg 0.2785848602
## 343 bnoagg 0.1716988734
## 344 bnoagg 0.3162656179
## 345 bnoagg 0.2190104282
## 346 bnoagg -0.3341036443
## 347 bnoagg 0.5093586169
## 348 bnoagg 0.5484700454
## 349 bnoagg 0.4533971735
## 350 bnoagg 0.7335022201
## 351 bnoagg 0.7851060669
## 352 bnoagg 0.9794776391
## 353 bnoagg 0.3942777690
## 354 bnoagg 0.7473855938
## 355 bnoagg 0.7291729500
## 356 bnoagg 0.3079616747
## 357 bnoagg 0.7206089212
## 358 bnoagg 0.3320649549
## 359 bnoagg 0.4846086345
## 360 bnoagg 0.3302950879
## 361 bnoagg 0.2618095463
## 362 bnoagg 0.4382364281
## 363 bnoagg 0.7235424046
## 364 bnoagg 0.4054557806
## 365 bnoagg 0.3597339060
## 366 bnoagg 0.4905811047
## 367 bnoagg 0.3463936814
## 368 bnoagg 0.7231691999
## 369 bnoagg 0.6056213758
## 370 bnoagg -0.1797192870
## 371 bnoagg 0.4523450272
## 372 bnoagg 0.6565138457
## 373 bnoagg 0.1148753696
## 374 bnoagg 0.2385589858
## 375 bnoagg 0.2077492432
## 376 bnoagg 0.7560369378
## 377 bnoagg 0.0616071617
## 378 bnoagg -0.0758338731
## 379 bnoagg 0.9677776018
## 380 bnoagg 0.1692607585
## 381 bnoagg 0.2445028560
## 382 bnoagg 0.6133381743
## 383 bnoagg -0.0270478506
## 384 bnoagg 0.5491159733
## 385 bnoagg 0.6165465921
## 386 bnoagg -0.4741901147
## 387 bnoagg 0.7506369269
## 388 bnoagg -0.0151609208
## 389 bnoagg 0.9655832934
## 390 bnoagg 0.9771426730
## 391 bnoagg 0.0856246191
## 392 bnoagg 0.3609876774
## 393 bnoagg 1.3012678778
## 394 bnoagg -0.1939631039
## 395 bnoagg 0.1066765384
## 396 bnoagg 0.2381111891
## 397 bnoagg 0.5221766846
## 398 bnoagg 0.1938981017
## 399 bnoagg 0.3498246661
## 400 bnoagg 0.6025685925
## 401 bnoagg 0.5584711603
## 402 bnoagg 0.6082599693
## 403 bnoagg -0.0480577521
## 404 bnoagg -0.0433287388
## 405 bnoagg 0.3470174469
## 406 bnoagg 0.4461165057
## 407 bnoagg 0.3617823764
## 408 bnoagg 0.8975564228
## 409 bnoagg 0.3744505355
## 410 bnoagg 0.1034204293
## 411 bnoagg 0.6324949619
## 412 bnoagg 1.2995065303
## 413 bnoagg 0.5388488433
## 414 bnoagg 0.1770055937
## 415 bnoagg 0.2473465642
## 416 bnoagg 0.9935075827
## 417 bnoagg 1.0212003890
## 418 bnoagg 0.9357615813
## 419 bnoagg 0.6534089089
## 420 bnoagg 0.5546663271
## 421 bnoagg -0.0504980972
## 422 bnoagg 0.3368566001
## 423 bnoagg 0.2723118960
## 424 bnoagg 0.2765464778
## 425 bnoagg 0.7999636256
## 426 bnoagg 0.5167845345
## 427 bnoagg 0.7826056988
## 428 bnoagg 0.2515526152
## 429 bnoagg 0.1763713461
## 430 bnoagg 0.0728735779
## 431 bnoagg 0.8573074904
## 432 bnoagg 0.6074904466
## 433 bnoagg 0.1989180843
## 434 bnoagg 0.1328254796
## 435 bnoagg 0.6220153803
## 436 bnoagg 0.6993999647
## 437 bnoagg 0.2315220189
## 438 bnoagg 0.5493539677
## 439 bnoagg 0.2400019965
## 440 bnoagg 0.4876016455
## 441 bnoagg 0.6336666121
## 442 bnoagg -0.0212737456
## 443 bnoagg 0.5229931666
## 444 bnoagg 0.8393845954
## 445 bnoagg 0.5427146567
## 446 bnoagg 0.2440655770
## 447 bnoagg 0.4123330860
## 448 bnoagg 0.1420003429
## 449 bnoagg 0.5170807473
## 450 bnoagg -0.0055133014
## 451 bnoagg 0.9867797961
## 452 bnoagg 0.9936600738
## 453 bnoagg 1.1044362697
## 454 bnoagg 0.3597640143
## 455 bnoagg 0.8616650300
## 456 bnoagg 0.5268642451
## 457 bnoagg 0.6303489784
## 458 bnoagg -0.0172590512
## 459 bnoagg 0.6715290020
## 460 bnoagg -0.2969344683
## 461 bnoagg 0.6356433040
## 462 bnoagg 0.3909988617
## 463 bnoagg 0.4231557397
## 464 bnoagg 0.5434964191
## 465 bnoagg 0.3422432184
## 466 bnoagg 0.7052843344
## 467 bnoagg 0.1120734650
## 468 bnoagg 0.8477353097
## 469 bnoagg 0.2442549133
## 470 bnoagg -0.1229815952
## 471 bnoagg 0.8451387297
## 472 bnoagg 0.5094375651
## 473 bnoagg 0.6944531093
## 474 bnoagg 0.6813148450
## 475 bnoagg 0.2314544332
## 476 bnoagg 0.5160912124
## 477 bnoagg 0.5623072794
## 478 bnoagg 0.5358814803
## 479 bnoagg -0.1381564543
## 480 bnoagg 0.2485376704
## 481 bnoagg 0.6447743288
## 482 bnoagg 0.2937695118
## 483 bnoagg 0.2596242766
## 484 bnoagg 0.3590290755
## 485 bnoagg 0.3780777008
## 486 bnoagg 0.6548676862
## 487 bnoagg -0.0910282390
## 488 bnoagg 0.2472731984
## 489 bnoagg -0.1768164930
## 490 bnoagg 0.5092837388
## 491 bnoagg 0.4777005272
## 492 bnoagg 0.2362026342
## 493 bnoagg 0.1382761707
## 494 bnoagg 0.5922658742
## 495 bnoagg 0.5768595399
## 496 bnoagg 0.5635252767
## 497 bnoagg 0.6690116064
## 498 bnoagg 0.4329099975
## 499 bnoagg 0.7391947914
## 500 bnoagg 0.2333352196
## 501 bnoagg 0.1248206908
## 502 bnoagg 0.2612336411
## 503 bnoagg 0.7755456440
## 504 bnoagg 0.5850433214
## 505 bnoagg 0.3972582575
## 506 bnoagg 0.1813719670
## 507 bnoagg 0.9064752002
## 508 bnoagg 0.6435331498
## 509 bnoagg 0.5053575462
## 510 bnoagg 0.2546162673
## 511 bnoagg 0.1139066280
## 512 bnoagg 0.6922136536
## 513 bnoagg 0.2913173435
## 514 bnoagg 0.0627425264
## 515 bnoagg 0.6401583822
## 516 bnoagg 0.4869014762
## 517 bnoagg 0.6763482167
## 518 bnoagg 0.1461620646
## 519 bnoagg 0.0666880548
## 520 bnoagg 0.8458584971
## 521 bnoagg 0.1496761031
## 522 bnoagg 0.1808705578
## 523 bnoagg 0.0988735629
## 524 bnoagg 0.6414553479
## 525 bnoagg 0.5188523789
## 526 bnoagg 0.9125578762
## 527 bnoagg -0.0424271592
## 528 bnoagg 0.1474836701
## 529 bnoagg 0.3585362602
## 530 bnoagg 0.3385177865
## 531 bnoagg 0.4024653978
## 532 bnoagg 0.9890141186
## 533 bnoagg 0.3238257367
## 534 bnoagg 0.4510767872
## 535 bnoagg 1.1912697595
## 536 bnoagg 0.5636355146
## 537 bnoagg 0.7610941701
## 538 bnoagg 0.6301451397
## 539 bnoagg 0.1916449953
## 540 bnoagg 0.6541545153
## 541 bnoagg 0.6026027874
## 542 bnoagg 0.7036160689
## 543 bnoagg 0.4510495056
## 544 bnoagg 0.6497859256
## 545 bnoagg 1.4142051556
## 546 bnoagg 0.5996510775
## 547 bnoagg 0.2456471428
## 548 bnoagg 0.1254009147
## 549 bnoagg 0.6631055201
## 550 bnoagg 0.4869714573
## 551 bnoagg 0.4911457553
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## 1700 bnoagg -0.5548752837
## 1701 bnoagg 0.3683451443
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## 1703 bnoagg 0.3606441415
## 1704 bnoagg 0.6525306635
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## 1711 bnoagg 0.3172343775
## 1712 bnoagg 1.2474851649
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## 1714 bnoagg 0.2629318932
## 1715 bnoagg 0.6404092486
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## 1717 bnoagg 0.0710839671
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## 1725 bnoagg 0.5159417799
## 1726 bnoagg 0.6467928428
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## 1730 bnoagg 0.5947454247
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## 1738 bnoagg 0.1145567762
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## 1740 bnoagg 0.2285324100
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## 1778 bnoagg 0.8274905706
## 1779 bnoagg 0.8411445557
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## 1783 bnoagg 0.6403957705
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## 1790 bnoagg 0.2108101580
## 1791 bnoagg 0.4055202724
## 1792 bnoagg 0.4205311408
## 1793 bnoagg 0.4262917219
## 1794 bnoagg 0.0433716791
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## 1800 bnoagg 0.3885802240
## 1801 bnoagg 0.8660525046
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## 1804 bnoagg 0.2694706388
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## 1807 bnoagg 0.3373996766
## 1808 bnoagg 0.6704486355
## 1809 bnoagg 0.6629747764
## 1810 bnoagg 0.7739819487
## 1811 bnoagg 1.2282718107
## 1812 bnoagg 0.2012156060
## 1813 bnoagg 0.9073684847
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## 1818 bnoagg 0.7267731190
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## 1821 bnoagg 0.7663882036
## 1822 bnoagg 0.2839876758
## 1823 bnoagg 0.4532993987
## 1824 bnoagg 0.9497774650
## 1825 bnoagg 0.5668950365
## 1826 bnoagg -0.0045661454
## 1827 bnoagg 0.5241004994
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## 1829 bnoagg 0.4607203884
## 1830 bnoagg 0.4819412551
## 1831 bnoagg 0.4854639882
## 1832 bnoagg 0.7119580149
## 1833 bnoagg 0.7416606018
## 1834 bnoagg 0.6110936970
## 1835 bnoagg 0.1177500656
## 1836 bnoagg 0.4911694906
## 1837 bnoagg 0.4410209920
## 1838 bnoagg 0.4108765215
## 1839 bnoagg 1.2047681227
## 1840 bnoagg 0.2248316807
## 1841 bnoagg 0.7511976362
## 1842 bnoagg 0.4875472204
## 1843 bnoagg 0.3136532197
## 1844 bnoagg 0.4990158495
## 1845 bnoagg 0.5077955576
## 1846 bnoagg 0.2289462856
## 1847 bnoagg 0.5450042608
## 1848 bnoagg 0.8595491872
## 1849 bnoagg 0.8499746656
## 1850 bnoagg 0.4189970709
## 1851 bnoagg 0.9519941798
## 1852 bnoagg 0.5683116987
## 1853 bnoagg 0.4715380551
## 1854 bnoagg 0.0427727829
## 1855 bnoagg 0.3067843247
## 1856 bnoagg 0.1539905523
## 1857 bnoagg 0.4975221940
## 1858 bnoagg 0.2553272898
## 1859 bnoagg 0.7889176416
## 1860 bnoagg 0.8254791057
## 1861 bnoagg 0.5559005458
## 1862 bnoagg 0.9502972064
## 1863 bnoagg 0.4470802443
## 1864 bnoagg 1.2139032782
## 1865 bnoagg 0.2269873162
## 1866 bnoagg 0.0675567405
## 1867 bnoagg 0.6574992218
## 1868 bnoagg -0.0337414207
## 1869 bnoagg 0.6899193754
## 1870 bnoagg 0.2070970646
## 1871 bnoagg 0.3877551718
## 1872 bnoagg 0.9635971476
## 1873 bnoagg 0.9751069559
## 1874 bnoagg 0.2651553261
## 1875 bnoagg 0.6159140977
## 1876 bnoagg 0.8396966860
## 1877 bnoagg 0.7588575715
## 1878 bnoagg 0.5413863115
## 1879 bnoagg 0.1258007094
## 1880 bnoagg 0.2714198272
## 1881 bnoagg 0.4786235164
## 1882 bnoagg 0.1344081343
## 1883 bnoagg 0.6638119945
## 1884 bnoagg 0.6696432363
## 1885 bnoagg 0.6998666839
## 1886 bnoagg -0.0466296511
## 1887 bnoagg 0.2930835573
## 1888 bnoagg 0.5005687624
## 1889 bnoagg 0.6962262497
## 1890 bnoagg 0.5870074148
## 1891 bnoagg 0.2742904649
## 1892 bnoagg 0.9805084640
## 1893 bnoagg 0.0392587362
## 1894 bnoagg 0.8761355342
## 1895 bnoagg 0.5185260169
## 1896 bnoagg 0.4997844469
## 1897 bnoagg 1.0531847128
## 1898 bnoagg 0.6260520660
## 1899 bnoagg 0.6225442266
## 1900 bnoagg 0.3070841661
## 1901 bnoagg 0.2224689192
## 1902 bnoagg 0.1794824769
## 1903 bnoagg 0.3697655809
## 1904 bnoagg 0.0444888842
## 1905 bnoagg 0.6849288608
## 1906 bnoagg 0.4316485215
## 1907 bnoagg 0.9733134980
## 1908 bnoagg 0.6956105116
## 1909 bnoagg 0.6168173723
## 1910 bnoagg 0.8389476403
## 1911 bnoagg 0.7216700421
## 1912 bnoagg 0.3186728625
## 1913 bnoagg 0.8702915647
## 1914 bnoagg 0.5664598487
## 1915 bnoagg 1.1384786218
## 1916 bnoagg 0.3171914955
## 1917 bnoagg 0.3985271797
## 1918 bnoagg 0.6233262535
## 1919 bnoagg 0.1665105288
## 1920 bnoagg 0.8702668518
## 1921 bnoagg -0.1379755986
## 1922 bnoagg 0.5419094403
## 1923 bnoagg 1.1236974791
## 1924 bnoagg 0.0591047011
## 1925 bnoagg 0.7028807908
## 1926 bnoagg 0.5062930127
## 1927 bnoagg 0.1845092335
## 1928 bnoagg 0.8955374147
## 1929 bnoagg -0.1660042773
## 1930 bnoagg 0.0502464408
## 1931 bnoagg 0.1676244707
## 1932 bnoagg 0.6126226527
## 1933 bnoagg 0.4005586048
## 1934 bnoagg 0.8442786557
## 1935 bnoagg 0.0885581609
## 1936 bnoagg 1.1589989870
## 1937 bnoagg 0.1298574452
## 1938 bnoagg 0.3921744898
## 1939 bnoagg 0.1008911270
## 1940 bnoagg 1.0519177590
## 1941 bnoagg 0.5065354834
## 1942 bnoagg 0.8499872354
## 1943 bnoagg -0.3068960194
## 1944 bnoagg 0.2895203856
## 1945 bnoagg 0.3797874568
## 1946 bnoagg 0.6233325067
## 1947 bnoagg 0.6322323141
## 1948 bnoagg 0.6195377155
## 1949 bnoagg 0.0287264363
## 1950 bnoagg 0.4903007807
## 1951 bnoagg 0.1320046015
## 1952 bnoagg 0.2644908935
## 1953 bnoagg 0.5235940479
## 1954 bnoagg 0.7917599341
## 1955 bnoagg 0.8631649861
## 1956 bnoagg 0.1563389254
## 1957 bnoagg 0.5969630252
## 1958 bnoagg 0.2949430077
## 1959 bnoagg -0.2550127744
## 1960 bnoagg 0.6162254966
## 1961 bnoagg 1.0012017319
## 1962 bnoagg 0.3507548913
## 1963 bnoagg 0.8338233516
## 1964 bnoagg -0.0994053004
## 1965 bnoagg 0.5806427768
## 1966 bnoagg -0.1260000759
## 1967 bnoagg 0.7780460264
## 1968 bnoagg 0.4068513009
## 1969 bnoagg 0.4843118000
## 1970 bnoagg 0.7159914920
## 1971 bnoagg 0.3305132994
## 1972 bnoagg 0.1923351494
## 1973 bnoagg 0.0600727187
## 1974 bnoagg 0.3443073793
## 1975 bnoagg 0.2983686741
## 1976 bnoagg 0.7979120073
## 1977 bnoagg 0.1979783006
## 1978 bnoagg 0.1169022150
## 1979 bnoagg 0.6278024281
## 1980 bnoagg 0.2061569104
## 1981 bnoagg 0.9619098505
## 1982 bnoagg 0.4646242553
## 1983 bnoagg 0.4012800653
## 1984 bnoagg 0.7037850454
## 1985 bnoagg 0.5087664800
## 1986 bnoagg 0.0845677962
## 1987 bnoagg 0.4718806866
## 1988 bnoagg 0.6222633874
## 1989 bnoagg -0.0608552673
## 1990 bnoagg 0.1144045949
## 1991 bnoagg 0.8604208282
## 1992 bnoagg 0.3659521062
## 1993 bnoagg 0.6159745411
## 1994 bnoagg 1.0697205395
## 1995 bnoagg 0.6563469402
## 1996 bnoagg 0.4737601659
## 1997 bnoagg 0.1909858078
## 1998 bnoagg 0.5078226048
## 1999 bnoagg 0.6458648710
## 2000 bnoagg 0.4682034974
## 2001 bnoagg 0.6792166542
## 2002 bnoagg 0.6874451982
## 2003 bnoagg 0.4796962332
## 2004 bnoagg 0.1286344719
## 2005 bnoagg 0.5815082526
## 2006 bnoagg 0.1085429933
## 2007 bnoagg -0.1453388678
## 2008 bnoagg 0.4776340808
## 2009 bnoagg 0.5575549810
## 2010 bnoagg 0.3188616145
## 2011 bnoagg 0.6662960069
## 2012 bnoagg 0.2806875644
## 2013 bnoagg 0.4752285836
## 2014 bnoagg 0.5446519248
## 2015 bnoagg 0.4937188878
## 2016 bnoagg 0.4469233076
## 2017 bnoagg -0.0794634569
## 2018 bnoagg 0.9936406139
## 2019 bnoagg 0.3840451162
## 2020 bnoagg 0.6501549374
## 2021 bnoagg 0.1393232704
## 2022 bnoagg 0.5036837462
## 2023 bnoagg 0.4618515605
## 2024 bnoagg 0.5863965882
## 2025 bnoagg 0.7446891859
## 2026 bnoagg 0.4570370196
## 2027 bnoagg 0.4124660088
## 2028 bnoagg 0.7386475744
## 2029 bnoagg 0.6434935486
## 2030 bnoagg 1.0190595635
## 2031 bnoagg 0.0283525238
## 2032 bnoagg 0.8336471662
## 2033 bnoagg 0.0480583068
## 2034 bnoagg 0.0912423817
## 2035 bnoagg 0.3382598730
## 2036 bnoagg 0.8552350893
## 2037 bnoagg 0.4659374344
## 2038 bnoagg 0.1963016631
## 2039 bnoagg 0.8813503040
## 2040 bnoagg 0.7496662540
## 2041 bnoagg 0.6057206045
## 2042 bnoagg 0.6984592472
## 2043 bnoagg 0.5989369395
## 2044 bnoagg 0.8211705968
## 2045 bnoagg 0.3417415421
## 2046 bnoagg 0.4433090675
## 2047 bnoagg 1.0697924802
## 2048 bnoagg 0.7502341493
## 2049 bnoagg 0.4084440174
## 2050 bnoagg 0.4973437170
## 2051 bnoagg 0.2829637222
## 2052 bnoagg 0.5427680650
## 2053 bnoagg 0.5004647687
## 2054 bnoagg 0.8906960928
## 2055 bnoagg 0.5202395998
## 2056 bnoagg 0.9084348314
## 2057 bnoagg -0.0943172841
## 2058 bnoagg 0.8558800603
## 2059 bnoagg 0.4327432444
## 2060 bnoagg 0.2712664190
## 2061 bnoagg 0.8801101870
## 2062 bnoagg 0.3500281440
## 2063 bnoagg 0.3517323875
## 2064 bnoagg 0.5921352367
## 2065 bnoagg -0.0318218199
## 2066 bnoagg -0.3596103237
## 2067 bnoagg 0.8646464232
## 2068 bnoagg 0.2933039692
## 2069 bnoagg 0.2278971756
## 2070 bnoagg -0.2966765167
## 2071 bnoagg 0.8423315576
## 2072 bnoagg 0.3666689927
## 2073 bnoagg 1.0493537571
## 2074 bnoagg 0.6196711768
## 2075 bnoagg 0.2939689487
## 2076 bnoagg 0.6071304629
## 2077 bnoagg 0.3782325682
## 2078 bnoagg 0.2330813388
## 2079 bnoagg 0.6346780252
## 2080 bnoagg 0.4347564976
## 2081 bnoagg 0.3700487583
## 2082 bnoagg 0.7066121106
## 2083 bnoagg 0.5096682304
## 2084 bnoagg 0.1798934648
## 2085 bnoagg 0.1158414197
## 2086 bnoagg 0.8440485739
## 2087 bnoagg 0.3750969223
## 2088 bnoagg 0.7056788557
## 2089 bnoagg 0.6274901153
## 2090 bnoagg 0.6679005273
## 2091 bnoagg 0.8057560326
## 2092 bnoagg 0.4989226143
## 2093 bnoagg 0.3409279943
## 2094 bnoagg 0.4179512032
## 2095 bnoagg -0.2169624426
## 2096 bnoagg 0.6147218912
## 2097 bnoagg 0.2695417434
## 2098 bnoagg 0.3013712926
## 2099 bnoagg -0.0610378630
## 2100 bnoagg 0.5361381419
## 2101 bnoagg 0.9193805247
## 2102 bnoagg 0.5258478653
## 2103 bnoagg 0.1029661224
## 2104 bnoagg 0.6838191398
## 2105 bnoagg 0.8753952416
## 2106 bnoagg 0.2694289665
## 2107 bnoagg 0.0365592548
## 2108 bnoagg 1.0750327917
## 2109 bnoagg 0.9320212024
## 2110 bnoagg 0.8955291255
## 2111 bnoagg 0.3628490705
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## 2113 bnoagg 0.4818157544
## 2114 bnoagg 0.5982173099
## 2115 bnoagg 0.1312501097
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## 2117 bnoagg 1.0128713046
## 2118 bnoagg 0.6284701198
## 2119 bnoagg 0.1615150379
## 2120 bnoagg 0.3248481310
## 2121 bnoagg 0.5027312165
## 2122 bnoagg 0.6863634146
## 2123 bnoagg 0.3064661724
## 2124 bnoagg 0.3529602522
## 2125 bnoagg 0.4376620234
## 2126 bnoagg 0.3778180125
## 2127 bnoagg 0.2477640115
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## 2129 bnoagg 0.1047127461
## 2130 bnoagg 0.8663399671
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## 2134 bnoagg 0.5630040004
## 2135 bnoagg 0.5783388781
## 2136 bnoagg 0.0672775345
## 2137 bnoagg 0.6679358480
## 2138 bnoagg 0.2724146023
## 2139 bnoagg 0.4079082296
## 2140 bnoagg 0.1752537734
## 2141 bnoagg 0.8473050914
## 2142 bnoagg 0.2982372910
## 2143 bnoagg 0.3818430538
## 2144 bnoagg 0.1865606952
## 2145 bnoagg 0.8372240665
## 2146 bnoagg 0.6795508621
## 2147 bnoagg 0.1306896765
## 2148 bnoagg 0.0238427843
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## 2152 bnoagg 0.3960063848
## 2153 bnoagg 0.5300082740
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## 2155 bnoagg -0.1866457541
## 2156 bnoagg 0.6833579745
## 2157 bnoagg 0.6439166690
## 2158 bnoagg -0.0013852904
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## 2161 bnoagg 0.2405775937
## 2162 bnoagg 1.1017382230
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## 2195 bnoagg 0.6418350835
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## 2213 bnoagg 0.7867100858
## 2214 bnoagg 0.5692318345
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## 2216 bnoagg -0.3941812129
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## 2218 bnoagg 0.9987731532
## 2219 bnoagg 0.2935837335
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## 2221 bnoagg 0.5954351208
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## 2223 bnoagg 0.6313459508
## 2224 bnoagg 0.4880868741
## 2225 bnoagg 0.0286331907
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## 2228 bnoagg 0.3824611769
## 2229 bnoagg 0.4646525882
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## 2231 bnoagg 0.6051347284
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## 2233 bnoagg -0.1945591232
## 2234 bnoagg 0.3229780158
## 2235 bnoagg 0.2795881955
## 2236 bnoagg 0.1994943976
## 2237 bnoagg 0.6912628019
## 2238 bnoagg 0.3839047569
## 2239 bnoagg 0.6744543207
## 2240 bnoagg 0.3057865500
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## 2250 bnoagg -0.0084769936
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## 2252 bnoagg 0.5585706071
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## 2258 bnoagg 0.7842199375
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## 2260 bnoagg 0.9261880305
## 2261 bnoagg 0.2137094322
## 2262 bnoagg 0.2811293535
## 2263 bnoagg 0.5638276626
## 2264 bnoagg 0.2626401969
## 2265 bnoagg 0.6879507732
## 2266 bnoagg 1.0806758212
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## 2268 bnoagg 0.0770909189
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## 2272 bnoagg 0.3801756053
## 2273 bnoagg 0.8536941554
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## 1953 bnoagg 0.5235940479
## 1954 bnoagg 0.7917599341
## 1955 bnoagg 0.8631649861
## 1956 bnoagg 0.1563389254
## 1957 bnoagg 0.5969630252
## 1958 bnoagg 0.2949430077
## 1959 bnoagg -0.2550127744
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## 1961 bnoagg 1.0012017319
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## 1963 bnoagg 0.8338233516
## 1964 bnoagg -0.0994053004
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## 1966 bnoagg -0.1260000759
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## 1968 bnoagg 0.4068513009
## 1969 bnoagg 0.4843118000
## 1970 bnoagg 0.7159914920
## 1971 bnoagg 0.3305132994
## 1972 bnoagg 0.1923351494
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## 1975 bnoagg 0.2983686741
## 1976 bnoagg 0.7979120073
## 1977 bnoagg 0.1979783006
## 1978 bnoagg 0.1169022150
## 1979 bnoagg 0.6278024281
## 1980 bnoagg 0.2061569104
## 1981 bnoagg 0.9619098505
## 1982 bnoagg 0.4646242553
## 1983 bnoagg 0.4012800653
## 1984 bnoagg 0.7037850454
## 1985 bnoagg 0.5087664800
## 1986 bnoagg 0.0845677962
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## 1988 bnoagg 0.6222633874
## 1989 bnoagg -0.0608552673
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## 1991 bnoagg 0.8604208282
## 1992 bnoagg 0.3659521062
## 1993 bnoagg 0.6159745411
## 1994 bnoagg 1.0697205395
## 1995 bnoagg 0.6563469402
## 1996 bnoagg 0.4737601659
## 1997 bnoagg 0.1909858078
## 1998 bnoagg 0.5078226048
## 1999 bnoagg 0.6458648710
## 2000 bnoagg 0.4682034974
## 2001 bnoagg 0.6792166542
## 2002 bnoagg 0.6874451982
## 2003 bnoagg 0.4796962332
## 2004 bnoagg 0.1286344719
## 2005 bnoagg 0.5815082526
## 2006 bnoagg 0.1085429933
## 2007 bnoagg -0.1453388678
## 2008 bnoagg 0.4776340808
## 2009 bnoagg 0.5575549810
## 2010 bnoagg 0.3188616145
## 2011 bnoagg 0.6662960069
## 2012 bnoagg 0.2806875644
## 2013 bnoagg 0.4752285836
## 2014 bnoagg 0.5446519248
## 2015 bnoagg 0.4937188878
## 2016 bnoagg 0.4469233076
## 2017 bnoagg -0.0794634569
## 2018 bnoagg 0.9936406139
## 2019 bnoagg 0.3840451162
## 2020 bnoagg 0.6501549374
## 2021 bnoagg 0.1393232704
## 2022 bnoagg 0.5036837462
## 2023 bnoagg 0.4618515605
## 2024 bnoagg 0.5863965882
## 2025 bnoagg 0.7446891859
## 2026 bnoagg 0.4570370196
## 2027 bnoagg 0.4124660088
## 2028 bnoagg 0.7386475744
## 2029 bnoagg 0.6434935486
## 2030 bnoagg 1.0190595635
## 2031 bnoagg 0.0283525238
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## 7978 bmean 0.7451189926
## 7979 bmean 0.1143554695
## 7980 bmean 0.6615940070
## 7981 bmean 0.3986829038
## 7982 bmean 0.4957987306
## 7983 bmean 0.9708967736
## 7984 bmean 0.7018486299
## 7985 bmean 0.7575614216
## 7986 bmean 0.1207998646
## 7987 bmean 0.8299570716
## 7988 bmean 1.1163818061
## 7989 bmean 0.9327462985
## 7990 bmean 0.7456168175
## 7991 bmean -0.0154179137
## 7992 bmean 0.5697963425
## 7993 bmean 0.5305871066
## 7994 bmean 0.6683314048
## 7995 bmean 0.7021834029
## 7996 bmean 0.5962127268
## 7997 bmean 0.4960663373
## 7998 bmean 0.9795900753
## 7999 bmean 0.5469498094
## 8000 bmean 0.4520069435Magnitude of Effect Size Estimates - Remove Null
Next, we are interested in the effect magnitude estimates with and without counting of null effect generalizations – when coding for ES observations, there were many that mentioned an effect of no magnitude, i.e. “Neither ad is more effective than the other.” These statements are inherently ambiguous as the participant may have been sensitive to the magnitude of effect and estimated it to be 0 or they may have been thinking more coarsely and dichotomously about effects as either being significant or not. We plot the distributions of magnitude of effect size versus none and investigate the relationship between magnitude of effect size and generalization class. We are also curious about sensisivity to effect size when we remove generalizations that describe null effects or no effects.
df_50 %>%
subset(df_50$effectSizeMagnitudeRemoveNulls == TRUE, na.rm=TRUE) %>%
ddply(.(aggStrat), summarise,
TotalIsEffect=sum(effectSizeMagnitude==TRUE),
total=sum(correct==TRUE)+sum(correct==FALSE),
correct=sum(correct==TRUE),
acc=correct/total) ## aggStrat TotalIsEffect total correct acc
## 1 disagg 13 13 8 0.6153846
## 2 disagg+mean 20 20 12 0.6000000
## 3 mean 6 6 4 0.6666667## Mean accuracy with standard error, taking into account differences between workers
df_50 %>%
subset(df_50$effectSizeMagnitudeRemoveNulls == TRUE, na.rm=TRUE) %>%
ddply(.(workerId, aggStrat), summarise,
TotalIsEffect=sum(effectSizeMagnitude==TRUE),
total=sum(correct==TRUE)+sum(correct==FALSE),
correct=sum(correct==TRUE),
acc=correct/total) %>%
ddply(~aggStrat, summarise,
N = sum((total)),
meanAcc = mean(acc),
sd = sd(acc),
se = sd / sqrt(N))## aggStrat N meanAcc sd se
## 1 disagg 13 0.6250000 0.4826536 0.1338640
## 2 disagg+mean 20 0.5333333 0.5163978 0.1154701
## 3 mean 6 0.6666667 0.5163978 0.2108185
Effect Magnitude Remove Nulls Bayesian Model
Results
## Mean StdDev lower 0.95 upper 0.95 n_eff Rhat
## bnoagg 1.025193 0.5627030 -0.06311994 2.129558 3164.097 1.001300
## bmean 1.525089 0.5371338 0.50263917 2.607839 2858.994 1.001856## a_worker[1] a_worker[2] a_worker[3] a_worker[4] a_worker[5] a_worker[6]
## 6.651482e-01 5.430934e-01 5.112861e-01 1.948685e+00 1.358216e+00 7.237493e-01
## a_worker[7] a_worker[8] a_worker[9] a_worker[10] a_worker[11] a_worker[12]
## 8.398152e-01 1.358051e+00 6.727456e-01 5.243503e-01 5.010967e-01 8.019099e-01
## a_worker[13] a_worker[14] a_worker[15] a_worker[16] a_worker[17] a_worker[18]
## 5.154345e-01 5.537602e-01 5.491186e-01 6.479411e-01 6.046008e-01 2.247264e+01
## a_worker[19] a_worker[20] a_worker[21] a_worker[22] a_worker[23] a_worker[24]
## 5.586351e-01 7.897997e-01 4.195702e+00 5.944090e-01 7.026316e-01 5.765101e-01
## a_worker[25] a_worker[26] a_worker[27] a_worker[28] a_worker[29] a_worker[30]
## 6.225732e-01 6.940347e-01 5.752287e-01 1.918976e+00 6.828350e-01 1.000127e+01
## a_worker[31] a_worker[32] a_worker[33] a_worker[34] a_worker[35] a_worker[36]
## 2.016812e+00 2.671042e+00 5.526416e+00 7.302358e-01 5.511045e-01 6.266607e-01
## a_worker[37] a_worker[38] a_worker[39] a_worker[40] a_worker[41] a_worker[42]
## 7.969136e-01 5.940158e-01 3.539472e+00 6.264367e-01 2.152554e+00 5.719877e-01
## a_worker[43] a_worker[44] a_worker[45] a_worker[46] a_worker[47] a_worker[48]
## 4.731297e-01 5.819236e-01 8.838840e-01 7.299351e-01 5.904326e-01 5.204174e+00
## a_worker[49] a_worker[50] a_worker[51] a_worker[52] a_worker[53] a_worker[54]
## 7.355903e-01 3.785375e+00 2.221313e+00 7.735166e-01 2.345424e+00 7.047465e-01
## a_worker[55] a_worker[56] a_worker[57] a_worker[58] a_worker[59] a_worker[60]
## 2.817271e+00 2.035792e+01 6.666137e-01 6.905975e-01 3.376743e+00 9.253783e-01
## a_worker[61] a_worker[62] a_worker[63] a_worker[64] a_worker[65] a_worker[66]
## 6.981626e-01 5.579354e-01 5.784336e-01 6.738215e-01 6.336266e-01 5.880311e-01
## a_worker[67] a_worker[68] a_worker[69] a_worker[70] a_worker[71] a_worker[72]
## 2.377948e+00 2.205325e+00 5.867132e-01 5.338861e-01 4.796693e-01 5.477503e+00
## a_worker[73] a_worker[74] a_worker[75] a_worker[76] a_worker[77] a_worker[78]
## 6.231280e-01 5.717452e-01 4.210826e+00 6.300772e-01 5.574822e-01 6.199677e-01
## a_worker[79] a_worker[80] a_worker[81] a_worker[82] a_worker[83] a_worker[84]
## 7.405906e-01 6.126647e-01 6.671200e-01 3.954981e+00 5.382006e-01 5.089533e-01
## a_worker[85] a_worker[86] a_worker[87] a_worker[88] a_worker[89] a_worker[90]
## 2.148627e+00 6.922122e-01 5.398134e-01 5.830916e-01 4.452771e-01 5.745074e-01
## a_spec[1] a_spec[2] a_spec[3] a_spec[4] a_spec[5] a_spec[6]
## 2.066213e-01 1.882726e-01 2.274815e-01 2.052520e-01 1.888081e-01 4.013004e-01
## a_spec[7] a_spec[8] a_spec[9] a_spec[10] a_spec[11] a_spec[12]
## 8.213402e+00 1.482377e+00 1.541117e+00 1.834227e-01 5.914550e+00 2.027780e+00
## a_spec[13] a_spec[14] a_spec[15] a bnoagg bmean
## 3.860221e+00 4.142367e+00 8.381733e+00 6.406245e-04 2.787635e+00 4.595553e+00
## btrial sigma_worker sigma_spec
## 1.109536e+00 4.425738e+00 6.349959e+00## Mean StdDev lower 0.95 upper 0.95 n_eff Rhat
## bnoagg 2.787635 1.755411 0.9388309 8.411145 Inf 2.721817
## bmean 4.595553 1.711096 1.6530783 13.569692 Inf 2.723331Plot
## 105 vector or matrix parameters omitted in display. Use depth=2 to show them.## item value
## 1 bnoagg 0.0982466990
## 2 bnoagg 0.8315754730
## 3 bnoagg 1.5430175624
## 4 bnoagg 1.0820029206
## 5 bnoagg 0.8145966393
## 6 bnoagg 1.1974495074
## 7 bnoagg 1.0295445682
## 8 bnoagg 1.9329906066
## 9 bnoagg 0.6083519153
## 10 bnoagg 1.3101048266
## 11 bnoagg 0.8407603163
## 12 bnoagg 1.4690413997
## 13 bnoagg 1.2113295453
## 14 bnoagg 0.5369824020
## 15 bnoagg 1.4511952188
## 16 bnoagg 0.4846109901
## 17 bnoagg 1.9591029110
## 18 bnoagg 1.1914662105
## 19 bnoagg 0.5121688398
## 20 bnoagg 0.9505898056
## 21 bnoagg 1.2035237151
## 22 bnoagg 0.5709621840
## 23 bnoagg 1.1293815225
## 24 bnoagg 1.1205958323
## 25 bnoagg 1.3031553457
## 26 bnoagg 1.3235461000
## 27 bnoagg 2.2644497988
## 28 bnoagg 0.3562376581
## 29 bnoagg 1.1248725255
## 30 bnoagg 2.4852499090
## 31 bnoagg 0.9985625375
## 32 bnoagg 1.1096925629
## 33 bnoagg 1.8648063783
## 34 bnoagg 1.3954394226
## 35 bnoagg 1.1335548017
## 36 bnoagg 1.8094035004
## 37 bnoagg 1.3178759200
## 38 bnoagg 1.3572203572
## 39 bnoagg 0.5686168453
## 40 bnoagg 0.8986054531
## 41 bnoagg 1.5847089057
## 42 bnoagg 0.6163242290
## 43 bnoagg 1.1459296109
## 44 bnoagg 0.8221625185
## 45 bnoagg 1.3414815927
## 46 bnoagg 1.1434848119
## 47 bnoagg 0.0818192375
## 48 bnoagg 1.0272555956
## 49 bnoagg 1.0273989379
## 50 bnoagg 0.1907102649
## 51 bnoagg 0.5388616761
## 52 bnoagg 1.3171922686
## 53 bnoagg 1.5807906984
## 54 bnoagg 1.1401308724
## 55 bnoagg 1.5936349066
## 56 bnoagg -0.0872267277
## 57 bnoagg 1.1970888424
## 58 bnoagg 1.2246149269
## 59 bnoagg 1.1542435200
## 60 bnoagg 0.8919795883
## 61 bnoagg 1.1930128570
## 62 bnoagg -0.1844407273
## 63 bnoagg 1.1705945029
## 64 bnoagg 0.9498395496
## 65 bnoagg 2.0376539437
## 66 bnoagg 0.9709767788
## 67 bnoagg 0.7056879298
## 68 bnoagg 1.5764506076
## 69 bnoagg 1.0745372594
## 70 bnoagg 0.1046518367
## 71 bnoagg 0.6426754276
## 72 bnoagg 1.0542050567
## 73 bnoagg 1.2874541189
## 74 bnoagg 0.9027905141
## 75 bnoagg 0.8293281870
## 76 bnoagg 2.0944668962
## 77 bnoagg 0.4754845125
## 78 bnoagg 0.5943028552
## 79 bnoagg 0.8904279512
## 80 bnoagg 1.0372889924
## 81 bnoagg 1.3622528268
## 82 bnoagg 1.0654334715
## 83 bnoagg 1.5135862981
## 84 bnoagg 0.6804695043
## 85 bnoagg 1.5222093531
## 86 bnoagg 0.7802142272
## 87 bnoagg 1.8285577611
## 88 bnoagg 0.1671708690
## 89 bnoagg 0.4193511223
## 90 bnoagg -0.5312077675
## 91 bnoagg 1.2029069895
## 92 bnoagg 1.5473308070
## 93 bnoagg 0.8988982815
## 94 bnoagg 1.8372337903
## 95 bnoagg 1.1329750872
## 96 bnoagg -0.0232202612
## 97 bnoagg 0.7323599530
## 98 bnoagg 1.4039094255
## 99 bnoagg 1.1247600085
## 100 bnoagg 0.2678395675
## 101 bnoagg 1.7677353473
## 102 bnoagg 0.7332914124
## 103 bnoagg 1.1478066519
## 104 bnoagg 0.5109625160
## 105 bnoagg 0.4091855229
## 106 bnoagg 2.3571226614
## 107 bnoagg 0.6303843437
## 108 bnoagg 1.0070451830
## 109 bnoagg 2.3118715787
## 110 bnoagg -0.2803221100
## 111 bnoagg 0.2927244435
## 112 bnoagg 1.2774065689
## 113 bnoagg -0.0032297794
## 114 bnoagg 1.5643880708
## 115 bnoagg 1.6060468434
## 116 bnoagg 1.1325409468
## 117 bnoagg -0.3984943925
## 118 bnoagg 0.8610957768
## 119 bnoagg 0.8242183834
## 120 bnoagg 0.6675916314
## 121 bnoagg 0.3601327473
## 122 bnoagg 0.8150638332
## 123 bnoagg 1.1955797221
## 124 bnoagg 1.3792470182
## 125 bnoagg 1.8286210362
## 126 bnoagg 1.2496365551
## 127 bnoagg 1.2199590222
## 128 bnoagg 1.2348231006
## 129 bnoagg 0.5003700023
## 130 bnoagg 1.5100527737
## 131 bnoagg 0.2177116860
## 132 bnoagg 1.4775530673
## 133 bnoagg 0.7770949560
## 134 bnoagg 1.3069444411
## 135 bnoagg 1.2608065507
## 136 bnoagg 0.4091916896
## 137 bnoagg 0.9841543960
## 138 bnoagg 1.2814250707
## 139 bnoagg 1.7268147629
## 140 bnoagg 0.8156573953
## 141 bnoagg 0.5831823946
## 142 bnoagg 2.6458945751
## 143 bnoagg 1.4130061924
## 144 bnoagg 0.6557320417
## 145 bnoagg 0.7035672552
## 146 bnoagg 1.7541721542
## 147 bnoagg 0.8350286178
## 148 bnoagg 1.2096100334
## 149 bnoagg 1.2025281515
## 150 bnoagg 0.8015755381
## 151 bnoagg 0.9350272008
## 152 bnoagg 1.6918096121
## 153 bnoagg 0.9186102126
## 154 bnoagg 0.5788523382
## 155 bnoagg 0.6815903600
## 156 bnoagg 0.7559908994
## 157 bnoagg 0.5254761790
## 158 bnoagg 1.7774485885
## 159 bnoagg 1.2579071714
## 160 bnoagg 0.6570459166
## 161 bnoagg 0.3282274551
## 162 bnoagg 1.1484314021
## 163 bnoagg 1.2964644828
## 164 bnoagg 1.4989270748
## 165 bnoagg 0.8843842980
## 166 bnoagg 1.7044498151
## 167 bnoagg 0.9351112372
## 168 bnoagg 1.1844288800
## 169 bnoagg 1.3729320026
## 170 bnoagg 0.6239052750
## 171 bnoagg 0.8673893405
## 172 bnoagg 0.5508784755
## 173 bnoagg -0.3738619080
## 174 bnoagg 0.9126348606
## 175 bnoagg 0.8604822489
## 176 bnoagg 0.9944047740
## 177 bnoagg 0.9173348144
## 178 bnoagg 1.0867472094
## 179 bnoagg 0.6675889137
## 180 bnoagg 1.0364378200
## 181 bnoagg 0.4832052703
## 182 bnoagg 1.0552477008
## 183 bnoagg 2.1548573651
## 184 bnoagg 1.0410351994
## 185 bnoagg 0.0734960447
## 186 bnoagg 1.1396739932
## 187 bnoagg 1.5413962283
## 188 bnoagg 1.7033729196
## 189 bnoagg 1.2934640151
## 190 bnoagg 0.8225952219
## 191 bnoagg 0.8800603259
## 192 bnoagg 0.5952889075
## 193 bnoagg 1.0503116063
## 194 bnoagg 0.2719385368
## 195 bnoagg 1.7368957569
## 196 bnoagg 0.8811894561
## 197 bnoagg 0.1155029592
## 198 bnoagg 0.9370102235
## 199 bnoagg 0.4777829374
## 200 bnoagg -0.2238548457
## 201 bnoagg 0.1790774632
## 202 bnoagg 1.4298080664
## 203 bnoagg 0.3413000139
## 204 bnoagg 1.2626879072
## 205 bnoagg 1.4174360005
## 206 bnoagg 1.0228962357
## 207 bnoagg 1.0889213315
## 208 bnoagg 1.2225914221
## 209 bnoagg 1.1782734727
## 210 bnoagg 1.2866268162
## 211 bnoagg 0.4910119749
## 212 bnoagg 0.3386260755
## 213 bnoagg 1.0412451977
## 214 bnoagg 2.8151741718
## 215 bnoagg 0.8089989949
## 216 bnoagg 0.7358772072
## 217 bnoagg 0.6521975481
## 218 bnoagg 0.7220191352
## 219 bnoagg 2.3193131978
## 220 bnoagg 0.6584883940
## 221 bnoagg 1.2510625198
## 222 bnoagg -0.0701264472
## 223 bnoagg 1.6535935293
## 224 bnoagg 0.5172468139
## 225 bnoagg 0.7893664395
## 226 bnoagg 0.7150565597
## 227 bnoagg 0.5508500769
## 228 bnoagg 1.1411382727
## 229 bnoagg 1.0250980564
## 230 bnoagg 0.4492810637
## 231 bnoagg 0.0346504094
## 232 bnoagg 0.7480657009
## 233 bnoagg 1.3355652479
## 234 bnoagg 1.4551471563
## 235 bnoagg 2.0524586341
## 236 bnoagg 1.9725657890
## 237 bnoagg 1.2859282468
## 238 bnoagg 1.0597190959
## 239 bnoagg 1.4901027980
## 240 bnoagg 0.9420873036
## 241 bnoagg 1.0395094278
## 242 bnoagg 1.6944260634
## 243 bnoagg 0.6878475612
## 244 bnoagg 1.1205601873
## 245 bnoagg 0.5930504101
## 246 bnoagg 0.6062242738
## 247 bnoagg 0.8613202978
## 248 bnoagg 0.1552065558
## 249 bnoagg 1.8427054325
## 250 bnoagg 1.3730646916
## 251 bnoagg 0.7791544490
## 252 bnoagg 0.9089322719
## 253 bnoagg 0.5293731740
## 254 bnoagg 1.1110310155
## 255 bnoagg 0.7520105336
## 256 bnoagg 1.3022444044
## 257 bnoagg 1.3447011534
## 258 bnoagg 0.4527348045
## 259 bnoagg 1.3156230470
## 260 bnoagg 0.1818693738
## 261 bnoagg 0.2304070306
## 262 bnoagg 0.9373879960
## 263 bnoagg 2.4445653522
## 264 bnoagg 0.9058216743
## 265 bnoagg 1.4162922397
## 266 bnoagg 1.7834055852
## 267 bnoagg 2.2270002665
## 268 bnoagg 1.5143861609
## 269 bnoagg 0.7931458129
## 270 bnoagg 1.4713635136
## 271 bnoagg 0.2972641947
## 272 bnoagg 1.8725706188
## 273 bnoagg 0.8917251531
## 274 bnoagg 1.1396554119
## 275 bnoagg 0.7625864692
## 276 bnoagg 1.3523548334
## 277 bnoagg 0.8904630713
## 278 bnoagg 1.2933513981
## 279 bnoagg 1.1810582697
## 280 bnoagg 1.5389421517
## 281 bnoagg 0.5711481911
## 282 bnoagg 0.5451974459
## 283 bnoagg 1.2395428902
## 284 bnoagg 1.1231339522
## 285 bnoagg 0.9475307860
## 286 bnoagg 0.6851000444
## 287 bnoagg 1.1735687865
## 288 bnoagg 1.6279900961
## 289 bnoagg 1.0200222497
## 290 bnoagg 1.0242881751
## 291 bnoagg 0.9197864365
## 292 bnoagg 2.2234601167
## 293 bnoagg 1.4575702290
## 294 bnoagg 0.5255254750
## 295 bnoagg 1.2139174656
## 296 bnoagg 0.8842510050
## 297 bnoagg 0.9734915782
## 298 bnoagg 1.0338278116
## 299 bnoagg 1.3695750039
## 300 bnoagg 0.7946803234
## 301 bnoagg 0.6094371033
## 302 bnoagg 2.2874646949
## 303 bnoagg 0.5673701381
## 304 bnoagg 1.6105385350
## 305 bnoagg 1.5949283846
## 306 bnoagg 0.4021609915
## 307 bnoagg 0.9312414026
## 308 bnoagg 0.4040510495
## 309 bnoagg 0.6396079010
## 310 bnoagg 0.6129840757
## 311 bnoagg 1.4812395909
## 312 bnoagg 2.5754113210
## 313 bnoagg 1.2873875051
## 314 bnoagg 0.9423192386
## 315 bnoagg 1.2914018221
## 316 bnoagg 2.1190637454
## 317 bnoagg -0.0514951786
## 318 bnoagg 1.1667287629
## 319 bnoagg 1.1127151685
## 320 bnoagg 1.2293930686
## 321 bnoagg 0.4191454589
## 322 bnoagg 0.6948781158
## 323 bnoagg 0.7852412446
## 324 bnoagg 1.1794417050
## 325 bnoagg 1.2389309210
## 326 bnoagg 1.8907523229
## 327 bnoagg 2.3260241473
## 328 bnoagg 0.9554944926
## 329 bnoagg 1.5109959193
## 330 bnoagg 0.4746542061
## 331 bnoagg 1.5873613665
## 332 bnoagg 1.7481928172
## 333 bnoagg 0.6571701029
## 334 bnoagg 0.6565791854
## 335 bnoagg 1.8089701037
## 336 bnoagg 1.7250775993
## 337 bnoagg 0.3682530677
## 338 bnoagg 1.2130999898
## 339 bnoagg 0.3764904300
## 340 bnoagg 0.8399918478
## 341 bnoagg 1.9751530386
## 342 bnoagg 1.4818197812
## 343 bnoagg 0.7932193184
## 344 bnoagg 1.0596851487
## 345 bnoagg 0.8442289471
## 346 bnoagg 1.3421897596
## 347 bnoagg 1.7753418251
## 348 bnoagg 0.6577215050
## 349 bnoagg 0.3956697042
## 350 bnoagg 2.8359750301
## 351 bnoagg 1.5083226275
## 352 bnoagg 1.6105577875
## 353 bnoagg 0.3496725789
## 354 bnoagg 1.0060531369
## 355 bnoagg 1.1238897395
## 356 bnoagg 2.2917177407
## 357 bnoagg 1.6871797678
## 358 bnoagg -0.0986112856
## 359 bnoagg 1.0656918976
## 360 bnoagg 0.5224269915
## 361 bnoagg 1.0463355358
## 362 bnoagg 2.0251157096
## 363 bnoagg 1.0434277082
## 364 bnoagg 0.5126382817
## 365 bnoagg 1.5161426748
## 366 bnoagg 0.6625571231
## 367 bnoagg 0.4275586311
## 368 bnoagg -0.4343326435
## 369 bnoagg 1.1492163034
## 370 bnoagg 1.1015184686
## 371 bnoagg 0.3049322429
## 372 bnoagg 0.0804868626
## 373 bnoagg 0.4689793785
## 374 bnoagg 1.2724150340
## 375 bnoagg 0.7057613712
## 376 bnoagg 0.8914605574
## 377 bnoagg 1.3682793431
## 378 bnoagg 1.5804431464
## 379 bnoagg 0.1382557776
## 380 bnoagg 1.1194957643
## 381 bnoagg 1.1914867799
## 382 bnoagg 1.1843725116
## 383 bnoagg 0.6049509355
## 384 bnoagg 0.6771699861
## 385 bnoagg 1.0322495108
## 386 bnoagg 1.2578279359
## 387 bnoagg 0.5653219926
## 388 bnoagg 1.0269880331
## 389 bnoagg 0.0921426070
## 390 bnoagg 1.0365591044
## 391 bnoagg 0.9415402340
## 392 bnoagg 0.7953045813
## 393 bnoagg 0.5083645702
## 394 bnoagg 1.3850049997
## 395 bnoagg 0.4986257275
## 396 bnoagg 0.9805776533
## 397 bnoagg 0.9315508127
## 398 bnoagg 0.6348635015
## 399 bnoagg 1.1734664665
## 400 bnoagg 1.2461935120
## 401 bnoagg 0.5378624957
## 402 bnoagg 1.6245859099
## 403 bnoagg 1.3038352128
## 404 bnoagg 0.7167115409
## 405 bnoagg 1.0691973163
## 406 bnoagg -0.1833492785
## 407 bnoagg 0.8836789858
## 408 bnoagg 0.7497347571
## 409 bnoagg 2.5014890796
## 410 bnoagg 1.2142147956
## 411 bnoagg 0.1961216782
## 412 bnoagg 0.3109872742
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## 1569 bnoagg 0.7733903442
## 1570 bnoagg 2.2361220593
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## 1572 bnoagg 0.6437846433
## 1573 bnoagg 1.7118463325
## 1574 bnoagg 0.8695101918
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## 1577 bnoagg 1.3541379442
## 1578 bnoagg 0.4759588757
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## 1580 bnoagg 1.0758262345
## 1581 bnoagg 2.1065722635
## 1582 bnoagg 0.6632808674
## 1583 bnoagg 0.6729632026
## 1584 bnoagg 0.6582052119
## 1585 bnoagg 0.0636261203
## 1586 bnoagg 0.6674038475
## 1587 bnoagg 0.9462414351
## 1588 bnoagg 0.8987224183
## 1589 bnoagg 1.3600816871
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## 1591 bnoagg 1.3074592839
## 1592 bnoagg 1.0939166692
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## 1596 bnoagg 0.6136718141
## 1597 bnoagg 1.1778688554
## 1598 bnoagg 0.5125889690
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## 1600 bnoagg 0.9036173989
## 1601 bnoagg 1.6652459358
## 1602 bnoagg 0.7515630501
## 1603 bnoagg 0.6493896091
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## 1605 bnoagg 0.9754081817
## 1606 bnoagg 1.5192841363
## 1607 bnoagg 0.8277462292
## 1608 bnoagg 1.1128314856
## 1609 bnoagg 1.0871837711
## 1610 bnoagg 1.5020244751
## 1611 bnoagg 0.2710457563
## 1612 bnoagg 0.8456777967
## 1613 bnoagg 1.1651183323
## 1614 bnoagg 0.6583975540
## 1615 bnoagg 0.9161629446
## 1616 bnoagg 2.0192301242
## 1617 bnoagg 0.7242316753
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## 1619 bnoagg 1.0953246017
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## 1621 bnoagg 0.6203961643
## 1622 bnoagg 0.2829161355
## 1623 bnoagg 1.5940266142
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## 1625 bnoagg 1.2171076259
## 1626 bnoagg 1.7451828792
## 1627 bnoagg 1.4351573063
## 1628 bnoagg 1.0035420635
## 1629 bnoagg 1.1611704766
## 1630 bnoagg 0.0792332382
## 1631 bnoagg -0.3119042420
## 1632 bnoagg 1.0232044873
## 1633 bnoagg 1.1818887119
## 1634 bnoagg 1.0443222859
## 1635 bnoagg 1.0002192810
## 1636 bnoagg 0.5166073466
## 1637 bnoagg 0.9772496142
## 1638 bnoagg -0.1635953934
## 1639 bnoagg 0.9349102539
## 1640 bnoagg 1.9678759849
## 1641 bnoagg 0.2451485535
## 1642 bnoagg 0.6761655753
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## 1644 bnoagg 1.0559655227
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## 1646 bnoagg 0.7142308434
## 1647 bnoagg 0.9489394645
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## 1649 bnoagg 1.5745654667
## 1650 bnoagg 0.3934836428
## 1651 bnoagg 0.4006566813
## 1652 bnoagg 0.8367163910
## 1653 bnoagg 0.9581316446
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## 1655 bnoagg 1.7167424682
## 1656 bnoagg 0.7108677681
## 1657 bnoagg 1.9479064453
## 1658 bnoagg 0.6194731371
## 1659 bnoagg 1.2697232957
## 1660 bnoagg 0.7530688285
## 1661 bnoagg 0.8367727870
## 1662 bnoagg 1.1575074831
## 1663 bnoagg 0.7669896692
## 1664 bnoagg 0.7075839802
## 1665 bnoagg 1.4168386550
## 1666 bnoagg 1.6053654934
## 1667 bnoagg 1.4714125036
## 1668 bnoagg 1.3463995854
## 1669 bnoagg 1.6503657705
## 1670 bnoagg 1.0883800481
## 1671 bnoagg 1.0904762662
## 1672 bnoagg 0.9658330106
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## 1674 bnoagg 0.9163367468
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## 1677 bnoagg 0.7493422395
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## 1679 bnoagg 1.4862434342
## 1680 bnoagg 1.1032726732
## 1681 bnoagg 0.4389720086
## 1682 bnoagg 1.5367913630
## 1683 bnoagg 0.7503253199
## 1684 bnoagg 1.6273958250
## 1685 bnoagg 1.4141393739
## 1686 bnoagg 1.4810017648
## 1687 bnoagg 1.6858077763
## 1688 bnoagg 0.5431306005
## 1689 bnoagg 1.4515611161
## 1690 bnoagg 1.8744254838
## 1691 bnoagg 0.6291753400
## 1692 bnoagg 1.1401602682
## 1693 bnoagg 2.5880790531
## 1694 bnoagg 1.0075369637
## 1695 bnoagg 0.9457014851
## 1696 bnoagg 0.5783221410
## 1697 bnoagg 0.9509005751
## 1698 bnoagg 0.8651100068
## 1699 bnoagg 0.8226389577
## 1700 bnoagg 1.1559279405
## 1701 bnoagg 0.4963520344
## 1702 bnoagg 1.2467062960
## 1703 bnoagg 1.2054003655
## 1704 bnoagg 0.5468021224
## 1705 bnoagg 0.4960038146
## 1706 bnoagg 1.8203548724
## 1707 bnoagg 1.3497889771
## 1708 bnoagg -0.0936045750
## 1709 bnoagg -0.3572266633
## 1710 bnoagg 1.8684562292
## 1711 bnoagg 1.3675772614
## 1712 bnoagg 1.3900606481
## 1713 bnoagg 0.5383620886
## 1714 bnoagg 0.7768122639
## 1715 bnoagg 0.3383598981
## 1716 bnoagg 0.8387971288
## 1717 bnoagg 1.5643931961
## 1718 bnoagg 1.3507149734
## 1719 bnoagg 1.0226907359
## 1720 bnoagg 1.2948146238
## 1721 bnoagg 1.1857371231
## 1722 bnoagg 1.3117905847
## 1723 bnoagg 0.5240391733
## 1724 bnoagg 1.2626816757
## 1725 bnoagg 1.6431695490
## 1726 bnoagg 1.5440885214
## 1727 bnoagg 2.0785602486
## 1728 bnoagg 0.9129628070
## 1729 bnoagg 0.1030065869
## 1730 bnoagg 0.8945999461
## 1731 bnoagg 1.4525529176
## 1732 bnoagg 1.4335970587
## 1733 bnoagg 1.4743237365
## 1734 bnoagg 0.3295841031
## 1735 bnoagg 1.3167503651
## 1736 bnoagg 1.0842001935
## 1737 bnoagg 2.1700358575
## 1738 bnoagg 1.4041694831
## 1739 bnoagg 1.4828440731
## 1740 bnoagg 1.0410525736
## 1741 bnoagg 1.2553512214
## 1742 bnoagg 0.7273942650
## 1743 bnoagg 0.3741072110
## 1744 bnoagg 1.0399808035
## 1745 bnoagg 1.0470697651
## 1746 bnoagg 0.4552709991
## 1747 bnoagg 1.8442578654
## 1748 bnoagg 0.8035912709
## 1749 bnoagg 0.8186179134
## 1750 bnoagg -0.1375609361
## 1751 bnoagg 1.4794307541
## 1752 bnoagg 0.8346882220
## 1753 bnoagg 0.3960774305
## 1754 bnoagg 0.8026083059
## 1755 bnoagg 0.9787243553
## 1756 bnoagg 0.1857201394
## 1757 bnoagg 1.4221112052
## 1758 bnoagg 1.3112454318
## 1759 bnoagg 1.3915973253
## 1760 bnoagg 1.2438527002
## 1761 bnoagg 1.8524398566
## 1762 bnoagg 0.9323782336
## 1763 bnoagg 2.0639759645
## 1764 bnoagg 0.5460899680
## 1765 bnoagg 0.6903528498
## 1766 bnoagg 1.9854004610
## 1767 bnoagg 1.6849283115
## 1768 bnoagg 0.1474512194
## 1769 bnoagg 1.2964894349
## 1770 bnoagg 0.4703262517
## 1771 bnoagg 0.5948048226
## 1772 bnoagg 1.5414364139
## 1773 bnoagg 1.3596996676
## 1774 bnoagg 0.8073496867
## 1775 bnoagg 0.0437610849
## 1776 bnoagg 0.6945962446
## 1777 bnoagg 2.3749837863
## 1778 bnoagg 0.8731387847
## 1779 bnoagg 0.9869601695
## 1780 bnoagg 0.6398286982
## 1781 bnoagg 0.2337404703
## 1782 bnoagg 0.8636161794
## 1783 bnoagg 1.9284685749
## 1784 bnoagg 0.2491320092
## 1785 bnoagg 0.4930947875
## 1786 bnoagg 0.8052259594
## 1787 bnoagg 0.7641950565
## 1788 bnoagg 1.4692121088
## 1789 bnoagg 2.1343883082
## 1790 bnoagg 0.7324552656
## 1791 bnoagg 1.6442133528
## 1792 bnoagg 0.8982351730
## 1793 bnoagg 0.2462245612
## 1794 bnoagg 1.5672984518
## 1795 bnoagg 0.9166932139
## 1796 bnoagg 0.6175999723
## 1797 bnoagg -0.1287054334
## 1798 bnoagg 0.2616819535
## 1799 bnoagg 0.9630658783
## 1800 bnoagg 0.7722868584
## 1801 bnoagg 1.1022289209
## 1802 bnoagg 0.0155634811
## 1803 bnoagg 1.1713975012
## 1804 bnoagg 0.5861316865
## 1805 bnoagg 1.1904296997
## 1806 bnoagg 0.5654029507
## 1807 bnoagg 1.8963180871
## 1808 bnoagg 1.4674641106
## 1809 bnoagg 1.2377903336
## 1810 bnoagg 0.5726067661
## 1811 bnoagg 0.7752615972
## 1812 bnoagg 1.7814577954
## 1813 bnoagg 0.3951767638
## 1814 bnoagg 0.3203264263
## 1815 bnoagg 0.4303901658
## 1816 bnoagg 1.2852144321
## 1817 bnoagg 1.1311898618
## 1818 bnoagg 1.4464711937
## 1819 bnoagg 0.2123014691
## 1820 bnoagg 0.3712695416
## 1821 bnoagg 0.8676518807
## 1822 bnoagg 0.7661026408
## 1823 bnoagg 0.7921302357
## 1824 bnoagg 1.3959153844
## 1825 bnoagg 0.9652185405
## 1826 bnoagg 0.9944177036
## 1827 bnoagg -0.0817454501
## 1828 bnoagg 2.4550668198
## 1829 bnoagg 1.4477587349
## 1830 bnoagg 1.1674337663
## 1831 bnoagg 0.8931369124
## 1832 bnoagg 0.7514820757
## 1833 bnoagg 0.6321876321
## 1834 bnoagg 1.4249579094
## 1835 bnoagg 0.8331755062
## 1836 bnoagg 0.9769327184
## 1837 bnoagg -0.0039396467
## 1838 bnoagg 1.8713920235
## 1839 bnoagg 0.9430751972
## 1840 bnoagg 1.4389863441
## 1841 bnoagg -0.5057623255
## 1842 bnoagg 0.9308557223
## 1843 bnoagg 1.0002725282
## 1844 bnoagg 1.9084434711
## 1845 bnoagg 1.4976006933
## 1846 bnoagg -0.4686444173
## 1847 bnoagg 0.8672543747
## 1848 bnoagg 1.5901545640
## 1849 bnoagg 0.1534773383
## 1850 bnoagg 0.4006363193
## 1851 bnoagg 0.6526791907
## 1852 bnoagg 0.2947069231
## 1853 bnoagg 0.9276481084
## 1854 bnoagg 1.2317864117
## 1855 bnoagg -0.0320871613
## 1856 bnoagg 0.9913056235
## 1857 bnoagg 0.9458959159
## 1858 bnoagg 1.1581555801
## 1859 bnoagg 0.6226901931
## 1860 bnoagg 1.8547919548
## 1861 bnoagg 0.8038085306
## 1862 bnoagg 1.6692013507
## 1863 bnoagg 0.3441760515
## 1864 bnoagg 1.3421406696
## 1865 bnoagg 0.8190738701
## 1866 bnoagg 0.7317869498
## 1867 bnoagg 0.5704029935
## 1868 bnoagg 1.1067161859
## 1869 bnoagg 1.8169739193
## 1870 bnoagg 1.4621097178
## 1871 bnoagg 1.0271183022
## 1872 bnoagg 0.9539102523
## 1873 bnoagg 1.0319253328
## 1874 bnoagg 0.0922312064
## 1875 bnoagg 1.1196032137
## 1876 bnoagg 1.9644425925
## 1877 bnoagg 1.4966774702
## 1878 bnoagg 0.7392206852
## 1879 bnoagg 0.9318338255
## 1880 bnoagg 1.0763971892
## 1881 bnoagg 1.2071904384
## 1882 bnoagg 1.4130950365
## 1883 bnoagg 1.7694149852
## 1884 bnoagg 1.2943374327
## 1885 bnoagg 1.2939328725
## 1886 bnoagg 1.1804008512
## 1887 bnoagg 1.1362223832
## 1888 bnoagg 2.0080448951
## 1889 bnoagg 0.8152043017
## 1890 bnoagg 0.8044256556
## 1891 bnoagg 0.9015560679
## 1892 bnoagg 1.5313778174
## 1893 bnoagg 0.4153678083
## 1894 bnoagg 1.4572934194
## 1895 bnoagg 1.7328515900
## 1896 bnoagg 2.2286212252
## 1897 bnoagg 0.7858235779
## 1898 bnoagg 1.3695364151
## 1899 bnoagg 0.5720598716
## 1900 bnoagg 1.4697925355
## 1901 bnoagg 0.0555660569
## 1902 bnoagg 1.2185638043
## 1903 bnoagg 0.7872187741
## 1904 bnoagg 1.8093826391
## 1905 bnoagg 1.7723146075
## 1906 bnoagg 0.3373444995
## 1907 bnoagg 0.2968617829
## 1908 bnoagg 0.8519645649
## 1909 bnoagg 0.6407548209
## 1910 bnoagg 0.8568787288
## 1911 bnoagg 1.1367369060
## 1912 bnoagg 0.8048330418
## 1913 bnoagg 0.7399658215
## 1914 bnoagg 0.3846592298
## 1915 bnoagg 0.7369987414
## 1916 bnoagg 1.1940814922
## 1917 bnoagg 0.6245308241
## 1918 bnoagg 1.1506538630
## 1919 bnoagg 0.1630704140
## 1920 bnoagg 1.1521766988
## 1921 bnoagg 0.7692398295
## 1922 bnoagg 1.2463602989
## 1923 bnoagg 2.1996993955
## 1924 bnoagg 0.3184033887
## 1925 bnoagg 0.5135979137
## 1926 bnoagg 1.0425749546
## 1927 bnoagg 1.0125071415
## 1928 bnoagg 1.2004695617
## 1929 bnoagg 0.1466915983
## 1930 bnoagg 1.1928730794
## 1931 bnoagg 1.1083932898
## 1932 bnoagg 1.6129652613
## 1933 bnoagg 1.4384492803
## 1934 bnoagg 1.0490652998
## 1935 bnoagg 0.4002294287
## 1936 bnoagg 2.0370586666
## 1937 bnoagg 0.4962666266
## 1938 bnoagg 0.9045154705
## 1939 bnoagg 1.1812704977
## 1940 bnoagg 0.6740122383
## 1941 bnoagg 0.9412040047
## 1942 bnoagg 0.5368281946
## 1943 bnoagg 1.7748794007
## 1944 bnoagg 0.2125724975
## 1945 bnoagg 0.9954751920
## 1946 bnoagg 0.2950853915
## 1947 bnoagg 0.8238457212
## 1948 bnoagg 1.4036128089
## 1949 bnoagg 1.2258940808
## 1950 bnoagg 0.9223862082
## 1951 bnoagg 1.2543177263
## 1952 bnoagg 0.6291099011
## 1953 bnoagg 2.1976926570
## 1954 bnoagg 0.8441842223
## 1955 bnoagg 1.0319174025
## 1956 bnoagg 1.3528708374
## 1957 bnoagg 0.6844719333
## 1958 bnoagg 0.0329070790
## 1959 bnoagg 2.3425001438
## 1960 bnoagg 0.2422281366
## 1961 bnoagg 0.6852122495
## 1962 bnoagg 0.8597330556
## 1963 bnoagg 1.4704425690
## 1964 bnoagg 1.6280282077
## 1965 bnoagg 0.5812571245
## 1966 bnoagg 1.2749778562
## 1967 bnoagg 1.3394137521
## 1968 bnoagg 1.0542091911
## 1969 bnoagg 2.1675110504
## 1970 bnoagg 0.3463764533
## 1971 bnoagg 1.7702074558
## 1972 bnoagg 1.2874923874
## 1973 bnoagg 0.4765392966
## 1974 bnoagg 0.4220895121
## 1975 bnoagg 1.1036155028
## 1976 bnoagg 1.7737009632
## 1977 bnoagg 1.5414286423
## 1978 bnoagg 2.1490893287
## 1979 bnoagg 0.6411634479
## 1980 bnoagg 0.6359116376
## 1981 bnoagg 0.6935529836
## 1982 bnoagg 1.1340502425
## 1983 bnoagg 1.2319404963
## 1984 bnoagg 0.4449099984
## 1985 bnoagg 0.4143015473
## 1986 bnoagg 1.8234216689
## 1987 bnoagg 0.8537711504
## 1988 bnoagg -0.0629492461
## 1989 bnoagg 0.9367088758
## 1990 bnoagg 1.4967675480
## 1991 bnoagg 1.3374366413
## 1992 bnoagg -0.1279378000
## 1993 bnoagg 1.1701193708
## 1994 bnoagg 1.6251914985
## 1995 bnoagg 1.0141660612
## 1996 bnoagg 2.3961388102
## 1997 bnoagg 0.9474290976
## 1998 bnoagg 1.5286291534
## 1999 bnoagg 0.5703387717
## 2000 bnoagg 0.8300208392
## 2001 bnoagg 1.1837414225
## 2002 bnoagg 0.5956050777
## 2003 bnoagg 0.9817115891
## 2004 bnoagg 2.1540003562
## 2005 bnoagg 1.3681520248
## 2006 bnoagg 0.6390881886
## 2007 bnoagg 1.0139741052
## 2008 bnoagg 0.9608014577
## 2009 bnoagg 0.7969624953
## 2010 bnoagg 0.5340903369
## 2011 bnoagg 0.6150691385
## 2012 bnoagg 1.5755266506
## 2013 bnoagg 0.5424445272
## 2014 bnoagg 1.2322367523
## 2015 bnoagg 0.7635418918
## 2016 bnoagg 2.8512576357
## 2017 bnoagg 1.3451093077
## 2018 bnoagg -0.5160950728
## 2019 bnoagg 0.0117504580
## 2020 bnoagg 1.5632180361
## 2021 bnoagg 1.0526556175
## 2022 bnoagg 0.5467334260
## 2023 bnoagg 1.0520820241
## 2024 bnoagg 0.7038317809
## 2025 bnoagg 0.2641931377
## 2026 bnoagg 0.8661218221
## 2027 bnoagg 1.2318393019
## 2028 bnoagg 0.9449908528
## 2029 bnoagg 1.3486194687
## 2030 bnoagg 0.9612297998
## 2031 bnoagg 1.3798114708
## 2032 bnoagg 0.6787226444
## 2033 bnoagg -0.0226441157
## 2034 bnoagg 1.1717175578
## 2035 bnoagg 1.2808986791
## 2036 bnoagg 1.0114184602
## 2037 bnoagg 0.9532500278
## 2038 bnoagg 0.8241255304
## 2039 bnoagg 1.4645976318
## 2040 bnoagg 0.6376939767
## 2041 bnoagg 0.9299073338
## 2042 bnoagg 0.8778994227
## 2043 bnoagg 1.4251880339
## 2044 bnoagg 1.5453485200
## 2045 bnoagg 0.9757912135
## 2046 bnoagg 1.5978989492
## 2047 bnoagg 0.5128590256
## 2048 bnoagg 0.1758100086
## 2049 bnoagg 0.3285271534
## 2050 bnoagg 1.0765078625
## 2051 bnoagg 1.6089398343
## 2052 bnoagg 2.1485021239
## 2053 bnoagg 1.3732482507
## 2054 bnoagg 0.5693066906
## 2055 bnoagg 0.4373467508
## 2056 bnoagg 0.7682349349
## 2057 bnoagg 1.3732352903
## 2058 bnoagg 1.4685053344
## 2059 bnoagg 0.3779948649
## 2060 bnoagg 0.3743246073
## 2061 bnoagg 0.0643629253
## 2062 bnoagg 1.0532668557
## 2063 bnoagg 2.3388278920
## 2064 bnoagg 0.5660444043
## 2065 bnoagg 0.3477153343
## 2066 bnoagg 1.8982664376
## 2067 bnoagg 0.8278587076
## 2068 bnoagg 0.9809423323
## 2069 bnoagg 2.1952225836
## 2070 bnoagg 1.1268075893
## 2071 bnoagg 0.2291585510
## 2072 bnoagg 1.6379282330
## 2073 bnoagg 0.7826980476
## 2074 bnoagg 1.1294201302
## 2075 bnoagg 2.2012944881
## 2076 bnoagg 1.5738272640
## 2077 bnoagg 1.0931653150
## 2078 bnoagg 0.8388042629
## 2079 bnoagg 1.0262603088
## 2080 bnoagg 1.1327886930
## 2081 bnoagg 1.7665563981
## 2082 bnoagg 1.0848087568
## 2083 bnoagg 0.9097753060
## 2084 bnoagg 0.9586771661
## 2085 bnoagg 1.4408181577
## 2086 bnoagg 1.2539053496
## 2087 bnoagg 0.6671904762
## 2088 bnoagg 1.3231535121
## 2089 bnoagg 1.1389040171
## 2090 bnoagg 1.3872460136
## 2091 bnoagg 0.5610045875
## 2092 bnoagg 1.8281539621
## 2093 bnoagg 1.5807839276
## 2094 bnoagg 1.3597883864
## 2095 bnoagg 0.4882277433
## 2096 bnoagg 0.8758944812
## 2097 bnoagg 1.3416373380
## 2098 bnoagg 1.0957601302
## 2099 bnoagg 1.2974716409
## 2100 bnoagg 2.0043696865
## 2101 bnoagg 1.6187390974
## 2102 bnoagg 1.2564085647
## 2103 bnoagg 1.0011138713
## 2104 bnoagg 1.8317469137
## 2105 bnoagg 0.9936661458
## 2106 bnoagg 0.4708174728
## 2107 bnoagg -0.1377832729
## 2108 bnoagg 0.9475711559
## 2109 bnoagg 1.6108014415
## 2110 bnoagg 2.3168862137
## 2111 bnoagg 1.8600911459
## 2112 bnoagg 1.9443821837
## 2113 bnoagg 0.9573298753
## 2114 bnoagg 0.8302552558
## 2115 bnoagg 0.8651189526
## 2116 bnoagg 1.8553394497
## 2117 bnoagg 1.3976396632
## 2118 bnoagg 1.4482546648
## 2119 bnoagg 2.0374671218
## 2120 bnoagg 1.3933420625
## 2121 bnoagg 1.0050227023
## 2122 bnoagg 0.7191745250
## 2123 bnoagg 0.5699400034
## 2124 bnoagg 1.6531864438
## 2125 bnoagg 0.8717330776
## 2126 bnoagg 0.7324663346
## 2127 bnoagg 0.4614365024
## 2128 bnoagg 0.7005244942
## 2129 bnoagg 1.6294862093
## 2130 bnoagg 0.5545921449
## 2131 bnoagg -0.4300339183
## 2132 bnoagg 0.5302936491
## 2133 bnoagg 0.0680727437
## 2134 bnoagg 0.6853227293
## 2135 bnoagg 1.4059749554
## 2136 bnoagg 1.3489828893
## 2137 bnoagg 0.7128183175
## 2138 bnoagg 0.9808436972
## 2139 bnoagg 2.0029750680
## 2140 bnoagg 1.3437824212
## 2141 bnoagg 1.0211146509
## 2142 bnoagg 0.9318199676
## 2143 bnoagg 0.9744648254
## 2144 bnoagg 0.5976618907
## 2145 bnoagg 1.0973942566
## 2146 bnoagg 2.1179096549
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## 7523 bmean 1.8739566440
## 7524 bmean 0.7788881595
## 7525 bmean 1.0525524833
## 7526 bmean 1.6647271805
## 7527 bmean 1.7660690013
## 7528 bmean 1.6995998101
## 7529 bmean 1.0506381301
## 7530 bmean 1.3602593861
## 7531 bmean 1.5658503146
## 7532 bmean 2.5410868549
## 7533 bmean 1.1963716405
## 7534 bmean 1.1999358932
## 7535 bmean 1.5283384740
## 7536 bmean 1.5691397662
## 7537 bmean 3.0485468034
## 7538 bmean 1.8655444745
## 7539 bmean 1.4735164482
## 7540 bmean 1.3384490340
## 7541 bmean 1.1754995423
## 7542 bmean 1.1429896757
## 7543 bmean 0.7473608829
## 7544 bmean 1.0093510049
## 7545 bmean 1.2599938053
## 7546 bmean 2.2125969448
## 7547 bmean 1.5537803182
## 7548 bmean 2.1072817905
## 7549 bmean 1.1495121834
## 7550 bmean 2.0871256152
## 7551 bmean 0.6672420246
## 7552 bmean 1.4647059129
## 7553 bmean 1.0581605502
## 7554 bmean 1.3623871429
## 7555 bmean 2.8665557176
## 7556 bmean 0.6967423728
## 7557 bmean 1.4536200032
## 7558 bmean 1.0571099477
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## 7560 bmean 1.0249340561
## 7561 bmean 0.2940705165
## 7562 bmean 1.2024100565
## 7563 bmean 1.8097365354
## 7564 bmean 1.0018586108
## 7565 bmean 0.9606036141
## 7566 bmean 1.7488571720
## 7567 bmean 1.9721964543
## 7568 bmean 0.8161145383
## 7569 bmean 1.5061502640
## 7570 bmean 2.4320177080
## 7571 bmean 1.2004188098
## 7572 bmean 1.5130938947
## 7573 bmean 1.7124496926
## 7574 bmean 2.3430760276
## 7575 bmean 1.4182183905
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## 7577 bmean 2.0440057185
## 7578 bmean 1.2227112919
## 7579 bmean 1.5049548672
## 7580 bmean 1.6020373054
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## 7584 bmean 1.1815038516
## 7585 bmean 1.4761969167
## 7586 bmean 1.8811683175
## 7587 bmean 1.9087957137
## 7588 bmean 1.6545926336
## 7589 bmean 0.8934583704
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## 7612 bmean 1.9074757284
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## 7618 bmean 1.7784821579
## 7619 bmean 1.4098480861
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## 7622 bmean 1.5403791552
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## 7625 bmean 0.5028049005
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## 7851 bmean 1.3471211058
## 7852 bmean 2.2567118004
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## 7865 bmean 1.1128243423
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## 7867 bmean 0.6875810610
## 7868 bmean 1.2726147896
## 7869 bmean 1.3122727285
## 7870 bmean 1.7390632080
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## 7874 bmean 1.8794409295
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## 7876 bmean 1.7424909627
## 7877 bmean 2.6947363643
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## 7891 bmean 2.2106191340
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## 7900 bmean 2.3605706254
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## 7903 bmean 2.1644131831
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## 7905 bmean 1.6559564588
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## 7907 bmean 1.1603112453
## 7908 bmean 1.3003663820
## 7909 bmean 2.0015802218
## 7910 bmean 0.8826109503
## 7911 bmean 2.3531757821
## 7912 bmean 1.9171359049
## 7913 bmean 1.2043803914
## 7914 bmean 1.8696013900
## 7915 bmean 0.7778359547
## 7916 bmean 1.2592795384
## 7917 bmean 1.9221841941
## 7918 bmean 1.3814928188
## 7919 bmean 1.6256871815
## 7920 bmean 1.4368976130
## 7921 bmean 1.6107305724
## 7922 bmean 1.7264570942
## 7923 bmean 1.8043778653
## 7924 bmean 1.4395688297
## 7925 bmean 2.2504674743
## 7926 bmean 1.2029055963
## 7927 bmean 1.2141822408
## 7928 bmean 0.8417957724
## 7929 bmean 1.5683278636
## 7930 bmean 2.2825277861
## 7931 bmean 1.1872223801
## 7932 bmean 1.2923250220
## 7933 bmean 1.3049820831
## 7934 bmean 1.2013659650
## 7935 bmean 1.0497021454
## 7936 bmean 1.1889765858
## 7937 bmean 1.2502509201
## 7938 bmean 1.2584201946
## 7939 bmean 1.5225811144
## 7940 bmean 1.9341981199
## 7941 bmean 1.0596776672
## 7942 bmean 1.7991307829
## 7943 bmean 1.1313559726
## 7944 bmean 1.7196202403
## 7945 bmean 1.7325326362
## 7946 bmean 2.2764256742
## 7947 bmean 0.6264574249
## 7948 bmean 2.1578401441
## 7949 bmean 1.9428165886
## 7950 bmean 1.6731347844
## 7951 bmean 1.0054002853
## 7952 bmean 1.7873987346
## 7953 bmean 0.9013061040
## 7954 bmean 2.0046318894
## 7955 bmean 0.8212305040
## 7956 bmean 0.8684156993
## 7957 bmean 1.5221813661
## 7958 bmean 0.4486028235
## 7959 bmean 1.3273474693
## 7960 bmean 1.5212085941
## 7961 bmean 1.9115042927
## 7962 bmean 1.1378189521
## 7963 bmean 1.3326096208
## 7964 bmean 1.7874141875
## 7965 bmean -0.0888076062
## 7966 bmean 1.5715386187
## 7967 bmean 1.0442752002
## 7968 bmean 0.5219152879
## 7969 bmean 1.7385348973
## 7970 bmean 1.0250219857
## 7971 bmean 0.9288076529
## 7972 bmean 0.7286746535
## 7973 bmean 1.9332904302
## 7974 bmean 0.7004889086
## 7975 bmean 0.6099493032
## 7976 bmean 1.2432878828
## 7977 bmean 1.8992681255
## 7978 bmean 2.1688676537
## 7979 bmean 1.4303077272
## 7980 bmean 1.1903955867
## 7981 bmean 2.3150895501
## 7982 bmean 2.2065533380
## 7983 bmean 2.1772348790
## 7984 bmean 1.0161049076
## 7985 bmean 1.7788313795
## 7986 bmean 1.6421148002
## 7987 bmean 2.1719984651
## 7988 bmean 1.5637304912
## 7989 bmean 1.3030084197
## 7990 bmean 0.7330486674
## 7991 bmean 1.1427625986
## 7992 bmean 1.0896709448
## 7993 bmean 0.7908302076
## 7994 bmean 1.6008358221
## 7995 bmean 1.7223453083
## 7996 bmean 0.7382243516
## 7997 bmean 1.7010080761
## 7998 bmean 1.0996678234
## 7999 bmean 2.4575183750
## 8000 bmean 2.8228385772
## Subset our original data frame based on the removing the null effect values,
## then calculate a 95% confidence interval for error bars
df_50 %>%
subset(df_50$effectSizeMagnitudeRemoveNulls == FALSE, na.rm=TRUE) %>%
ddply(.(workerId, aggStrat), summarise,
TotalIsEffect=sum(effectSizeMagnitude==TRUE),
total=sum(correct==TRUE)+sum(correct==FALSE),
correct=sum(correct==TRUE),
acc=correct/total) %>%
ddply(~aggStrat, summarise,
N = sum((total)),
meanAcc = mean(acc),
sd = sd(acc),
se = sd / sqrt(N))## aggStrat N meanAcc sd se
## 1 disagg 537 0.4957530 0.2337081 0.01008525
## 2 disagg+mean 538 0.5272777 0.2449560 0.01056080
## 3 mean 494 0.4999401 0.2824277 0.01270702
Dichotomous Descriptive Statistics
We do an exploratory analysis of a code to investigate generalizations that do not include a magnitude of effect, but infer an effect or not (i.e. “Number of time on sites is pretty unrelated to the number of visits” or “Ad Campaign B resulted in the greatest number of purchases”). We find that the mean aggregation strategy has the highest rate of dichotomous thinking, at 49%, vs disaggregation with means at 46%, and finally disaggregation at 37%. This supports our hypothesis that there will be a higher rate of generalizations coded as dichotomous under the mean aggregation condition.
df_50 %>%
subset(df_50$dichotomous == TRUE, na.rm=TRUE) %>%
ddply(.(aggStrat), summarise,
total=sum(correct==TRUE)+sum(correct==FALSE),
percentOfTotal=total/618,
Correct=sum(correct==TRUE),
acc=Correct/total) # hard coded from nrow of subset(df_50$dichotomous == TRUE, na.rm=TRUE)## aggStrat total percentOfTotal Correct acc
## 1 disagg 234 0.3786408 96 0.4102564
## 2 disagg+mean 234 0.3786408 116 0.4957265
## 3 mean 339 0.5485437 176 0.5191740## Mean accuracy with standard error, taking into account differences between workers
df_50 %>%
subset(df_50$dichotomous == TRUE, na.rm=TRUE) %>%
ddply(.(workerId, aggStrat), summarise,
TotalIsEffect=sum(effectSizeMagnitude==TRUE),
total=sum(correct==TRUE)+sum(correct==FALSE),
correct=sum(correct==TRUE),
accuracy=correct/total) %>%
ddply(~aggStrat, summarise,
N = sum((total)),
meanAccuracy = mean(accuracy),
sd = sd(accuracy),
se = sd / sqrt(N))## aggStrat N meanAccuracy sd se
## 1 disagg 234 0.3715507 0.3415581 0.02232835
## 2 disagg+mean 234 0.4620956 0.3671058 0.02399846
## 3 mean 339 0.4948591 0.3458809 0.01878567## Using aggStrat as id variables
Dichotomous Bayesian Model
## Mean StdDev lower 0.95 upper 0.95 n_eff Rhat
## bnoagg -1.785516 0.1994063 -2.178903 -1.407494 5123.981 1.000024
## bmean -1.732061 0.1990793 -2.127718 -1.352198 4968.842 1.000038## 105 vector or matrix parameters omitted in display. Use depth=2 to show them.## Mean StdDev lower 0.95 upper 0.95 n_eff Rhat
## bnoagg 0.1677104 1.220678 0.1131656 0.2447559 Inf 2.718347
## bmean 0.1769194 1.220279 0.1191087 0.2586711 Inf 2.718385## a_worker[1] a_worker[2] a_worker[3] a_worker[4] a_worker[5] a_worker[6]
## 1.25313827 1.79262395 0.79993725 1.49477515 0.98793183 0.57730640
## a_worker[7] a_worker[8] a_worker[9] a_worker[10] a_worker[11] a_worker[12]
## 0.57383496 0.84090377 1.61848927 0.43892260 0.58318687 0.71335822
## a_worker[13] a_worker[14] a_worker[15] a_worker[16] a_worker[17] a_worker[18]
## 0.40796421 1.39022173 1.45698819 1.81713463 1.18700755 0.59156591
## a_worker[19] a_worker[20] a_worker[21] a_worker[22] a_worker[23] a_worker[24]
## 0.82940768 1.02057893 0.61622120 0.94961321 1.01918181 0.92329368
## a_worker[25] a_worker[26] a_worker[27] a_worker[28] a_worker[29] a_worker[30]
## 1.65951557 0.92426295 0.69680381 1.16279267 1.71688401 0.61359002
## a_worker[31] a_worker[32] a_worker[33] a_worker[34] a_worker[35] a_worker[36]
## 0.51580283 1.14481044 0.80941065 1.71024539 0.97291282 0.64581534
## a_worker[37] a_worker[38] a_worker[39] a_worker[40] a_worker[41] a_worker[42]
## 1.34563428 1.92982729 1.05725830 0.82936006 0.67265401 1.01437694
## a_worker[43] a_worker[44] a_worker[45] a_worker[46] a_worker[47] a_worker[48]
## 1.27299942 1.42889638 0.73040201 1.60304784 0.57517524 0.67033305
## a_worker[49] a_worker[50] a_worker[51] a_worker[52] a_worker[53] a_worker[54]
## 1.33177720 0.68011781 1.01241557 1.47328950 1.24986297 1.53979114
## a_worker[55] a_worker[56] a_worker[57] a_worker[58] a_worker[59] a_worker[60]
## 0.90755187 0.36958212 1.32599530 1.14805872 0.86760254 1.06837950
## a_worker[61] a_worker[62] a_worker[63] a_worker[64] a_worker[65] a_worker[66]
## 1.66573184 1.48433264 0.99182054 0.47429342 1.31777672 1.30262698
## a_worker[67] a_worker[68] a_worker[69] a_worker[70] a_worker[71] a_worker[72]
## 1.74361746 2.01428838 1.04576917 2.25534382 1.09479294 1.33252644
## a_worker[73] a_worker[74] a_worker[75] a_worker[76] a_worker[77] a_worker[78]
## 0.60534718 1.65097839 1.02202587 0.87129700 0.94369787 2.04448542
## a_worker[79] a_worker[80] a_worker[81] a_worker[82] a_worker[83] a_worker[84]
## 1.00789184 0.64417670 0.91320009 1.23782432 2.19056236 1.47810217
## a_worker[85] a_worker[86] a_worker[87] a_worker[88] a_worker[89] a_worker[90]
## 1.29422465 1.71418129 0.18201284 1.95389080 0.16710104 0.77364525
## a_spec[1] a_spec[2] a_spec[3] a_spec[4] a_spec[5] a_spec[6]
## 0.02665719 0.01637667 0.04767761 0.07734404 0.06067743 9.63080871
## a_spec[7] a_spec[8] a_spec[9] a_spec[10] a_spec[11] a_spec[12]
## 6.66566899 11.29841459 6.22805256 7.88781472 1.37650985 6.87109716
## a_spec[13] a_spec[14] a_spec[15] a bnoagg bmean
## 2.27692948 5.46209192 2.62215314 1.90546866 0.16771044 0.17691942
## btrial sigma_worker sigma_spec
## 0.99467071 2.02358309 12.64283629## 105 vector or matrix parameters omitted in display. Use depth=2 to show them.## item value
## 1 bnoagg -1.907294
## 2 bnoagg -1.782195
## 3 bnoagg -1.972316
## 4 bnoagg -1.828859
## 5 bnoagg -1.874933
## 6 bnoagg -1.558789
## 7 bnoagg -1.910034
## 8 bnoagg -1.283908
## 9 bnoagg -2.038172
## 10 bnoagg -1.860858
## 11 bnoagg -1.672640
## 12 bnoagg -2.106181
## 13 bnoagg -1.621742
## 14 bnoagg -2.056701
## 15 bnoagg -1.821354
## 16 bnoagg -1.490395
## 17 bnoagg -1.561074
## 18 bnoagg -1.949443
## 19 bnoagg -1.814256
## 20 bnoagg -2.194886
## 21 bnoagg -1.841366
## 22 bnoagg -2.058451
## 23 bnoagg -1.810604
## 24 bnoagg -1.943964
## 25 bnoagg -1.702694
## 26 bnoagg -1.683232
## 27 bnoagg -1.788928
## 28 bnoagg -1.847856
## 29 bnoagg -1.673789
## 30 bnoagg -2.046704
## 31 bnoagg -1.373387
## 32 bnoagg -1.564549
## 33 bnoagg -1.909481
## 34 bnoagg -1.641370
## 35 bnoagg -1.788699
## 36 bnoagg -2.051093
## 37 bnoagg -2.055297
## 38 bnoagg -1.962552
## 39 bnoagg -1.739743
## 40 bnoagg -1.993334
## 41 bnoagg -1.536003
## 42 bnoagg -1.621197
## 43 bnoagg -1.635573
## 44 bnoagg -1.842414
## 45 bnoagg -1.495014
## 46 bnoagg -1.505528
## 47 bnoagg -2.268684
## 48 bnoagg -1.855685
## 49 bnoagg -2.139518
## 50 bnoagg -1.542722
## 51 bnoagg -1.749681
## 52 bnoagg -1.809091
## 53 bnoagg -2.174185
## 54 bnoagg -1.737070
## 55 bnoagg -1.646540
## 56 bnoagg -2.046910
## 57 bnoagg -1.663080
## 58 bnoagg -1.991962
## 59 bnoagg -1.645904
## 60 bnoagg -1.739599
## 61 bnoagg -1.764624
## 62 bnoagg -1.971407
## 63 bnoagg -2.031818
## 64 bnoagg -2.165070
## 65 bnoagg -1.786928
## 66 bnoagg -1.561639
## 67 bnoagg -1.826136
## 68 bnoagg -1.710751
## 69 bnoagg -1.527556
## 70 bnoagg -1.903689
## 71 bnoagg -1.799589
## 72 bnoagg -1.816657
## 73 bnoagg -1.709268
## 74 bnoagg -1.664437
## 75 bnoagg -1.559752
## 76 bnoagg -1.699439
## 77 bnoagg -1.687565
## 78 bnoagg -1.918570
## 79 bnoagg -1.704981
## 80 bnoagg -1.873594
## 81 bnoagg -1.749613
## 82 bnoagg -1.796299
## 83 bnoagg -1.884579
## 84 bnoagg -1.800748
## 85 bnoagg -1.367595
## 86 bnoagg -1.795939
## 87 bnoagg -1.597076
## 88 bnoagg -1.767149
## 89 bnoagg -1.774035
## 90 bnoagg -1.854074
## 91 bnoagg -1.649864
## 92 bnoagg -2.111559
## 93 bnoagg -1.869305
## 94 bnoagg -1.943039
## 95 bnoagg -1.539314
## 96 bnoagg -1.460859
## 97 bnoagg -1.816542
## 98 bnoagg -1.296912
## 99 bnoagg -1.741402
## 100 bnoagg -1.847576
## 101 bnoagg -1.341103
## 102 bnoagg -1.652683
## 103 bnoagg -2.198442
## 104 bnoagg -1.533530
## 105 bnoagg -1.807663
## 106 bnoagg -1.658972
## 107 bnoagg -1.717360
## 108 bnoagg -1.629006
## 109 bnoagg -2.279550
## 110 bnoagg -1.899396
## 111 bnoagg -2.031395
## 112 bnoagg -1.559707
## 113 bnoagg -1.595045
## 114 bnoagg -1.889706
## 115 bnoagg -1.825626
## 116 bnoagg -1.765610
## 117 bnoagg -1.958345
## 118 bnoagg -1.433861
## 119 bnoagg -1.400940
## 120 bnoagg -2.022839
## 121 bnoagg -1.902777
## 122 bnoagg -2.033524
## 123 bnoagg -1.658571
## 124 bnoagg -2.006282
## 125 bnoagg -1.917045
## 126 bnoagg -1.983204
## 127 bnoagg -1.870162
## 128 bnoagg -1.739680
## 129 bnoagg -2.229044
## 130 bnoagg -2.143609
## 131 bnoagg -1.502421
## 132 bnoagg -1.977420
## 133 bnoagg -2.037921
## 134 bnoagg -2.006032
## 135 bnoagg -2.397048
## 136 bnoagg -1.691954
## 137 bnoagg -1.846918
## 138 bnoagg -1.733040
## 139 bnoagg -1.608864
## 140 bnoagg -2.193847
## 141 bnoagg -1.635905
## 142 bnoagg -1.906014
## 143 bnoagg -1.833038
## 144 bnoagg -1.796794
## 145 bnoagg -2.067187
## 146 bnoagg -1.806151
## 147 bnoagg -1.942053
## 148 bnoagg -1.460258
## 149 bnoagg -1.844048
## 150 bnoagg -1.910940
## 151 bnoagg -1.828958
## 152 bnoagg -1.684855
## 153 bnoagg -1.408037
## 154 bnoagg -1.725853
## 155 bnoagg -1.448846
## 156 bnoagg -1.654350
## 157 bnoagg -2.019405
## 158 bnoagg -1.837855
## 159 bnoagg -1.679179
## 160 bnoagg -2.019883
## 161 bnoagg -1.719917
## 162 bnoagg -1.615872
## 163 bnoagg -1.659043
## 164 bnoagg -1.821504
## 165 bnoagg -1.515715
## 166 bnoagg -1.670441
## 167 bnoagg -2.311710
## 168 bnoagg -1.652815
## 169 bnoagg -2.210696
## 170 bnoagg -1.859169
## 171 bnoagg -2.019229
## 172 bnoagg -1.570428
## 173 bnoagg -2.067898
## 174 bnoagg -1.640678
## 175 bnoagg -1.786940
## 176 bnoagg -2.062861
## 177 bnoagg -1.848900
## 178 bnoagg -1.938566
## 179 bnoagg -1.708732
## 180 bnoagg -1.912081
## 181 bnoagg -1.829723
## 182 bnoagg -1.500318
## 183 bnoagg -1.931624
## 184 bnoagg -2.117346
## 185 bnoagg -1.931196
## 186 bnoagg -2.014465
## 187 bnoagg -1.872360
## 188 bnoagg -1.558590
## 189 bnoagg -1.777569
## 190 bnoagg -1.720439
## 191 bnoagg -2.129154
## 192 bnoagg -2.250552
## 193 bnoagg -2.010272
## 194 bnoagg -1.902901
## 195 bnoagg -1.591634
## 196 bnoagg -1.977989
## 197 bnoagg -1.362917
## 198 bnoagg -1.521644
## 199 bnoagg -1.621590
## 200 bnoagg -1.761660
## 201 bnoagg -1.541357
## 202 bnoagg -1.535484
## 203 bnoagg -2.049799
## 204 bnoagg -2.045491
## 205 bnoagg -1.419544
## 206 bnoagg -1.822767
## 207 bnoagg -1.831541
## 208 bnoagg -1.800949
## 209 bnoagg -1.494640
## 210 bnoagg -1.830811
## 211 bnoagg -1.674909
## 212 bnoagg -1.642804
## 213 bnoagg -2.205585
## 214 bnoagg -1.487405
## 215 bnoagg -1.646833
## 216 bnoagg -1.927550
## 217 bnoagg -1.575031
## 218 bnoagg -1.899269
## 219 bnoagg -1.418166
## 220 bnoagg -1.996752
## 221 bnoagg -1.579748
## 222 bnoagg -1.589105
## 223 bnoagg -1.585621
## 224 bnoagg -1.647984
## 225 bnoagg -2.125502
## 226 bnoagg -1.812802
## 227 bnoagg -1.632706
## 228 bnoagg -1.590699
## 229 bnoagg -1.958787
## 230 bnoagg -1.995099
## 231 bnoagg -1.888342
## 232 bnoagg -1.564161
## 233 bnoagg -2.066207
## 234 bnoagg -1.757779
## 235 bnoagg -1.832717
## 236 bnoagg -1.841227
## 237 bnoagg -1.515706
## 238 bnoagg -1.943102
## 239 bnoagg -2.179467
## 240 bnoagg -2.062873
## 241 bnoagg -1.665636
## 242 bnoagg -1.741053
## 243 bnoagg -1.831104
## 244 bnoagg -2.055503
## 245 bnoagg -1.492270
## 246 bnoagg -1.586928
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## 250 bnoagg -1.853655
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## 252 bnoagg -1.792155
## 253 bnoagg -1.826115
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## 255 bnoagg -1.659797
## 256 bnoagg -1.748016
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## 258 bnoagg -1.901968
## 259 bnoagg -1.711712
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## 261 bnoagg -1.707717
## 262 bnoagg -2.020781
## 263 bnoagg -1.952071
## 264 bnoagg -1.609072
## 265 bnoagg -1.716774
## 266 bnoagg -1.562000
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## 268 bnoagg -1.759311
## 269 bnoagg -1.801501
## 270 bnoagg -1.799298
## 271 bnoagg -1.500400
## 272 bnoagg -1.700833
## 273 bnoagg -1.971480
## 274 bnoagg -1.675281
## 275 bnoagg -1.951384
## 276 bnoagg -1.643530
## 277 bnoagg -1.863447
## 278 bnoagg -1.740561
## 279 bnoagg -2.050426
## 280 bnoagg -1.673333
## 281 bnoagg -1.938075
## 282 bnoagg -1.868280
## 283 bnoagg -1.774528
## 284 bnoagg -1.724514
## 285 bnoagg -1.555505
## 286 bnoagg -1.987560
## 287 bnoagg -1.909080
## 288 bnoagg -1.856489
## 289 bnoagg -1.507233
## 290 bnoagg -2.011361
## 291 bnoagg -2.095808
## 292 bnoagg -1.871397
## 293 bnoagg -1.895638
## 294 bnoagg -2.010509
## 295 bnoagg -2.011787
## 296 bnoagg -2.008535
## 297 bnoagg -1.795936
## 298 bnoagg -1.894520
## 299 bnoagg -2.199696
## 300 bnoagg -1.726566
## 301 bnoagg -2.020100
## 302 bnoagg -1.991442
## 303 bnoagg -1.764131
## 304 bnoagg -1.563934
## 305 bnoagg -1.826368
## 306 bnoagg -1.553034
## 307 bnoagg -2.123422
## 308 bnoagg -1.874628
## 309 bnoagg -1.984001
## 310 bnoagg -1.873084
## 311 bnoagg -2.421247
## 312 bnoagg -1.933556
## 313 bnoagg -1.567067
## 314 bnoagg -2.162661
## 315 bnoagg -1.295947
## 316 bnoagg -1.386540
## 317 bnoagg -1.887965
## 318 bnoagg -1.840709
## 319 bnoagg -1.891733
## 320 bnoagg -1.854156
## 321 bnoagg -1.731159
## 322 bnoagg -1.732354
## 323 bnoagg -1.762626
## 324 bnoagg -1.852412
## 325 bnoagg -2.134106
## 326 bnoagg -2.115603
## 327 bnoagg -1.622779
## 328 bnoagg -1.482126
## 329 bnoagg -1.537804
## 330 bnoagg -1.766497
## 331 bnoagg -1.628177
## 332 bnoagg -1.488018
## 333 bnoagg -1.301723
## 334 bnoagg -1.374807
## 335 bnoagg -2.138119
## 336 bnoagg -1.873872
## 337 bnoagg -2.271997
## 338 bnoagg -1.609199
## 339 bnoagg -2.187764
## 340 bnoagg -1.547696
## 341 bnoagg -1.539218
## 342 bnoagg -1.734176
## 343 bnoagg -1.906834
## 344 bnoagg -1.713983
## 345 bnoagg -1.575802
## 346 bnoagg -1.905133
## 347 bnoagg -1.904229
## 348 bnoagg -1.637026
## 349 bnoagg -1.812753
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## 352 bnoagg -1.900187
## 353 bnoagg -1.744037
## 354 bnoagg -1.921902
## 355 bnoagg -1.618563
## 356 bnoagg -1.866731
## 357 bnoagg -1.838657
## 358 bnoagg -1.414993
## 359 bnoagg -1.695338
## 360 bnoagg -1.985274
## 361 bnoagg -1.652531
## 362 bnoagg -1.995457
## 363 bnoagg -1.566105
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## 365 bnoagg -2.011512
## 366 bnoagg -1.875516
## 367 bnoagg -2.056033
## 368 bnoagg -1.764677
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## 372 bnoagg -1.805306
## 373 bnoagg -1.865365
## 374 bnoagg -1.813920
## 375 bnoagg -1.582649
## 376 bnoagg -1.673779
## 377 bnoagg -1.831158
## 378 bnoagg -1.631837
## 379 bnoagg -1.614615
## 380 bnoagg -1.675551
## 381 bnoagg -1.726875
## 382 bnoagg -1.658983
## 383 bnoagg -1.483600
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## 385 bnoagg -1.957177
## 386 bnoagg -1.817682
## 387 bnoagg -1.835140
## 388 bnoagg -1.571995
## 389 bnoagg -1.751134
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## 394 bnoagg -1.566298
## 395 bnoagg -1.689107
## 396 bnoagg -1.846942
## 397 bnoagg -2.007096
## 398 bnoagg -2.337933
## 399 bnoagg -1.532787
## 400 bnoagg -1.663358
## 401 bnoagg -1.661620
## 402 bnoagg -1.765494
## 403 bnoagg -1.833723
## 404 bnoagg -1.616741
## 405 bnoagg -1.600042
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## 407 bnoagg -1.743559
## 408 bnoagg -1.566702
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## 410 bnoagg -1.906582
## 411 bnoagg -1.675090
## 412 bnoagg -2.009665
## 413 bnoagg -1.615263
## 414 bnoagg -1.721901
## 415 bnoagg -1.856772
## 416 bnoagg -1.704638
## 417 bnoagg -1.836464
## 418 bnoagg -1.637820
## 419 bnoagg -2.012285
## 420 bnoagg -1.686009
## 421 bnoagg -1.643232
## 422 bnoagg -2.103187
## 423 bnoagg -1.702564
## 424 bnoagg -1.553858
## 425 bnoagg -1.611605
## 426 bnoagg -1.824147
## 427 bnoagg -1.789015
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## 429 bnoagg -1.768029
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## 456 bnoagg -1.540842
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## 473 bnoagg -1.461422
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## 475 bnoagg -1.732839
## 476 bnoagg -2.031786
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## 485 bnoagg -1.946052
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## 502 bnoagg -1.591233
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## 504 bnoagg -1.616371
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## 536 bnoagg -1.716019
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## 573 bnoagg -1.947301
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## 575 bnoagg -1.514935
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## 585 bnoagg -1.797250
## 586 bnoagg -1.787469
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## 588 bnoagg -1.559474
## 589 bnoagg -1.396089
## 590 bnoagg -1.699184
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## 1794 bnoagg -1.890415
## 1795 bnoagg -1.780154
## 1796 bnoagg -1.561657
## 1797 bnoagg -1.724652
## 1798 bnoagg -1.662841
## 1799 bnoagg -1.463226
## 1800 bnoagg -1.659785
## 1801 bnoagg -1.624440
## 1802 bnoagg -1.638088
## 1803 bnoagg -1.787251
## 1804 bnoagg -1.608915
## 1805 bnoagg -1.790751
## 1806 bnoagg -1.531924
## 1807 bnoagg -1.862072
## 1808 bnoagg -2.036181
## 1809 bnoagg -1.991360
## 1810 bnoagg -2.144635
## 1811 bnoagg -1.724350
## 1812 bnoagg -1.846774
## 1813 bnoagg -1.866008
## 1814 bnoagg -1.866603
## 1815 bnoagg -1.673627
## 1816 bnoagg -1.698387
## 1817 bnoagg -2.087115
## 1818 bnoagg -1.862765
## 1819 bnoagg -1.822168
## 1820 bnoagg -1.564911
## 1821 bnoagg -1.824172
## 1822 bnoagg -1.495198
## 1823 bnoagg -1.876040
## 1824 bnoagg -1.486204
## 1825 bnoagg -1.938496
## 1826 bnoagg -1.571768
## 1827 bnoagg -2.100032
## 1828 bnoagg -1.978940
## 1829 bnoagg -1.710881
## 1830 bnoagg -2.072328
## 1831 bnoagg -1.846218
## 1832 bnoagg -1.446508
## 1833 bnoagg -1.976585
## 1834 bnoagg -1.638625
## 1835 bnoagg -1.762352
## 1836 bnoagg -1.847438
## 1837 bnoagg -2.182061
## 1838 bnoagg -1.708125
## 1839 bnoagg -2.288643
## 1840 bnoagg -1.967155
## 1841 bnoagg -2.194344
## 1842 bnoagg -1.787880
## 1843 bnoagg -1.556136
## 1844 bnoagg -1.665272
## 1845 bnoagg -1.733156
## 1846 bnoagg -1.698921
## 1847 bnoagg -1.957571
## 1848 bnoagg -1.744861
## 1849 bnoagg -1.738973
## 1850 bnoagg -1.561091
## 1851 bnoagg -1.833485
## 1852 bnoagg -1.713580
## 1853 bnoagg -1.608783
## 1854 bnoagg -1.930219
## 1855 bnoagg -2.168396
## 1856 bnoagg -2.123635
## 1857 bnoagg -1.602963
## 1858 bnoagg -1.546192
## 1859 bnoagg -1.681702
## 1860 bnoagg -1.801262
## 1861 bnoagg -2.053445
## 1862 bnoagg -1.528437
## 1863 bnoagg -1.895238
## 1864 bnoagg -2.125166
## 1865 bnoagg -1.738703
## 1866 bnoagg -1.348177
## 1867 bnoagg -1.657788
## 1868 bnoagg -1.656590
## 1869 bnoagg -1.908896
## 1870 bnoagg -1.586210
## 1871 bnoagg -1.670658
## 1872 bnoagg -1.810799
## 1873 bnoagg -1.877166
## 1874 bnoagg -1.585700
## 1875 bnoagg -2.024928
## 1876 bnoagg -1.856847
## 1877 bnoagg -1.518457
## 1878 bnoagg -1.963515
## 1879 bnoagg -1.413734
## 1880 bnoagg -1.631157
## 1881 bnoagg -1.827922
## 1882 bnoagg -1.977145
## 1883 bnoagg -1.675362
## 1884 bnoagg -1.878568
## 1885 bnoagg -1.906822
## 1886 bnoagg -1.744372
## 1887 bnoagg -1.771991
## 1888 bnoagg -1.475090
## 1889 bnoagg -1.971102
## 1890 bnoagg -1.546788
## 1891 bnoagg -1.818283
## 1892 bnoagg -2.290133
## 1893 bnoagg -1.834892
## 1894 bnoagg -1.635229
## 1895 bnoagg -1.643625
## 1896 bnoagg -1.705157
## 1897 bnoagg -1.952790
## 1898 bnoagg -1.721898
## 1899 bnoagg -1.872636
## 1900 bnoagg -1.541488
## 1901 bnoagg -1.712216
## 1902 bnoagg -1.643542
## 1903 bnoagg -1.740206
## 1904 bnoagg -2.121438
## 1905 bnoagg -1.960565
## 1906 bnoagg -1.611076
## 1907 bnoagg -1.918560
## 1908 bnoagg -1.895902
## 1909 bnoagg -1.681084
## 1910 bnoagg -1.500746
## 1911 bnoagg -1.459037
## 1912 bnoagg -1.647673
## 1913 bnoagg -1.827321
## 1914 bnoagg -1.905553
## 1915 bnoagg -1.688039
## 1916 bnoagg -1.598920
## 1917 bnoagg -1.507263
## 1918 bnoagg -1.609033
## 1919 bnoagg -1.657547
## 1920 bnoagg -1.991745
## 1921 bnoagg -1.560447
## 1922 bnoagg -1.673869
## 1923 bnoagg -1.445350
## 1924 bnoagg -1.703260
## 1925 bnoagg -1.664466
## 1926 bnoagg -1.812734
## 1927 bnoagg -1.734347
## 1928 bnoagg -1.956609
## 1929 bnoagg -1.959422
## 1930 bnoagg -1.827121
## 1931 bnoagg -2.094887
## 1932 bnoagg -1.428524
## 1933 bnoagg -2.156099
## 1934 bnoagg -1.673201
## 1935 bnoagg -1.957832
## 1936 bnoagg -1.939723
## 1937 bnoagg -2.070436
## 1938 bnoagg -1.807538
## 1939 bnoagg -1.924701
## 1940 bnoagg -1.795196
## 1941 bnoagg -1.658767
## 1942 bnoagg -2.029938
## 1943 bnoagg -1.682544
## 1944 bnoagg -1.588189
## 1945 bnoagg -1.675696
## 1946 bnoagg -1.624849
## 1947 bnoagg -1.935329
## 1948 bnoagg -1.747865
## 1949 bnoagg -2.269624
## 1950 bnoagg -1.661896
## 1951 bnoagg -2.246501
## 1952 bnoagg -1.754743
## 1953 bnoagg -1.666772
## 1954 bnoagg -1.547801
## 1955 bnoagg -1.670262
## 1956 bnoagg -1.682365
## 1957 bnoagg -1.917194
## 1958 bnoagg -1.912088
## 1959 bnoagg -2.058149
## 1960 bnoagg -1.638305
## 1961 bnoagg -1.765152
## 1962 bnoagg -1.973683
## 1963 bnoagg -2.090418
## 1964 bnoagg -1.696059
## 1965 bnoagg -1.833520
## 1966 bnoagg -1.446369
## 1967 bnoagg -1.606598
## 1968 bnoagg -1.474971
## 1969 bnoagg -1.523194
## 1970 bnoagg -2.008544
## 1971 bnoagg -1.945920
## 1972 bnoagg -1.782692
## 1973 bnoagg -1.921908
## 1974 bnoagg -1.762605
## 1975 bnoagg -1.569233
## 1976 bnoagg -1.520050
## 1977 bnoagg -1.917450
## 1978 bnoagg -1.765434
## 1979 bnoagg -1.841287
## 1980 bnoagg -1.614858
## 1981 bnoagg -2.011937
## 1982 bnoagg -1.651215
## 1983 bnoagg -1.795709
## 1984 bnoagg -1.579298
## 1985 bnoagg -2.058460
## 1986 bnoagg -2.263685
## 1987 bnoagg -1.756585
## 1988 bnoagg -1.769347
## 1989 bnoagg -1.890915
## 1990 bnoagg -1.673640
## 1991 bnoagg -1.708875
## 1992 bnoagg -1.734087
## 1993 bnoagg -1.656245
## 1994 bnoagg -1.816864
## 1995 bnoagg -2.175300
## 1996 bnoagg -1.888947
## 1997 bnoagg -1.584641
## 1998 bnoagg -1.734252
## 1999 bnoagg -1.552259
## 2000 bnoagg -2.052941
## 2001 bnoagg -1.778653
## 2002 bnoagg -1.936264
## 2003 bnoagg -1.786059
## 2004 bnoagg -1.632061
## 2005 bnoagg -2.085868
## 2006 bnoagg -1.623970
## 2007 bnoagg -1.923896
## 2008 bnoagg -1.891165
## 2009 bnoagg -1.746595
## 2010 bnoagg -1.405530
## 2011 bnoagg -1.599469
## 2012 bnoagg -1.849342
## 2013 bnoagg -1.649295
## 2014 bnoagg -1.582979
## 2015 bnoagg -1.828141
## 2016 bnoagg -1.834810
## 2017 bnoagg -1.796012
## 2018 bnoagg -2.123683
## 2019 bnoagg -1.757669
## 2020 bnoagg -1.845224
## 2021 bnoagg -2.024213
## 2022 bnoagg -1.928787
## 2023 bnoagg -1.480845
## 2024 bnoagg -1.640640
## 2025 bnoagg -1.775364
## 2026 bnoagg -1.667779
## 2027 bnoagg -1.609280
## 2028 bnoagg -2.173704
## 2029 bnoagg -1.902871
## 2030 bnoagg -1.779128
## 2031 bnoagg -1.737740
## 2032 bnoagg -1.654672
## 2033 bnoagg -1.547668
## 2034 bnoagg -1.609875
## 2035 bnoagg -1.632481
## 2036 bnoagg -1.684748
## 2037 bnoagg -1.473786
## 2038 bnoagg -1.392997
## 2039 bnoagg -1.937865
## 2040 bnoagg -1.751278
## 2041 bnoagg -1.472548
## 2042 bnoagg -1.830214
## 2043 bnoagg -1.952736
## 2044 bnoagg -1.610712
## 2045 bnoagg -1.828950
## 2046 bnoagg -1.657846
## 2047 bnoagg -1.910642
## 2048 bnoagg -1.721012
## 2049 bnoagg -1.582947
## 2050 bnoagg -1.864193
## 2051 bnoagg -2.096239
## 2052 bnoagg -1.780971
## 2053 bnoagg -1.705997
## 2054 bnoagg -1.855827
## 2055 bnoagg -1.542793
## 2056 bnoagg -1.731239
## 2057 bnoagg -1.758759
## 2058 bnoagg -1.469155
## 2059 bnoagg -1.774457
## 2060 bnoagg -1.618186
## 2061 bnoagg -1.656000
## 2062 bnoagg -1.981578
## 2063 bnoagg -1.621560
## 2064 bnoagg -1.815045
## 2065 bnoagg -1.963225
## 2066 bnoagg -1.806629
## 2067 bnoagg -1.783837
## 2068 bnoagg -1.963970
## 2069 bnoagg -1.827079
## 2070 bnoagg -1.509700
## 2071 bnoagg -2.048614
## 2072 bnoagg -1.694130
## 2073 bnoagg -1.510069
## 2074 bnoagg -1.837901
## 2075 bnoagg -1.609692
## 2076 bnoagg -1.609950
## 2077 bnoagg -1.907853
## 2078 bnoagg -1.842230
## 2079 bnoagg -1.893028
## 2080 bnoagg -2.074209
## 2081 bnoagg -1.825269
## 2082 bnoagg -1.372061
## 2083 bnoagg -1.675787
## 2084 bnoagg -1.652660
## 2085 bnoagg -1.697589
## 2086 bnoagg -1.725719
## 2087 bnoagg -1.630465
## 2088 bnoagg -1.792854
## 2089 bnoagg -1.948374
## 2090 bnoagg -1.777508
## 2091 bnoagg -1.822035
## 2092 bnoagg -1.764692
## 2093 bnoagg -2.079670
## 2094 bnoagg -1.796798
## 2095 bnoagg -2.321424
## 2096 bnoagg -1.937054
## 2097 bnoagg -1.866992
## 2098 bnoagg -2.096023
## 2099 bnoagg -1.905083
## 2100 bnoagg -1.522826
## 2101 bnoagg -2.078395
## 2102 bnoagg -1.583660
## 2103 bnoagg -1.703642
## 2104 bnoagg -1.484234
## 2105 bnoagg -1.468975
## 2106 bnoagg -1.726851
## 2107 bnoagg -1.806001
## 2108 bnoagg -1.921144
## 2109 bnoagg -2.111251
## 2110 bnoagg -1.744883
## 2111 bnoagg -1.285517
## 2112 bnoagg -1.599198
## 2113 bnoagg -1.527938
## 2114 bnoagg -1.807724
## 2115 bnoagg -1.895098
## 2116 bnoagg -1.689845
## 2117 bnoagg -1.702870
## 2118 bnoagg -1.866040
## 2119 bnoagg -2.111743
## 2120 bnoagg -1.587530
## 2121 bnoagg -1.259580
## 2122 bnoagg -1.903531
## 2123 bnoagg -2.057587
## 2124 bnoagg -1.889845
## 2125 bnoagg -1.964900
## 2126 bnoagg -1.844619
## 2127 bnoagg -1.666144
## 2128 bnoagg -2.200583
## 2129 bnoagg -1.788187
## 2130 bnoagg -1.507300
## 2131 bnoagg -1.490979
## 2132 bnoagg -2.033905
## 2133 bnoagg -1.490291
## 2134 bnoagg -1.923528
## 2135 bnoagg -2.178903
## 2136 bnoagg -1.918758
## 2137 bnoagg -1.568231
## 2138 bnoagg -1.596924
## 2139 bnoagg -1.674953
## 2140 bnoagg -1.984356
## 2141 bnoagg -1.982929
## 2142 bnoagg -1.608567
## 2143 bnoagg -1.781063
## 2144 bnoagg -1.929350
## 2145 bnoagg -1.789889
## 2146 bnoagg -1.590048
## 2147 bnoagg -2.004438
## 2148 bnoagg -1.732873
## 2149 bnoagg -1.759505
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## 2151 bnoagg -1.455793
## 2152 bnoagg -1.242231
## 2153 bnoagg -1.675883
## 2154 bnoagg -1.583541
## 2155 bnoagg -1.691564
## 2156 bnoagg -1.359206
## 2157 bnoagg -1.757882
## 2158 bnoagg -2.071296
## 2159 bnoagg -1.931079
## 2160 bnoagg -1.951543
## 2161 bnoagg -1.688882
## 2162 bnoagg -1.756064
## 2163 bnoagg -1.981699
## 2164 bnoagg -1.667780
## 2165 bnoagg -1.721818
## 2166 bnoagg -1.718066
## 2167 bnoagg -1.365330
## 2168 bnoagg -1.655468
## 2169 bnoagg -1.217465
## 2170 bnoagg -1.655166
## 2171 bnoagg -1.701885
## 2172 bnoagg -1.572870
## 2173 bnoagg -1.768316
## 2174 bnoagg -1.633540
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## 2176 bnoagg -1.380491
## 2177 bnoagg -1.625749
## 2178 bnoagg -1.874436
## 2179 bnoagg -2.140805
## 2180 bnoagg -1.694753
## 2181 bnoagg -1.804280
## 2182 bnoagg -1.737347
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## 2185 bnoagg -1.840129
## 2186 bnoagg -1.837583
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## 2188 bnoagg -1.460704
## 2189 bnoagg -1.961884
## 2190 bnoagg -1.747394
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## 2192 bnoagg -1.560162
## 2193 bnoagg -1.709155
## 2194 bnoagg -1.966278
## 2195 bnoagg -1.777252
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## 2197 bnoagg -1.673899
## 2198 bnoagg -1.914082
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## 2201 bnoagg -1.559885
## 2202 bnoagg -1.269279
## 2203 bnoagg -1.987908
## 2204 bnoagg -1.477812
## 2205 bnoagg -1.810198
## 2206 bnoagg -1.584157
## 2207 bnoagg -1.407783
## 2208 bnoagg -1.532679
## 2209 bnoagg -2.427424
## 2210 bnoagg -1.731310
## 2211 bnoagg -1.626572
## 2212 bnoagg -1.655789
## 2213 bnoagg -1.861350
## 2214 bnoagg -1.838649
## 2215 bnoagg -2.039917
## 2216 bnoagg -1.696247
## 2217 bnoagg -2.179702
## 2218 bnoagg -1.968496
## 2219 bnoagg -1.796748
## 2220 bnoagg -1.662343
## 2221 bnoagg -1.769154
## 2222 bnoagg -1.692457
## 2223 bnoagg -1.864665
## 2224 bnoagg -1.942229
## 2225 bnoagg -1.646892
## 2226 bnoagg -1.951100
## 2227 bnoagg -1.858767
## 2228 bnoagg -2.194824
## 2229 bnoagg -1.436451
## 2230 bnoagg -1.518149
## 2231 bnoagg -1.989290
## 2232 bnoagg -1.382266
## 2233 bnoagg -1.897310
## 2234 bnoagg -2.262585
## 2235 bnoagg -2.039479
## 2236 bnoagg -1.374196
## 2237 bnoagg -1.709316
## 2238 bnoagg -1.848676
## 2239 bnoagg -2.056921
## 2240 bnoagg -1.538265
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## 2242 bnoagg -1.972283
## 2243 bnoagg -1.742757
## 2244 bnoagg -1.900131
## 2245 bnoagg -1.835396
## 2246 bnoagg -1.982231
## 2247 bnoagg -1.697720
## 2248 bnoagg -1.655358
## 2249 bnoagg -1.883607
## 2250 bnoagg -1.918795
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## 2254 bnoagg -1.802680
## 2255 bnoagg -1.447429
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## 2257 bnoagg -2.120655
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## 2260 bnoagg -1.755069
## 2261 bnoagg -1.355959
## 2262 bnoagg -1.830484
## 2263 bnoagg -1.920303
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## 2266 bnoagg -1.528606
## 2267 bnoagg -1.628203
## 2268 bnoagg -1.912235
## 2269 bnoagg -1.910913
## 2270 bnoagg -1.718062
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## 2272 bnoagg -1.692581
## 2273 bnoagg -1.947143
## 2274 bnoagg -1.732651
## 2275 bnoagg -1.484971
## 2276 bnoagg -1.883063
## 2277 bnoagg -1.745389
## 2278 bnoagg -1.809368
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## 2281 bnoagg -1.840694
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## 2284 bnoagg -1.764177
## 2285 bnoagg -1.872732
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## 2287 bnoagg -1.852153
## 2288 bnoagg -1.389501
## 2289 bnoagg -1.596335
## 2290 bnoagg -1.541785
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## 2293 bnoagg -1.385825
## 2294 bnoagg -1.583522
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## 2296 bnoagg -1.827245
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## 2299 bnoagg -1.734832
## 2300 bnoagg -1.821706
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## 2302 bnoagg -1.690062
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## 2311 bnoagg -1.590576
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## 2314 bnoagg -1.806940
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## 7435 bmean -1.650789
## 7436 bmean -1.665458
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## 7460 bmean -1.536422
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## 7529 bmean -1.824533
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## 7531 bmean -1.565483
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## 7535 bmean -2.006320
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## 7613 bmean -1.712214
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## 7615 bmean -1.782747
## 7616 bmean -1.891885
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## 7619 bmean -1.708696
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## 7628 bmean -1.478885
## 7629 bmean -1.811751
## 7630 bmean -1.628629
## 7631 bmean -1.408212
## 7632 bmean -1.395966
## 7633 bmean -1.564838
## 7634 bmean -2.164601
## 7635 bmean -1.850355
## 7636 bmean -1.719057
## 7637 bmean -1.869349
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## 7639 bmean -1.881272
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## 7641 bmean -1.858571
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## 7645 bmean -1.441014
## 7646 bmean -1.657772
## 7647 bmean -1.702227
## 7648 bmean -1.745926
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## 7650 bmean -1.699280
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## 7659 bmean -1.701398
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## 7670 bmean -2.028554
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## 7677 bmean -1.548506
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## 7679 bmean -2.147512
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## 7706 bmean -1.810635
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## 7710 bmean -1.430404
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## 7715 bmean -1.742551
## 7716 bmean -1.677608
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## 7730 bmean -1.934019
## 7731 bmean -1.852654
## 7732 bmean -1.796515
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## 7734 bmean -1.672198
## 7735 bmean -1.734144
## 7736 bmean -2.001730
## 7737 bmean -1.908796
## 7738 bmean -2.111333
## 7739 bmean -1.477617
## 7740 bmean -2.110536
## 7741 bmean -1.919741
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## 7745 bmean -1.829514
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## 7757 bmean -1.638038
## 7758 bmean -1.792704
## 7759 bmean -1.871215
## 7760 bmean -2.094796
## 7761 bmean -1.732863
## 7762 bmean -1.958507
## 7763 bmean -2.006026
## 7764 bmean -1.973242
## 7765 bmean -1.607682
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## 7768 bmean -1.558380
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## 7797 bmean -2.032877
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## 7800 bmean -1.659341
## 7801 bmean -1.754996
## 7802 bmean -1.661989
## 7803 bmean -1.725554
## 7804 bmean -1.704075
## 7805 bmean -1.632103
## 7806 bmean -1.589265
## 7807 bmean -1.938417
## 7808 bmean -1.985241
## 7809 bmean -1.433244
## 7810 bmean -1.877506
## 7811 bmean -1.878538
## 7812 bmean -2.017298
## 7813 bmean -2.027585
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## 7819 bmean -1.461299
## 7820 bmean -1.732945
## 7821 bmean -1.529810
## 7822 bmean -1.625110
## 7823 bmean -1.346443
## 7824 bmean -1.560172
## 7825 bmean -1.830060
## 7826 bmean -1.898917
## 7827 bmean -1.777169
## 7828 bmean -1.428636
## 7829 bmean -1.946546
## 7830 bmean -1.828763
## 7831 bmean -1.658539
## 7832 bmean -2.016405
## 7833 bmean -1.721004
## 7834 bmean -1.526400
## 7835 bmean -1.327396
## 7836 bmean -2.048529
## 7837 bmean -1.943432
## 7838 bmean -1.499478
## 7839 bmean -1.567646
## 7840 bmean -1.690856
## 7841 bmean -1.801622
## 7842 bmean -1.845715
## 7843 bmean -1.833003
## 7844 bmean -1.461661
## 7845 bmean -1.714684
## 7846 bmean -1.257034
## 7847 bmean -1.555480
## 7848 bmean -1.736043
## 7849 bmean -1.478529
## 7850 bmean -1.813163
## 7851 bmean -1.530347
## 7852 bmean -1.787700
## 7853 bmean -1.389404
## 7854 bmean -1.863699
## 7855 bmean -1.563176
## 7856 bmean -1.809435
## 7857 bmean -1.691365
## 7858 bmean -1.486971
## 7859 bmean -1.599049
## 7860 bmean -1.458277
## 7861 bmean -1.572630
## 7862 bmean -1.893787
## 7863 bmean -2.041434
## 7864 bmean -2.003133
## 7865 bmean -2.057212
## 7866 bmean -1.832986
## 7867 bmean -1.476593
## 7868 bmean -1.586438
## 7869 bmean -1.474077
## 7870 bmean -2.014783
## 7871 bmean -2.126925
## 7872 bmean -2.102359
## 7873 bmean -1.428214
## 7874 bmean -2.001804
## 7875 bmean -1.904915
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## 7877 bmean -1.373448
## 7878 bmean -2.095169
## 7879 bmean -1.863316
## 7880 bmean -1.837000
## 7881 bmean -1.626031
## 7882 bmean -1.707735
## 7883 bmean -1.658742
## 7884 bmean -1.717189
## 7885 bmean -1.596868
## 7886 bmean -1.641433
## 7887 bmean -1.658277
## 7888 bmean -1.882515
## 7889 bmean -1.799070
## 7890 bmean -1.874831
## 7891 bmean -1.352198
## 7892 bmean -1.934700
## 7893 bmean -1.695137
## 7894 bmean -1.569171
## 7895 bmean -1.825790
## 7896 bmean -1.778516
## 7897 bmean -1.425039
## 7898 bmean -2.087119
## 7899 bmean -2.017680
## 7900 bmean -1.393256
## 7901 bmean -1.192189
## 7902 bmean -1.758951
## 7903 bmean -1.834055
## 7904 bmean -1.486499
## 7905 bmean -1.711836
## 7906 bmean -1.932695
## 7907 bmean -1.332715
## 7908 bmean -1.984868
## 7909 bmean -1.807715
## 7910 bmean -1.974706
## 7911 bmean -1.736588
## 7912 bmean -1.740713
## 7913 bmean -1.634136
## 7914 bmean -1.642645
## 7915 bmean -1.599416
## 7916 bmean -1.750814
## 7917 bmean -1.698217
## 7918 bmean -1.879835
## 7919 bmean -2.015878
## 7920 bmean -1.764592
## 7921 bmean -1.863704
## 7922 bmean -1.906072
## 7923 bmean -1.776161
## 7924 bmean -1.460188
## 7925 bmean -1.794805
## 7926 bmean -1.826136
## 7927 bmean -1.789152
## 7928 bmean -1.681910
## 7929 bmean -1.859935
## 7930 bmean -1.636856
## 7931 bmean -1.581581
## 7932 bmean -1.801388
## 7933 bmean -1.659407
## 7934 bmean -1.732322
## 7935 bmean -1.319659
## 7936 bmean -1.656434
## 7937 bmean -2.031400
## 7938 bmean -1.627765
## 7939 bmean -1.801073
## 7940 bmean -1.971676
## 7941 bmean -1.870116
## 7942 bmean -1.998858
## 7943 bmean -1.594243
## 7944 bmean -1.736496
## 7945 bmean -1.782168
## 7946 bmean -1.395367
## 7947 bmean -1.498440
## 7948 bmean -1.905084
## 7949 bmean -1.675262
## 7950 bmean -1.682765
## 7951 bmean -1.847993
## 7952 bmean -1.695670
## 7953 bmean -1.406175
## 7954 bmean -1.605887
## 7955 bmean -1.983104
## 7956 bmean -1.525301
## 7957 bmean -1.566641
## 7958 bmean -1.503233
## 7959 bmean -1.993014
## 7960 bmean -2.060092
## 7961 bmean -1.636269
## 7962 bmean -1.555339
## 7963 bmean -1.828564
## 7964 bmean -1.535958
## 7965 bmean -1.554114
## 7966 bmean -1.965147
## 7967 bmean -1.731067
## 7968 bmean -1.643078
## 7969 bmean -1.684216
## 7970 bmean -1.696583
## 7971 bmean -1.598884
## 7972 bmean -1.467940
## 7973 bmean -1.907736
## 7974 bmean -2.017084
## 7975 bmean -1.962797
## 7976 bmean -2.022253
## 7977 bmean -1.545556
## 7978 bmean -1.912515
## 7979 bmean -1.604388
## 7980 bmean -1.780762
## 7981 bmean -1.785028
## 7982 bmean -1.734215
## 7983 bmean -2.108376
## 7984 bmean -1.567913
## 7985 bmean -1.503596
## 7986 bmean -2.004804
## 7987 bmean -1.645262
## 7988 bmean -1.516513
## 7989 bmean -1.814108
## 7990 bmean -1.786621
## 7991 bmean -1.937065
## 7992 bmean -1.533152
## 7993 bmean -1.832226
## 7994 bmean -1.762324
## 7995 bmean -1.840021
## 7996 bmean -1.686675
## 7997 bmean -2.124379
## 7998 bmean -1.723923
## 7999 bmean -1.632255
## 8000 bmean -1.539248
Dichotomous Accuracy Rates
## aggStrat N meanAcc sd se
## 1 disagg 234 0.3715507 0.3415581 0.02232835
## 2 disagg+mean 234 0.4620956 0.3671058 0.02399846
## 3 mean 339 0.4948591 0.3458809 0.01878567
