This notebook contains statistical analyses reported in Stauch, Peter, Ehrlich, Nolte, & Fries (2021), Gammaband responses to equiluminant colors in early human visual cortex.
To run it, the dataset color_dataset.csv (available from Zenodo) needs to be stored in the same folder as the notebook.
First, we’ll load in required libraries as well as the dataset, remove invalid trials and prepare data formats.
library(magrittr)
library(lme4)
library(lmerTest)
library(afex)
library(dplyr)
library(data.table)
library(tidyverse)
library(Hmisc)
library(emmeans)
library(MuMIn)
library(BayesFactor)
data <- fread('color_dataset.csv', stringsAsFactors=TRUE)
data$ID <- factor(data$ID)
data$stimulusVerbal <- relevel(data$stimulusVerbal, ref = "grating")
data %<>% filter(answer !='prepress' & answer !='pause')
peaks <- fread('color_peaks.csv', stringsAsFactors=TRUE)
peaks$ID <- factor(peaks$ID)
ERFparam <- fread('color_ERFs.csv', stringsAsFactors=TRUE)
ERFparam$ID <- factor(ERFparam$ID)We’ll now go through the subsections of our Results and provide code for statistical analyses.
data %>%
filter(correctReport==1|correctReport==0) %>%
filter(wasCatch==0 & wasFixated==1) -> behavData
behavData %>%
group_by(ID, stimType) %>%
dplyr::summarize(meanAcc = mean(correctReport, na.rm=TRUE),
meanRT = mean(rt, na.rm=TRUE)) -> meanBehav
behavData %>%
group_by(ID, stimulusVerbal) %>%
dplyr::summarize(meanAcc = mean(correctReport, na.rm=TRUE),
meanRT = mean(rt, na.rm=TRUE)) -> meanBehavAllStims
behavData %>%
group_by(ID, stimulusVerbal) %>%
filter(repNr > 50) %>% # repetitions during which staircases were stable
dplyr::summarize(meanContrast = mean(tContrast, na.rm=TRUE)) ->
meanBehavAllStimsStablemeanBehav %>%
group_by(stimType) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$meanRT*1000))))meanBehav %>%
group_by(stimType) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$meanAcc))))meanBehavAllStims %>%
group_by(stimulusVerbal) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$meanRT*1000))))meanBehavAllStims %>% ungroup() %>%
filter(stimulusVerbal != "grating") -> dat
m0 <- aov_ez(id = "ID", dv = "meanRT", within = "stimulusVerbal",
data = dat)
summary(m0)##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 71.783 1 1.19798 29 1737.6899 < 2.2e-16 ***
## stimulusVerbal 0.062 7 0.19488 203 9.2562 6.237e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## stimulusVerbal 0.58141 0.97814
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## stimulusVerbal 0.87151 6.47e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## stimulusVerbal 1.129272 6.236841e-10emmeans(m0, specs = pairwise~stimulusVerbal, adjust ='holm')## $emmeans
## stimulusVerbal emmean SE df lower.CL upper.CL
## blue 0.560 0.0136 29 0.532 0.588
## green 0.558 0.0141 29 0.529 0.587
## greenblue 0.550 0.0142 29 0.521 0.579
## greenyellow 0.562 0.0137 29 0.534 0.590
## red 0.509 0.0146 29 0.480 0.539
## redblue 0.548 0.0155 29 0.517 0.580
## redyellow 0.552 0.0139 29 0.524 0.581
## yellow 0.536 0.0136 29 0.508 0.563
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## blue - green 0.00245 0.00831 29 0.294 1.0000
## blue - greenblue 0.01006 0.00810 29 1.242 1.0000
## blue - greenyellow -0.00136 0.00683 29 -0.199 1.0000
## blue - red 0.05073 0.00786 29 6.451 <.0001
## blue - redblue 0.01189 0.00852 29 1.394 1.0000
## blue - redyellow 0.00804 0.00878 29 0.915 1.0000
## blue - yellow 0.02439 0.00769 29 3.174 0.0674
## green - greenblue 0.00761 0.00825 29 0.922 1.0000
## green - greenyellow -0.00380 0.00744 29 -0.511 1.0000
## green - red 0.04828 0.00810 29 5.957 <.0001
## green - redblue 0.00944 0.00846 29 1.115 1.0000
## green - redyellow 0.00559 0.00896 29 0.624 1.0000
## green - yellow 0.02195 0.00656 29 3.346 0.0466
## greenblue - greenyellow -0.01142 0.00698 29 -1.636 1.0000
## greenblue - red 0.04066 0.00735 29 5.531 0.0001
## greenblue - redblue 0.00183 0.00841 29 0.217 1.0000
## greenblue - redyellow -0.00202 0.00792 29 -0.255 1.0000
## greenblue - yellow 0.01433 0.00721 29 1.988 1.0000
## greenyellow - red 0.05208 0.00760 29 6.851 <.0001
## greenyellow - redblue 0.01324 0.00798 29 1.661 1.0000
## greenyellow - redyellow 0.00940 0.00864 29 1.087 1.0000
## greenyellow - yellow 0.02575 0.00659 29 3.907 0.0113
## red - redblue -0.03884 0.00731 29 -5.316 0.0003
## red - redyellow -0.04269 0.00811 29 -5.263 0.0003
## red - yellow -0.02633 0.00784 29 -3.356 0.0466
## redblue - redyellow -0.00385 0.00970 29 -0.397 1.0000
## redblue - yellow 0.01251 0.00926 29 1.351 1.0000
## redyellow - yellow 0.01636 0.00821 29 1.993 1.0000
##
## P value adjustment: holm method for 28 testsmeanBehavAllStimsStable %>%
group_by(stimulusVerbal) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$meanContrast*100))))meanBehavAllStimsStable %>% ungroup() %>%
filter(stimulusVerbal != "grating") -> dat
m1 <- aov_ez(id = "ID", dv = "meanContrast", within = "stimulusVerbal",
data = dat)
summary(m1)##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 24.7034 1 0.78261 29 915.392 < 2.2e-16 ***
## stimulusVerbal 5.8773 7 2.07414 203 82.175 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## stimulusVerbal 0.088246 8.148e-05
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## stimulusVerbal 0.58532 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## stimulusVerbal 0.6936535 1.873504e-39m1## Anova Table (Type 3 tests)
##
## Response: meanContrast
## Effect df MSE F ges p.value
## 1 stimulusVerbal 4.10, 118.82 0.02 82.17 *** .673 <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GGemmeans(m1, specs = pairwise~stimulusVerbal, adjust ='holm')## $emmeans
## stimulusVerbal emmean SE df lower.CL upper.CL
## blue 0.709 0.0242 29 0.660 0.758
## green 0.302 0.0141 29 0.273 0.331
## greenblue 0.293 0.0139 29 0.265 0.322
## greenyellow 0.204 0.0226 29 0.158 0.251
## red 0.219 0.0148 29 0.189 0.250
## redblue 0.372 0.0325 29 0.305 0.438
## redyellow 0.199 0.0152 29 0.168 0.230
## yellow 0.268 0.0170 29 0.234 0.303
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## blue - green 0.40691 0.0255 29 15.951 <.0001
## blue - greenblue 0.41573 0.0234 29 17.778 <.0001
## blue - greenyellow 0.50470 0.0324 29 15.554 <.0001
## blue - red 0.48963 0.0275 29 17.829 <.0001
## blue - redblue 0.33735 0.0386 29 8.737 <.0001
## blue - redyellow 0.51030 0.0285 29 17.902 <.0001
## blue - yellow 0.44062 0.0282 29 15.652 <.0001
## green - greenblue 0.00882 0.0164 29 0.538 1.0000
## green - greenyellow 0.09779 0.0229 29 4.268 0.0027
## green - red 0.08272 0.0176 29 4.693 0.0010
## green - redblue -0.06957 0.0332 29 -2.093 0.3042
## green - redyellow 0.10339 0.0180 29 5.742 0.0001
## green - yellow 0.03371 0.0160 29 2.112 0.3042
## greenblue - greenyellow 0.08897 0.0265 29 3.353 0.0268
## greenblue - red 0.07390 0.0177 29 4.167 0.0033
## greenblue - redblue -0.07838 0.0306 29 -2.563 0.1425
## greenblue - redyellow 0.09457 0.0190 29 4.969 0.0005
## greenblue - yellow 0.02490 0.0211 29 1.180 1.0000
## greenyellow - red -0.01507 0.0259 29 -0.581 1.0000
## greenyellow - redblue -0.16735 0.0315 29 -5.312 0.0002
## greenyellow - redyellow 0.00560 0.0235 29 0.239 1.0000
## greenyellow - yellow -0.06408 0.0211 29 -3.033 0.0556
## red - redblue -0.15228 0.0318 29 -4.787 0.0008
## red - redyellow 0.02067 0.0197 29 1.051 1.0000
## red - yellow -0.04900 0.0215 29 -2.284 0.2388
## redblue - redyellow 0.17295 0.0356 29 4.863 0.0007
## redblue - yellow 0.10328 0.0374 29 2.762 0.0986
## redyellow - yellow -0.06968 0.0154 29 -4.514 0.0015
##
## P value adjustment: holm method for 28 testsmeanBehavAllStimsStable %>%
filter(stimulusVerbal != 'grating') %>%
mutate(isBlue = factor(stimulusVerbal=='blue')) %>%
group_by(isBlue, ID) %>%
dplyr::summarize(meanContBN = mean(meanContrast)) %>%
group_split(isBlue) -> s1
t.test(s1[[1]]$meanContBN, s1[[2]]$meanContBN, paired = TRUE)##
## Paired t-test
##
## data: s1[[1]]$meanContBN and s1[[2]]$meanContBN
## t = -18.009, df = 29, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4939853 -0.3932275
## sample estimates:
## mean of the differences
## -0.4436064data %>%
filter(wasFixated==1) -> erfData
erfData %>%
group_by(ID, stimType) %>%
dplyr::summarize(meanC1 = mean(C1, na.rm=TRUE)) %>%
ungroup() -> meanERFType
erfData %>%
group_by(ID, stimulusVerbal) %>%
dplyr::summarize(meanC1 = mean(C1, na.rm=TRUE)) %>%
ungroup() -> meanERFStimmeanERFStim %>%
group_by(stimulusVerbal) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$meanC1))))meanERFStim %>% ungroup() %>%
filter(stimulusVerbal != "grating") -> dat
m2 <- aov_ez(id = "ID", dv = "meanC1", within = "stimulusVerbal",
data = dat)
summary(m2)##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 105.105 1 12.4569 29 244.686 1.133e-15 ***
## stimulusVerbal 6.489 7 6.2057 203 30.324 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## stimulusVerbal 0.025745 1.1315e-09
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## stimulusVerbal 0.49773 2.474e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## stimulusVerbal 0.5740092 2.45615e-17m2## Anova Table (Type 3 tests)
##
## Response: meanC1
## Effect df MSE F ges p.value
## 1 stimulusVerbal 3.48, 101.04 0.06 30.32 *** .258 <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GGemmeans(m2, specs = pairwise~stimulusVerbal, adjust ='holm')## $emmeans
## stimulusVerbal emmean SE df lower.CL upper.CL
## blue -0.330 0.0243 29 -0.379 -0.280
## green -0.627 0.0443 29 -0.718 -0.536
## greenblue -0.507 0.0331 29 -0.575 -0.439
## greenyellow -0.822 0.0621 29 -0.949 -0.695
## red -0.831 0.0664 29 -0.967 -0.695
## redblue -0.739 0.0577 29 -0.857 -0.622
## redyellow -0.635 0.0443 29 -0.726 -0.544
## yellow -0.803 0.0652 29 -0.936 -0.670
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## blue - green 0.29716 0.0399 29 7.449 <.0001
## blue - greenblue 0.17740 0.0312 29 5.684 0.0001
## blue - greenyellow 0.49182 0.0513 29 9.580 <.0001
## blue - red 0.50134 0.0616 29 8.141 <.0001
## blue - redblue 0.40971 0.0527 29 7.773 <.0001
## blue - redyellow 0.30512 0.0431 29 7.071 <.0001
## blue - yellow 0.47337 0.0519 29 9.117 <.0001
## green - greenblue -0.11976 0.0254 29 -4.715 0.0008
## green - greenyellow 0.19465 0.0298 29 6.536 <.0001
## green - red 0.20418 0.0403 29 5.069 0.0003
## green - redblue 0.11254 0.0361 29 3.115 0.0371
## green - redyellow 0.00796 0.0338 29 0.235 1.0000
## green - yellow 0.17621 0.0475 29 3.709 0.0096
## greenblue - greenyellow 0.31442 0.0445 29 7.063 <.0001
## greenblue - red 0.32394 0.0486 29 6.662 <.0001
## greenblue - redblue 0.23231 0.0412 29 5.637 0.0001
## greenblue - redyellow 0.12772 0.0302 29 4.223 0.0028
## greenblue - yellow 0.29597 0.0515 29 5.749 0.0001
## greenyellow - red 0.00953 0.0490 29 0.194 1.0000
## greenyellow - redblue -0.08211 0.0449 29 -1.829 0.4662
## greenyellow - redyellow -0.18670 0.0472 29 -3.956 0.0054
## greenyellow - yellow -0.01845 0.0360 29 -0.512 1.0000
## red - redblue -0.09164 0.0268 29 -3.423 0.0187
## red - redyellow -0.19622 0.0418 29 -4.690 0.0008
## red - yellow -0.02798 0.0661 29 -0.423 1.0000
## redblue - redyellow -0.10458 0.0381 29 -2.746 0.0717
## redblue - yellow 0.06366 0.0600 29 1.062 1.0000
## redyellow - yellow 0.16825 0.0586 29 2.872 0.0603
##
## P value adjustment: holm method for 28 testsERFparam %>% ungroup() %>%
filter(stimulusVerbal != "grating") -> dat
mS <- aov_ez(id = "ID", dv = "erfSlopeZ", within = "stimulusVerbal",
data = dat)
summary(mS)##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 288305 1 81168 29 103.0067 4.711e-11 ***
## stimulusVerbal 23619 7 90951 203 7.5311 4.601e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## stimulusVerbal 0.16175 0.007808
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## stimulusVerbal 0.69794 3.27e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## stimulusVerbal 0.8564391 3.471194e-07mS## Anova Table (Type 3 tests)
##
## Response: erfSlopeZ
## Effect df MSE F ges p.value
## 1 stimulusVerbal 4.89, 141.68 641.94 7.53 *** .121 <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GGemmeans(mS, specs = pairwise~stimulusVerbal, adjust ='holm')## $emmeans
## stimulusVerbal emmean SE df lower.CL upper.CL
## blue -13.6 1.48 29 -16.7 -10.6
## green -28.2 5.17 29 -38.8 -17.7
## greenblue -28.1 3.90 29 -36.0 -20.1
## greenyellow -37.2 4.92 29 -47.3 -27.1
## red -41.6 6.31 29 -54.5 -28.7
## redblue -44.4 6.37 29 -57.4 -31.4
## redyellow -41.1 5.44 29 -52.3 -30.0
## yellow -43.0 4.42 29 -52.0 -33.9
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## blue - green 14.595 5.70 29 2.561 0.2836
## blue - greenblue 14.418 4.32 29 3.339 0.0511
## blue - greenyellow 23.551 4.71 29 4.997 0.0007
## blue - red 27.950 5.85 29 4.778 0.0011
## blue - redblue 30.760 6.18 29 4.974 0.0007
## blue - redyellow 27.496 5.47 29 5.024 0.0006
## blue - yellow 29.348 4.16 29 7.049 <.0001
## green - greenblue -0.176 3.95 29 -0.045 1.0000
## green - greenyellow 8.956 5.06 29 1.771 1.0000
## green - red 13.356 5.47 29 2.441 0.3357
## green - redblue 16.166 6.07 29 2.661 0.2386
## green - redyellow 12.901 5.41 29 2.384 0.3580
## green - yellow 14.754 6.61 29 2.232 0.4686
## greenblue - greenyellow 9.132 5.11 29 1.789 1.0000
## greenblue - red 13.532 5.28 29 2.565 0.2836
## greenblue - redblue 16.342 4.82 29 3.390 0.0467
## greenblue - redyellow 13.077 3.94 29 3.318 0.0515
## greenblue - yellow 14.930 5.51 29 2.708 0.2246
## greenyellow - red 4.400 5.54 29 0.795 1.0000
## greenyellow - redblue 7.210 6.46 29 1.117 1.0000
## greenyellow - redyellow 3.945 6.18 29 0.639 1.0000
## greenyellow - yellow 5.798 4.96 29 1.168 1.0000
## red - redblue 2.810 4.53 29 0.620 1.0000
## red - redyellow -0.455 5.13 29 -0.089 1.0000
## red - yellow 1.398 5.84 29 0.239 1.0000
## redblue - redyellow -3.265 5.88 29 -0.555 1.0000
## redblue - yellow -1.412 6.61 29 -0.213 1.0000
## redyellow - yellow 1.852 6.68 29 0.277 1.0000
##
## P value adjustment: holm method for 28 testsERFparam %>%
group_by(stimulusVerbal) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$erfPeakTime*1000))))ERFparam %>% ungroup() %>%
filter(stimulusVerbal != "grating") -> dat
mS <- aov_ez(id = "ID", dv = "erfPeakTime", within = "stimulusVerbal",
data = dat)
summary(mS)##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 1.59455 1 0.0123737 29 3737.0975 < 2.2e-16 ***
## stimulusVerbal 0.00264 7 0.0078385 203 9.7811 1.728e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## stimulusVerbal 0.062364 4.2686e-06
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## stimulusVerbal 0.55036 1.13e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## stimulusVerbal 0.6452016 1.751468e-07mS## Anova Table (Type 3 tests)
##
## Response: erfPeakTime
## Effect df MSE F ges p.value
## 1 stimulusVerbal 3.85, 111.72 0.00 9.78 *** .116 <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GGemmeans(mS, specs = pairwise~stimulusVerbal, adjust ='holm')## $emmeans
## stimulusVerbal emmean SE df lower.CL upper.CL
## blue 0.0886 0.00194 29 0.0846 0.0925
## green 0.0809 0.00169 29 0.0775 0.0844
## greenblue 0.0794 0.00191 29 0.0755 0.0833
## greenyellow 0.0825 0.00142 29 0.0796 0.0854
## red 0.0779 0.00175 29 0.0743 0.0815
## redblue 0.0788 0.00165 29 0.0754 0.0821
## redyellow 0.0796 0.00169 29 0.0762 0.0831
## yellow 0.0844 0.00151 29 0.0813 0.0875
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## blue - green 0.007639 0.00235 29 3.252 0.0611
## blue - greenblue 0.009194 0.00235 29 3.912 0.0122
## blue - greenyellow 0.006028 0.00222 29 2.715 0.2101
## blue - red 0.010639 0.00201 29 5.286 0.0003
## blue - redblue 0.009806 0.00198 29 4.940 0.0008
## blue - redyellow 0.008917 0.00212 29 4.212 0.0058
## blue - yellow 0.004139 0.00230 29 1.800 0.9871
## green - greenblue 0.001556 0.00135 29 1.155 1.0000
## green - greenyellow -0.001611 0.00116 29 -1.386 1.0000
## green - red 0.003000 0.00111 29 2.698 0.2101
## green - redblue 0.002167 0.00122 29 1.779 0.9871
## green - redyellow 0.001278 0.00130 29 0.980 1.0000
## green - yellow -0.003500 0.00170 29 -2.058 0.6333
## greenblue - greenyellow -0.003167 0.00129 29 -2.454 0.3059
## greenblue - red 0.001444 0.00120 29 1.207 1.0000
## greenblue - redblue 0.000611 0.00121 29 0.504 1.0000
## greenblue - redyellow -0.000278 0.00100 29 -0.277 1.0000
## greenblue - yellow -0.005056 0.00190 29 -2.666 0.2112
## greenyellow - red 0.004611 0.00111 29 4.152 0.0066
## greenyellow - redblue 0.003778 0.00127 29 2.984 0.1144
## greenyellow - redyellow 0.002889 0.00126 29 2.302 0.4022
## greenyellow - yellow -0.001889 0.00133 29 -1.425 1.0000
## red - redblue -0.000833 0.00101 29 -0.823 1.0000
## red - redyellow -0.001722 0.00124 29 -1.383 1.0000
## red - yellow -0.006500 0.00180 29 -3.617 0.0246
## redblue - redyellow -0.000889 0.00114 29 -0.777 1.0000
## redblue - yellow -0.005667 0.00146 29 -3.890 0.0124
## redyellow - yellow -0.004778 0.00182 29 -2.630 0.2163
##
## P value adjustment: holm method for 28 testsmeanERFType %>%
group_split(stimType) -> s1
t.test(s1[[1]]$meanC1, s1[[2]]$meanC1, paired = TRUE)##
## Paired t-test
##
## data: s1[[1]]$meanC1 and s1[[2]]$meanC1
## t = 6.7974, df = 29, p-value = 1.835e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.3568385 0.6639926
## sample estimates:
## mean of the differences
## 0.5104155data %>%
filter(wasFixated==1) -> gammaData
gammaData %>%
group_by(ID, stimType) %>%
dplyr::summarize(meanGPower = mean(percGammapower, na.rm=TRUE),
meanGFreq = mean(realGammapeak, na.rm=TRUE)) %>%
ungroup() -> meanGP
gammaData %>%
group_by(ID, stimulusVerbal) %>%
dplyr::summarize(meanGPower = mean(percGammapower, na.rm=TRUE),
meanGFreq = mean(realGammapeak, na.rm=TRUE)) %>%
ungroup() -> meanGPStims
peaks %>%
group_by(ID, stimType) %>%
dplyr::summarize(meanPeak1 = mean(peak1, na.rm=TRUE),
meanPeak2 = mean(peak2, na.rm=TRUE)) %>%
ungroup() -> peaksStimtypemeanGP %>%
group_by(stimType) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$meanGPower))))meanGP %>%
group_split(stimType) -> s1
t.test(s1[[1]]$meanGPower, s1[[2]]$meanGPower, paired = TRUE)##
## Paired t-test
##
## data: s1[[1]]$meanGPower and s1[[2]]$meanGPower
## t = -8.3195, df = 29, p-value = 3.594e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -100.22417 -60.67036
## sample estimates:
## mean of the differences
## -80.44727peaksStimtype %>%
group_by(stimType) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$meanPeak1, na.rm=TRUE))))peaksStimtype %>%
group_split(stimType) -> s1
t.test(s1[[1]]$meanPeak1, s1[[2]]$meanPeak1, paired = TRUE)##
## Paired t-test
##
## data: s1[[1]]$meanPeak1 and s1[[2]]$meanPeak1
## t = -4.2483, df = 27, p-value = 0.0002288
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.146827 -2.491694
## sample estimates:
## mean of the differences
## -4.81926cor.test(s1[[1]]$meanPeak1, s1[[2]]$meanPeak1)##
## Pearson's product-moment correlation
##
## data: s1[[1]]$meanPeak1 and s1[[2]]$meanPeak1
## t = 4.2245, df = 26, p-value = 0.0002598
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3476463 0.8166744
## sample estimates:
## cor
## 0.6379782peaksStimtype %>%
group_by(stimType) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$meanPeak2, na.rm=TRUE)))) t.test(s1[[1]]$meanPeak2, s1[[2]]$meanPeak2, paired = TRUE)##
## Paired t-test
##
## data: s1[[1]]$meanPeak2 and s1[[2]]$meanPeak2
## t = 0.54335, df = 1, p-value = 0.6831
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -110.1574 119.9996
## sample estimates:
## mean of the differences
## 4.92107meanGPStims %>%
group_by(stimulusVerbal) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$meanGPower))))meanGPStims %>% ungroup() %>%
filter(stimulusVerbal != "grating") -> dat
m3 <- aov_ez(id = "ID", dv = "meanGPower", within = "stimulusVerbal",
data = dat)
summary(m3)##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 89061 1 56055 29 46.076 1.882e-07 ***
## stimulusVerbal 24256 7 48400 203 14.534 2.728e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## stimulusVerbal 0.0002407 8.3231e-32
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## stimulusVerbal 0.33968 1.442e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## stimulusVerbal 0.3720514 5.320935e-07m3## Anova Table (Type 3 tests)
##
## Response: meanGPower
## Effect df MSE F ges p.value
## 1 stimulusVerbal 2.38, 68.96 701.90 14.53 *** .188 <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GGemmeans(m3, specs = pairwise~stimulusVerbal, adjust ='none')## $emmeans
## stimulusVerbal emmean SE df lower.CL upper.CL
## blue 2.25 1.05 29 0.106 4.38
## green 31.19 5.43 29 20.094 42.29
## greenblue 15.72 2.53 29 10.557 20.89
## greenyellow 30.86 5.13 29 20.363 41.36
## red 30.83 6.15 29 18.242 43.42
## redblue 17.36 2.53 29 12.177 22.54
## redyellow 10.46 1.96 29 6.454 14.46
## yellow 15.44 2.94 29 9.423 21.46
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## blue - green -28.9475 5.49 29 -5.269 <.0001
## blue - greenblue -13.4777 2.46 29 -5.474 <.0001
## blue - greenyellow -28.6167 5.40 29 -5.303 <.0001
## blue - red -28.5837 6.13 29 -4.661 0.0001
## blue - redblue -15.1117 2.57 29 -5.876 <.0001
## blue - redyellow -8.2111 1.96 29 -4.195 0.0002
## blue - yellow -13.1973 3.29 29 -4.017 0.0004
## green - greenblue 15.4698 3.27 29 4.725 0.0001
## green - greenyellow 0.3309 3.80 29 0.087 0.9312
## green - red 0.3638 2.70 29 0.135 0.8939
## green - redblue 13.8358 3.81 29 3.632 0.0011
## green - redyellow 20.7364 4.43 29 4.677 0.0001
## green - yellow 15.7502 4.91 29 3.206 0.0033
## greenblue - greenyellow -15.1390 4.07 29 -3.724 0.0008
## greenblue - red -15.1060 4.39 29 -3.439 0.0018
## greenblue - redblue -1.6340 1.75 29 -0.933 0.3583
## greenblue - redyellow 5.2666 1.70 29 3.093 0.0043
## greenblue - yellow 0.2804 3.14 29 0.089 0.9294
## greenyellow - red 0.0329 4.61 29 0.007 0.9944
## greenyellow - redblue 13.5050 3.63 29 3.716 0.0009
## greenyellow - redyellow 20.4056 5.06 29 4.035 0.0004
## greenyellow - yellow 15.4193 3.18 29 4.849 <.0001
## red - redblue 13.4720 4.13 29 3.261 0.0028
## red - redyellow 20.3726 5.42 29 3.756 0.0008
## red - yellow 15.3864 5.93 29 2.597 0.0146
## redblue - redyellow 6.9006 2.49 29 2.770 0.0097
## redblue - yellow 1.9144 2.79 29 0.686 0.4980
## redyellow - yellow -4.9862 3.56 29 -1.399 0.1725meanGPStims %>% ungroup() %>%
filter(stimulusVerbal == "red" | stimulusVerbal == "green") %>%
group_split(stimulusVerbal)-> s
1/ttestBF(s[[1]]$meanGPower, s[[2]]$meanGPower, paired = TRUE, rscale = 1)## Bayes factor analysis
## --------------
## [1] Null, mu=0 : 7.01814 ±0.01%
##
## Against denominator:
## Alternative, r = 1, mu =/= 0
## ---
## Bayes factor type: BFoneSample, JZSpeaks %>% ungroup() %>%
group_by(stimType,ID) %>%
dplyr::summarize(onePeak1Found = paste("Peak1",
as.character(sum(peak1found)>0)),
onePeak2Found = paste("Peak2",
as.character(sum(peak2found)>0))) -> d
prop.table(table(d$stimType, d$onePeak1Found),1)##
## Peak1 FALSE Peak1 TRUE
## color 0.03333333 0.96666667
## grating 0.03333333 0.96666667infer::prop_test(d, response = onePeak1Found, explanatory = stimType,
success = "Peak1 TRUE")prop.table(table(d$stimType, d$onePeak2Found),1)##
## Peak2 FALSE Peak2 TRUE
## color 0.1000000 0.9000000
## grating 0.8666667 0.1333333infer::prop_test(d, response = onePeak2Found, explanatory = stimType,
success = "Peak2 TRUE")peaks %>%
group_by(stimulusVerbal) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$peak1))))peaks %>% ungroup() %>%
filter(stimulusVerbal != "grating") -> dat
m4 <- aov_ez(id = "ID", dv = "peak1", within = "stimulusVerbal",
data = dat)
summary(m4)##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 113054 1 1894.4 5 298.3851 1.191e-05 ***
## stimulusVerbal 785 7 1374.0 35 2.8559 0.0182 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m4## Anova Table (Type 3 tests)
##
## Response: peak1
## Effect df MSE F ges p.value
## 1 stimulusVerbal 7, 35 39.26 2.86 * .194 .018
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GGemmeans(m4, specs = pairwise~stimulusVerbal, adjust ='holm')## $emmeans
## stimulusVerbal emmean SE df lower.CL upper.CL
## blue 49.3 2.26 5 43.5 55.2
## green 46.1 2.49 5 39.7 52.5
## greenblue 44.5 4.94 5 31.8 57.2
## greenyellow 45.6 2.72 5 38.6 52.6
## red 48.5 4.07 5 38.0 58.9
## redblue 47.0 5.36 5 33.2 60.8
## redyellow 58.4 2.33 5 52.4 64.3
## yellow 48.9 3.88 5 38.9 58.9
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## blue - green 3.248 3.71 5 0.876 1.0000
## blue - greenblue 4.843 5.58 5 0.868 1.0000
## blue - greenyellow 3.780 3.24 5 1.166 1.0000
## blue - red 0.874 4.83 5 0.181 1.0000
## blue - redblue 2.313 5.24 5 0.442 1.0000
## blue - redyellow -9.024 2.67 5 -3.379 0.5318
## blue - yellow 0.424 4.36 5 0.097 1.0000
## green - greenblue 1.595 2.63 5 0.607 1.0000
## green - greenyellow 0.532 1.17 5 0.453 1.0000
## green - red -2.374 2.10 5 -1.129 1.0000
## green - redblue -0.935 3.75 5 -0.250 1.0000
## green - redyellow -12.272 3.70 5 -3.313 0.5504
## green - yellow -2.824 1.83 5 -1.543 1.0000
## greenblue - greenyellow -1.063 2.54 5 -0.418 1.0000
## greenblue - red -3.969 1.94 5 -2.045 1.0000
## greenblue - redblue -2.530 3.07 5 -0.824 1.0000
## greenblue - redyellow -13.867 5.59 5 -2.481 1.0000
## greenblue - yellow -4.419 2.23 5 -1.983 1.0000
## greenyellow - red -2.906 2.31 5 -1.257 1.0000
## greenyellow - redblue -1.467 2.95 5 -0.497 1.0000
## greenyellow - redyellow -12.804 3.38 5 -3.790 0.3572
## greenyellow - yellow -3.356 1.47 5 -2.277 1.0000
## red - redblue 1.438 4.29 5 0.336 1.0000
## red - redyellow -9.898 4.66 5 -2.126 1.0000
## red - yellow -0.450 2.69 5 -0.167 1.0000
## redblue - redyellow -11.337 5.74 5 -1.975 1.0000
## redblue - yellow -1.889 2.16 5 -0.875 1.0000
## redyellow - yellow 9.448 4.67 5 2.022 1.0000
##
## P value adjustment: holm method for 28 testsAs the second peak is missing for many participant-stimulus combinations, we’re using a linear mixed-model instead of a RMANOVA here.
peaks %>%
group_by(stimulusVerbal) %>%
do(data.frame(rbind(Hmisc::smean.cl.boot(.$peak2))))peaks %>% ungroup() %>%
filter(stimulusVerbal != "grating") -> dat
m5 <- lmer(peak2 ~ (1|ID) + stimulusVerbal, data = dat)
anova(m5)emmeans(m5, specs = pairwise~stimulusVerbal, adjust ='holm')## $emmeans
## stimulusVerbal emmean SE df lower.CL upper.CL
## blue 97.4 3.04 94.9 91.3 103.4
## green 94.2 2.39 94.6 89.5 98.9
## greenblue 96.1 2.39 94.6 91.4 100.9
## greenyellow 96.2 2.30 94.3 91.6 100.7
## red 96.7 2.30 94.4 92.1 101.2
## redblue 95.2 2.03 92.8 91.1 99.2
## redyellow 96.3 2.59 94.9 91.1 101.4
## yellow 102.6 2.48 94.9 97.7 107.5
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## blue - green 3.1797 3.81 93.0 0.834 1.0000
## blue - greenblue 1.2634 3.84 94.0 0.329 1.0000
## blue - greenyellow 1.2301 3.72 91.0 0.331 1.0000
## blue - red 0.7089 3.72 90.8 0.191 1.0000
## blue - redblue 2.2165 3.58 92.4 0.619 1.0000
## blue - redyellow 1.1200 3.96 93.8 0.283 1.0000
## blue - yellow -5.1986 3.84 91.4 -1.352 1.0000
## green - greenblue -1.9164 3.23 82.3 -0.593 1.0000
## green - greenyellow -1.9497 3.13 76.9 -0.623 1.0000
## green - red -2.4708 3.13 77.1 -0.789 1.0000
## green - redblue -0.9632 2.98 79.8 -0.323 1.0000
## green - redyellow -2.0597 3.36 80.7 -0.612 1.0000
## green - yellow -8.3783 3.31 83.2 -2.531 0.3712
## greenblue - greenyellow -0.0333 3.19 84.3 -0.010 1.0000
## greenblue - red -0.5544 3.18 82.9 -0.174 1.0000
## greenblue - redblue 0.9532 3.01 83.5 0.317 1.0000
## greenblue - redyellow -0.1434 3.40 85.0 -0.042 1.0000
## greenblue - yellow -6.4619 3.32 84.5 -1.944 1.0000
## greenyellow - red -0.5211 3.08 77.8 -0.169 1.0000
## greenyellow - redblue 0.9865 2.90 77.5 0.340 1.0000
## greenyellow - redyellow -0.1101 3.33 83.3 -0.033 1.0000
## greenyellow - yellow -6.4286 3.26 83.8 -1.973 1.0000
## red - redblue 1.5076 2.91 79.0 0.518 1.0000
## red - redyellow 0.4110 3.35 85.0 0.123 1.0000
## red - yellow -5.9075 3.26 83.6 -1.814 1.0000
## redblue - redyellow -1.0966 3.16 83.2 -0.346 1.0000
## redblue - yellow -7.4151 3.09 84.7 -2.400 0.5017
## redyellow - yellow -6.3185 3.44 81.6 -1.838 1.0000
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: holm method for 28 testsmeanGPStims %>% inner_join(ERFparam) %>%
inner_join(meanERFStim) %>%
inner_join(ERFparam) %>%
inner_join(meanBehavAllStims) %>%
inner_join(meanBehavAllStimsStable) %>%
filter(stimulusVerbal != "grating") -> fullDatfullDat %>% group_by(ID) %>%
summarise(corERFGamma = cor(meanC1, meanGPower)) -> particCop
Hmisc::smean.cl.boot(particCop$corERFGamma)## Mean Lower Upper
## -0.3819432 -0.4831535 -0.2755984wilcox.test(particCop$corERFGamma, mu=0)##
## Wilcoxon signed rank exact test
##
## data: particCop$corERFGamma
## V = 30, p-value = 3.79e-06
## alternative hypothesis: true location is not equal to 0m3c <- lmer(meanGPower ~ (1|ID) + meanC1 + erfSlopeZ , data = fullDat)
fullDat$resid <- residuals(m3c)
m3c <- aov_ez(id = "ID", dv = "resid", within = "stimulusVerbal",
data = fullDat)
summary(m3c)##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 0 1 1349 29 0.0000 1
## stimulusVerbal 15245 7 50378 203 8.7757 2.041e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## stimulusVerbal 0.0005407 9.2457e-28
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## stimulusVerbal 0.33207 0.0002063 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## stimulusVerbal 0.3627392 0.0001202639m3c## Anova Table (Type 3 tests)
##
## Response: resid
## Effect df MSE F ges p.value
## 1 stimulusVerbal 2.32, 67.41 747.35 8.78 *** .228 <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GGemmeans(m3c, specs = pairwise~stimulusVerbal, adjust ='holm')## $emmeans
## stimulusVerbal emmean SE df lower.CL upper.CL
## blue -11.23 2.77 29 -16.890 -5.574
## green 12.09 3.25 29 5.438 18.746
## greenblue -0.51 1.14 29 -2.836 1.816
## greenyellow 8.01 3.24 29 1.379 14.650
## red 8.22 4.22 29 -0.424 16.854
## redblue -2.75 1.26 29 -5.322 -0.183
## redyellow -7.48 2.09 29 -11.746 -3.208
## yellow -6.35 2.37 29 -11.199 -1.501
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## blue - green -23.324 5.73 29 -4.070 0.0089
## blue - greenblue -10.722 2.71 29 -3.963 0.0111
## blue - greenyellow -19.246 5.50 29 -3.499 0.0305
## blue - red -19.447 6.63 29 -2.933 0.1104
## blue - redblue -8.480 3.09 29 -2.749 0.1528
## blue - redyellow -3.755 2.43 29 -1.543 0.9353
## blue - yellow -4.882 2.96 29 -1.651 0.8830
## green - greenblue 12.603 3.23 29 3.904 0.0125
## green - greenyellow 4.078 3.73 29 1.094 1.0000
## green - red 3.877 2.88 29 1.348 1.0000
## green - redblue 14.845 3.80 29 3.902 0.0125
## green - redyellow 19.569 4.37 29 4.478 0.0030
## green - yellow 18.442 5.05 29 3.650 0.0215
## greenblue - greenyellow -8.525 4.02 29 -2.121 0.5108
## greenblue - red -8.726 4.43 29 -1.972 0.6408
## greenblue - redblue 2.242 1.98 29 1.133 1.0000
## greenblue - redyellow 6.967 1.86 29 3.745 0.0175
## greenblue - yellow 5.839 3.07 29 1.902 0.6714
## greenyellow - red -0.201 4.61 29 -0.044 1.0000
## greenyellow - redblue 10.767 3.40 29 3.170 0.0680
## greenyellow - redyellow 15.491 4.90 29 3.160 0.0680
## greenyellow - yellow 14.364 3.55 29 4.046 0.0092
## red - redblue 10.968 4.19 29 2.620 0.1939
## red - redyellow 15.692 5.49 29 2.857 0.1254
## red - yellow 14.565 6.10 29 2.387 0.3087
## redblue - redyellow 4.724 2.76 29 1.709 0.8830
## redblue - yellow 3.597 2.70 29 1.333 1.0000
## redyellow - yellow -1.127 3.35 29 -0.337 1.0000
##
## P value adjustment: holm method for 28 testsfullDat %>% ungroup() %>%
filter(stimulusVerbal == "red" | stimulusVerbal == "green") %>%
group_split(stimulusVerbal)-> s
1/ttestBF(s[[1]]$resid, s[[2]]$resid, paired = TRUE, rscale = 1)## Bayes factor analysis
## --------------
## [1] Null, mu=0 : 2.998587 ±0.02%
##
## Against denominator:
## Alternative, r = 1, mu =/= 0
## ---
## Bayes factor type: BFoneSample, JZSfullDat %>% ungroup() %>%
filter(stimulusVerbal == "red" | stimulusVerbal == "green") %>%
group_by(ID) %>%
summarise(LMratioERF = meanC1[stimulusVerbal == "red"]/
meanC1[stimulusVerbal == "green"],
LMratioGamma = meanGPower[stimulusVerbal == "red"]/
meanGPower[stimulusVerbal == "green"],
LMratioContrast = meanContrast[stimulusVerbal == "red"]/
meanContrast[stimulusVerbal == "green"]) %>%
ungroup() -> LMratios
Hmisc::smean.cl.boot(1/LMratios$LMratioContrast)## Mean Lower Upper
## 1.479354 1.317237 1.641987range(1/LMratios$LMratioContrast)## [1] 0.6459659 2.6971367cor.test(LMratios$LMratioContrast, LMratios$LMratioERF, method = "spearman")##
## Spearman's rank correlation rho
##
## data: LMratios$LMratioContrast and LMratios$LMratioERF
## S = 4104, p-value = 0.6465
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.086985541/correlationBF(rank(LMratios$LMratioContrast, ties.method = "average"),
rank(LMratios$LMratioERF, ties.method = "average"), rscale = 1)## Bayes factor analysis
## --------------
## [1] Null, rho = 0 : 3.988183 ±0%
##
## Against denominator:
## Alternative, r = 1, rho =/= 0
## ---
## Bayes factor type: BFcorrelation, Jeffreys-beta*cor.test(LMratios$LMratioContrast, LMratios$LMratioGamma, method = "spearman")##
## Spearman's rank correlation rho
##
## data: LMratios$LMratioContrast and LMratios$LMratioGamma
## S = 5244, p-value = 0.3772
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.16662961/correlationBF(rank(LMratios$LMratioContrast, ties.method = "average"),
rank(LMratios$LMratioGamma, ties.method = "average"), rscale = 1)## Bayes factor analysis
## --------------
## [1] Null, rho = 0 : 3.043981 ±0%
##
## Against denominator:
## Alternative, r = 1, rho =/= 0
## ---
## Bayes factor type: BFcorrelation, Jeffreys-beta*nanCounts <- data %>%
group_by(ID) %>%
dplyr::summarize(non_na_count = sum(!is.na(gammapower)))
sprintf('Gamma power reported in %.0f%% of trials.',
100*(mean(nanCounts$non_na_count)/540))## [1] "Gamma power reported in 81% of trials."