Load libraries
library(reshape2)
library(reshape)
library(stringr)
library(plyr)
library(ggplot2)
library(nlme)
library(pastecs)
library(car)
library(psych)
library(fBasics)
library(Rmisc)
setwd("~/CompCat")We have a few different data sources here.
behavior.csvcontains the behavioral data (assessments, ages, etc.) for all of the participants.exp_data.csvcontains the experimental data from the main category learning task (collected in E-Prime 2.0).visualnorm.csvcontains aggregated similarity ratings for all of the stimulus pairings.
In this first step, we will select the relevant columns from exp_data and add in 3 types of visual similarity for each trial (probe-target, probe-distractor, and target-distractor), as well as a double-click indicator column for use in RT analyses.
#Read in data
dat0 <- read.csv("exp_data.csv")
visualnorm <- read.csv("visualnorm.csv")
#rename column 1 in visual norming
colnames(visualnorm)[1] <- "Robot"
#select just data from testing
dat1 <- subset(dat0, Block==2)
dat1$condition <- str_match(dat1$ExperimentName, "^([A-Za-z]{1,}_[A-Za-z]{1,})")[, 2]
dat1$train.type <- as.factor(str_match(dat1$ExperimentName, "^([A-Za-z]{1,})_")[, 2])
dat1$train.type <- revalue(dat1$train.type, c("Explicit" = "Directed", "Implicit" = "Undirected"))
dat1$info.type <- as.factor(str_match(dat1$ExperimentName, "^[A-Za-z]{1,}_([A-Za-z]{1,})_")[, 2])
#pick out the important variables --
#we can use Stimulus.Acc for test because there is only one response
# i.e. don't have to use NError
dat2 <- data.frame(dat1$Subject, dat1$condition, dat1$Trial, dat1$Probe_pic,
dat1$target_pic, dat1$distractor_pic, dat1$Stimulus.ACC,
dat1$Stimulus.RESP, dat1$Stimulus.RT, dat1$info.type, dat1$train.type)
dat2$doubleclick <- NA
#rename columns
names(dat2) <- c("Subj","condition", "Trial", "probepic","target_pic",
"distractor_pic","Acc","Resp","RT","info.type", "train.type", "doubleclick")
#if they double clicked, this column has a 1. If not, a 0
#double clicks should be removed when analyzing RT
dat2[dat2$Resp > 2, "doubleclick"] <- 1
dat2[dat2$Resp == 1, "doubleclick"] <- 0
#put visual norming data into a better format
vissim <- melt.data.frame(visualnorm, id.vars="Robot")
names(vissim) <- c("Rob1","Rob2","sim")
#add columns for visual similarity
#probe-target
dat1$vs.pt <- NA
#probe-distractor
dat1$vs.pd <- NA
#target-distractor
dat1$vs.td <- NA
#fill in probe-target visual similarity
#merge e-prime data with visual norming data
vs.pt <- merge(dat2, vissim, by.x=c("probepic", "target_pic"), by.y=c("Rob1", "Rob2"), all=T)
#remove excess columns
vs.pt <- vs.pt[!is.na(vs.pt$Subj),]
#sort by subject and trial
vs.pt.sort<- vs.pt[order(vs.pt$Subj, vs.pt$Trial),]
#put the visual similarity column in the overall data frame
dat2$vs.pt <- vs.pt.sort$sim
#fill in probe-distractor visual similarity
vs.pd <- merge(dat2, vissim, by.x=c("probepic", "distractor_pic"), by.y=c("Rob1", "Rob2"), all=T)
vs.pd <- vs.pd[!is.na(vs.pd$Subj),]
vs.pd.sort<- vs.pd[order(vs.pd$Subj, vs.pd$Trial),]
dat2$vs.pd <- vs.pd.sort$sim
#fill in target-distractor visual similarity
vs.td <- merge(dat2, vissim, by.x=c("target_pic", "distractor_pic"), by.y=c("Rob1", "Rob2"), all=T)
vs.td <- vs.td[!is.na(vs.td$Subj),]
vs.td.sort<- vs.td[order(vs.td$Subj, vs.td$Trial),]
dat2$vs.td <- vs.td.sort$simNow we will calculate the average accuracy for each subject for each condition.
acc <- data.frame(tapply(dat2$Acc, list(dat2$Subj, dat2$condition), mean))
acc$Subj <- as.numeric(rownames(acc))
acc.melt <- melt.data.frame(acc, id.vars = "Subj")
names(acc.melt) <- c("Subj", "Cond", "Acc")
acc.melt$train.type <- as.factor(str_match(acc.melt$Cond, "^([A-Za-z]{1,})_")[, 2])
acc.melt$train.type <- revalue(acc.melt$train.type, c("Explicit" = "Directed", "Implicit" = "Undirected"))
acc.melt$info.type <- as.factor(str_match(acc.melt$Cond, "^[A-Za-z]{1,}_([A-Za-z]{1,})")[, 2])Now we will merge all of the behavioral data into both the raw data and the aggregated data.
behavior <- read.csv("behavior.csv")
behavior$Group <- factor(behavior$Group, levels(behavior$Group)[c(2,1)])
dat3 <- merge(dat2, behavior, by = "Subj")
acc_beh <- merge(acc.melt, behavior, by = "Subj")For the group analysis, we want to only use people with a WA of greater than 95 and a KTEA of less than 90 or greater than 100.
acc_group <- acc_beh[acc_beh$KTEA_SS <= 90 | acc_beh$KTEA_SS >= 100,]
min(acc_group$WA_SS)## [1] 95
min(acc_group$P_IQ)## [1] 80
# check number of subjects
length(table(acc_group$Subj))## [1] 39
subs_groups <- data.frame(acc_group$Subj, acc_group$Group)
subs_groups <- unique(subs_groups)
table(subs_groups$acc_group.Group)##
## TD PC
## 20 19
beh_group <- behavior[behavior$KTEA_SS <= 90 | behavior$KTEA_SS >= 100,]
by(beh_group$KTEA_SS, list(beh_group$Group), mean)## : TD
## [1] 112.55
## --------------------------------------------------------
## : PC
## [1] 84.15789
by(beh_group$KTEA_SS, list(beh_group$Group), sd)## : TD
## [1] 8.816701
## --------------------------------------------------------
## : PC
## [1] 5.871429
t.test(beh_group$KTEA_SS ~ beh_group$Group)##
## Welch Two Sample t-test
##
## data: beh_group$KTEA_SS by beh_group$Group
## t = 11.891, df = 33.235, p-value = 1.613e-13
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 23.53559 33.24862
## sample estimates:
## mean in group TD mean in group PC
## 112.55000 84.15789
by(beh_group$WA_SS, list(beh_group$Group), mean)## : TD
## [1] 107.8
## --------------------------------------------------------
## : PC
## [1] 101.7895
by(beh_group$WA_SS, list(beh_group$Group), sd)## : TD
## [1] 8.563079
## --------------------------------------------------------
## : PC
## [1] 5.798266
t.test(beh_group$WA_SS ~ beh_group$Group)##
## Welch Two Sample t-test
##
## data: beh_group$WA_SS by beh_group$Group
## t = 2.578, df = 33.523, p-value = 0.01451
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.269912 10.751141
## sample estimates:
## mean in group TD mean in group PC
## 107.8000 101.7895
by(beh_group$P_IQ, list(beh_group$Group), mean)## : TD
## [1] 114.65
## --------------------------------------------------------
## : PC
## [1] 94.47368
by(beh_group$P_IQ, list(beh_group$Group), sd)## : TD
## [1] 12.35559
## --------------------------------------------------------
## : PC
## [1] 8.572361
t.test(beh_group$P_IQ ~ beh_group$Group)##
## Welch Two Sample t-test
##
## data: beh_group$P_IQ by beh_group$Group
## t = 5.9495, df = 33.936, p-value = 1.009e-06
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 13.28396 27.06868
## sample estimates:
## mean in group TD mean in group PC
## 114.65000 94.47368
by(acc_group$Acc, list(acc_group$info.type, acc_group$train.type), stat.desc)## : Nonverbal
## : Directed
## nbr.val nbr.null nbr.na min max
## 39.00000000 0.00000000 0.00000000 0.25000000 1.00000000
## range sum median mean SE.mean
## 0.75000000 28.50000000 0.73148148 0.73076923 0.03732858
## CI.mean.0.95 var std.dev coef.var
## 0.07556775 0.05434348 0.23311688 0.31900205
## --------------------------------------------------------
## : Verbal
## : Directed
## nbr.val nbr.null nbr.na min max
## 39.00000000 0.00000000 0.00000000 0.30555556 1.00000000
## range sum median mean SE.mean
## 0.69444444 31.67592593 0.92592593 0.81220323 0.03357472
## CI.mean.0.95 var std.dev coef.var
## 0.06796846 0.04396320 0.20967403 0.25815464
## --------------------------------------------------------
## : Nonverbal
## : Undirected
## nbr.val nbr.null nbr.na min max
## 39.00000000 0.00000000 0.00000000 0.17592593 1.00000000
## range sum median mean SE.mean
## 0.82407407 23.91666667 0.54629630 0.61324786 0.03531389
## CI.mean.0.95 var std.dev coef.var
## 0.07148923 0.04863576 0.22053516 0.35961830
## --------------------------------------------------------
## : Verbal
## : Undirected
## nbr.val nbr.null nbr.na min max
## 39.00000000 0.00000000 0.00000000 0.25925926 1.00000000
## range sum median mean SE.mean
## 0.74074074 27.79629630 0.62962963 0.71272555 0.03751887
## CI.mean.0.95 var std.dev coef.var
## 0.07595298 0.05489896 0.23430526 0.32874543
by(acc_group$Acc, list(acc_group$info.type, acc_group$train.type, acc_group$Group), stat.desc)## : Nonverbal
## : Directed
## : TD
## nbr.val nbr.null nbr.na min max
## 20.00000000 0.00000000 0.00000000 0.35185185 1.00000000
## range sum median mean SE.mean
## 0.64814815 16.32407407 0.93981481 0.81620370 0.04946411
## CI.mean.0.95 var std.dev coef.var
## 0.10352958 0.04893397 0.22121023 0.27102331
## --------------------------------------------------------
## : Verbal
## : Directed
## : TD
## nbr.val nbr.null nbr.na min max
## 20.00000000 0.00000000 0.00000000 0.65740741 1.00000000
## range sum median mean SE.mean
## 0.34259259 18.26851852 0.96296296 0.91342593 0.02489306
## CI.mean.0.95 var std.dev coef.var
## 0.05210176 0.01239328 0.11132513 0.12187647
## --------------------------------------------------------
## : Nonverbal
## : Undirected
## : TD
## nbr.val nbr.null nbr.na min max
## 20.00000000 0.00000000 0.00000000 0.41666667 1.00000000
## range sum median mean SE.mean
## 0.58333333 12.98148148 0.55092593 0.64907407 0.04726998
## CI.mean.0.95 var std.dev coef.var
## 0.09893720 0.04468901 0.21139776 0.32569127
## --------------------------------------------------------
## : Verbal
## : Undirected
## : TD
## nbr.val nbr.null nbr.na min max
## 20.00000000 0.00000000 0.00000000 0.42592593 1.00000000
## range sum median mean SE.mean
## 0.57407407 16.45370370 0.87037037 0.82268519 0.04042874
## CI.mean.0.95 var std.dev coef.var
## 0.08461831 0.03268965 0.18080280 0.21977155
## --------------------------------------------------------
## : Nonverbal
## : Directed
## : PC
## nbr.val nbr.null nbr.na min max
## 19.00000000 0.00000000 0.00000000 0.25000000 0.99074074
## range sum median mean SE.mean
## 0.74074074 12.17592593 0.66666667 0.64083821 0.04943132
## CI.mean.0.95 var std.dev coef.var
## 0.10385135 0.04642565 0.21546612 0.33622546
## --------------------------------------------------------
## : Verbal
## : Directed
## : PC
## nbr.val nbr.null nbr.na min max
## 19.00000000 0.00000000 0.00000000 0.30555556 0.99074074
## range sum median mean SE.mean
## 0.68518519 13.40740741 0.68518519 0.70565302 0.05446450
## CI.mean.0.95 var std.dev coef.var
## 0.11442567 0.05636125 0.23740525 0.33643341
## --------------------------------------------------------
## : Nonverbal
## : Undirected
## : PC
## nbr.val nbr.null nbr.na min max
## 19.00000000 0.00000000 0.00000000 0.17592593 1.00000000
## range sum median mean SE.mean
## 0.82407407 10.93518519 0.54629630 0.57553606 0.05260400
## CI.mean.0.95 var std.dev coef.var
## 0.11051690 0.05257643 0.22929550 0.39840337
## --------------------------------------------------------
## : Verbal
## : Undirected
## : PC
## nbr.val nbr.null nbr.na min max
## 19.00000000 0.00000000 0.00000000 0.25925926 1.00000000
## range sum median mean SE.mean
## 0.74074074 11.34259259 0.58333333 0.59697856 0.05322040
## CI.mean.0.95 var std.dev coef.var
## 0.11181191 0.05381581 0.23198235 0.38859410
To look for evidence of learning (acc > 0.5), we will use some t-tests.
ENV <- subset(acc_group, Cond == "Explicit_Nonverbal")
ENV.TD <- subset(ENV, Group == "TD")
ENV.SRCD <- subset(ENV, Group == "PC")
t.test(ENV.TD$Acc, mu = 0.5)##
## One Sample t-test
##
## data: ENV.TD$Acc
## t = 6.3926, df = 19, p-value = 3.941e-06
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.7126741 0.9197333
## sample estimates:
## mean of x
## 0.8162037
t.test(ENV.SRCD$Acc, mu = 0.5)##
## One Sample t-test
##
## data: ENV.SRCD$Acc
## t = 2.8492, df = 18, p-value = 0.01065
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.5369869 0.7446896
## sample estimates:
## mean of x
## 0.6408382
INV <- subset(acc_group, Cond == "Implicit_Nonverbal")
INV.TD <- subset(INV, Group == "TD")
INV.SRCD <- subset(INV, Group == "PC")
t.test(INV.TD$Acc, mu = 0.5)##
## One Sample t-test
##
## data: INV.TD$Acc
## t = 3.1537, df = 19, p-value = 0.005229
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.5501369 0.7480113
## sample estimates:
## mean of x
## 0.6490741
t.test(INV.SRCD$Acc, mu = 0.5)##
## One Sample t-test
##
## data: INV.SRCD$Acc
## t = 1.4359, df = 18, p-value = 0.1682
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.4650192 0.6860530
## sample estimates:
## mean of x
## 0.5755361
EV <- subset(acc_group, Cond == "Explicit_Verbal")
EV.TD <- subset(EV, Group == "TD")
EV.SRCD <- subset(EV, Group == "PC")
t.test(EV.TD$Acc, mu = 0.5)##
## One Sample t-test
##
## data: EV.TD$Acc
## t = 16.608, df = 19, p-value = 9.061e-13
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.8613242 0.9655277
## sample estimates:
## mean of x
## 0.9134259
t.test(EV.SRCD$Acc, mu = 0.5)##
## One Sample t-test
##
## data: EV.SRCD$Acc
## t = 3.7759, df = 18, p-value = 0.001384
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.5912274 0.8200787
## sample estimates:
## mean of x
## 0.705653
IV <- subset(acc_group, Cond == "Implicit_Verbal")
IV.TD <- subset(IV, Group == "TD")
IV.SRCD <- subset(IV, Group == "PC")
t.test(IV.TD$Acc, mu = 0.5)##
## One Sample t-test
##
## data: IV.TD$Acc
## t = 7.9816, df = 19, p-value = 1.734e-07
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.7380669 0.9073035
## sample estimates:
## mean of x
## 0.8226852
t.test(IV.SRCD$Acc, mu = 0.5)##
## One Sample t-test
##
## data: IV.SRCD$Acc
## t = 1.8222, df = 18, p-value = 0.08508
## alternative hypothesis: true mean is not equal to 0.5
## 95 percent confidence interval:
## 0.4851666 0.7087905
## sample estimates:
## mean of x
## 0.5969786
Group x Information Type x Training Type
acc_group$acc.logit <- car::logit(acc_group$Acc)
# add task effects
m0 <- lme(acc.logit ~ 1, random = ~1|Subj, data = acc_group, method = "ML")
summary(m0)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_group
## AIC BIC logLik
## 499.8637 509.0133 -246.9319
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 1.143842 0.9210212
##
## Fixed effects: acc.logit ~ 1
## Value Std.Error DF t-value p-value
## (Intercept) 1.309999 0.198084 117 6.613348 0
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.50922445 -0.55394788 -0.01941236 0.52004838 2.50742954
##
## Number of Observations: 156
## Number of Groups: 39
m0.0 <- lme(acc.logit ~ train.type + info.type, random = ~1|Subj, data = acc_group, method = "ML")
anova(m0,m0.0)## Model df AIC BIC logLik Test L.Ratio p-value
## m0 1 3 499.8637 509.0133 -246.9318
## m0.0 2 5 465.4306 480.6798 -227.7153 1 vs 2 38.43316 <.0001
# add task effects and group over and above PIQ and decoding
m0.5 <- lme(acc.logit ~ P_IQ + WA_SS, random = ~1|Subj, data = acc_group, method = "ML")
m1 <- lme(acc.logit ~ P_IQ + WA_SS + Group * train.type * info.type, random = ~1|Subj, data = acc_group, method = "ML")
summary(m1)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_group
## AIC BIC logLik
## 456.8754 493.4736 -216.4377
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.9881981 0.7472297
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + Group * train.type * info.type
## Value Std.Error DF
## (Intercept) -4.010552 3.0467522 111
## P_IQ 0.004107 0.0170469 35
## WA_SS 0.051127 0.0247818 35
## GroupPC -0.813277 0.5399742 35
## train.typeUndirected -1.109413 0.2442530 111
## info.typeVerbal 0.561352 0.2442530 111
## GroupPC:train.typeUndirected 0.852581 0.3499415 111
## GroupPC:info.typeVerbal -0.135719 0.3499415 111
## train.typeUndirected:info.typeVerbal 0.449453 0.3454260 111
## GroupPC:train.typeUndirected:info.typeVerbal -0.726304 0.4948921 111
## t-value p-value
## (Intercept) -1.316337 0.1908
## P_IQ 0.240905 0.8110
## WA_SS 2.063093 0.0466
## GroupPC -1.506141 0.1410
## train.typeUndirected -4.542066 0.0000
## info.typeVerbal 2.298241 0.0234
## GroupPC:train.typeUndirected 2.436353 0.0164
## GroupPC:info.typeVerbal -0.387834 0.6989
## train.typeUndirected:info.typeVerbal 1.301155 0.1959
## GroupPC:train.typeUndirected:info.typeVerbal -1.467601 0.1450
## Correlation:
## (Intr) P_IQ WA_SS GropPC
## P_IQ -0.494
## WA_SS -0.769 -0.168
## GroupPC -0.577 0.591 0.169
## train.typeUndirected -0.040 0.000 0.000 0.226
## info.typeVerbal -0.040 0.000 0.000 0.226
## GroupPC:train.typeUndirected 0.028 0.000 0.000 -0.324
## GroupPC:info.typeVerbal 0.028 0.000 0.000 -0.324
## train.typeUndirected:info.typeVerbal 0.028 0.000 0.000 -0.160
## GroupPC:train.typeUndirected:info.typeVerbal -0.020 0.000 0.000 0.229
## trn.tU inf.tV GrPC:.U GPC:.V
## P_IQ
## WA_SS
## GroupPC
## train.typeUndirected
## info.typeVerbal 0.500
## GroupPC:train.typeUndirected -0.698 -0.349
## GroupPC:info.typeVerbal -0.349 -0.698 0.500
## train.typeUndirected:info.typeVerbal -0.707 -0.707 0.494 0.494
## GroupPC:train.typeUndirected:info.typeVerbal 0.494 0.494 -0.707 -0.707
## t.U:.V
## P_IQ
## WA_SS
## GroupPC
## train.typeUndirected
## info.typeVerbal
## GroupPC:train.typeUndirected
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## GroupPC:train.typeUndirected:info.typeVerbal -0.698
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.15519128 -0.56930008 0.02437132 0.58799812 2.55152492
##
## Number of Observations: 156
## Number of Groups: 39
anova(m0.5,m1)## Model df AIC BIC logLik Test L.Ratio p-value
## m0.5 1 5 493.5303 508.7796 -241.7652
## m1 2 12 456.8754 493.4736 -216.4377 1 vs 2 50.65496 <.0001
## unpacking interaction between group and training type
PCSubset <- acc_group$Group=="PC"
TDSubset <- acc_group$Group=="TD"
tdModel <- lme(acc.logit ~ P_IQ + WA_SS + train.type * info.type, random = ~1|Subj, data = acc_group, subset = PCSubset, method = "ML")
summary(tdModel)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_group
## Subset: PCSubset
## AIC BIC logLik
## 203.5734 222.2192 -93.78668
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.9962869 0.6117928
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + train.type * info.type
## Value Std.Error DF t-value
## (Intercept) -13.150921 5.528892 54 -2.3785816
## P_IQ 0.028418 0.029964 16 0.9484007
## WA_SS 0.110370 0.044300 16 2.4914112
## train.typeUndirected -0.256832 0.206824 54 -1.2417931
## info.typeVerbal 0.425633 0.206824 54 2.0579513
## train.typeUndirected:info.typeVerbal -0.276851 0.292493 54 -0.9465229
## p-value
## (Intercept) 0.0209
## P_IQ 0.3570
## WA_SS 0.0241
## train.typeUndirected 0.2197
## info.typeVerbal 0.0444
## train.typeUndirected:info.typeVerbal 0.3481
## Correlation:
## (Intr) P_IQ WA_SS trn.tU inf.tV
## P_IQ -0.580
## WA_SS -0.859 0.084
## train.typeUndirected -0.019 0.000 0.000
## info.typeVerbal -0.019 0.000 0.000 0.500
## train.typeUndirected:info.typeVerbal 0.013 0.000 0.000 -0.707 -0.707
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.97724996 -0.52010884 -0.02230884 0.46071853 3.10611776
##
## Number of Observations: 76
## Number of Groups: 19
pcModel <- lme(acc.logit ~ P_IQ + WA_SS + train.type * info.type, random = ~1|Subj, data = acc_group, subset = TDSubset, method = "ML")
summary(pcModel)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_group
## Subset: TDSubset
## AIC BIC logLik
## 251.4228 270.479 -117.7114
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.8840984 0.8562773
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + train.type * info.type
## Value Std.Error DF t-value
## (Intercept) -1.0071643 3.279177 57 -0.307139
## P_IQ -0.0011107 0.019757 17 -0.056218
## WA_SS 0.0288154 0.028507 17 1.010827
## train.typeUndirected -1.1094134 0.281542 57 -3.940486
## info.typeVerbal 0.5613524 0.281542 57 1.993848
## train.typeUndirected:info.typeVerbal 0.4494528 0.398161 57 1.128822
## p-value
## (Intercept) 0.7599
## P_IQ 0.9558
## WA_SS 0.3263
## train.typeUndirected 0.0002
## info.typeVerbal 0.0510
## train.typeUndirected:info.typeVerbal 0.2637
## Correlation:
## (Intr) P_IQ WA_SS trn.tU inf.tV
## P_IQ -0.428
## WA_SS -0.743 -0.280
## train.typeUndirected -0.043 0.000 0.000
## info.typeVerbal -0.043 0.000 0.000 0.500
## train.typeUndirected:info.typeVerbal 0.030 0.000 0.000 -0.707 -0.707
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.9023423 -0.7801925 0.1037513 0.6677532 1.9013446
##
## Number of Observations: 80
## Number of Groups: 20
Group x Information Type x Training Type x Order
# add order
m2 <- lme(acc.logit ~ P_IQ + WA_SS + Order * Group * train.type * info.type, random = ~1|Subj, data = acc_group, method = "ML")
summary(m2)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_group
## AIC BIC logLik
## 431.047 492.0442 -195.5235
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.9457062 0.6369142
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + Order * Group * train.type * info.type
## Value
## (Intercept) -4.717762
## P_IQ 0.005656
## WA_SS 0.054314
## OrderUndirected First 0.413625
## GroupPC -0.220541
## train.typeUndirected -0.236071
## info.typeVerbal 0.642907
## OrderUndirected First:GroupPC -1.165608
## OrderUndirected First:train.typeUndirected -1.940760
## GroupPC:train.typeUndirected 0.085819
## OrderUndirected First:info.typeVerbal -0.181233
## GroupPC:info.typeVerbal -0.392777
## train.typeUndirected:info.typeVerbal 0.341052
## OrderUndirected First:GroupPC:train.typeUndirected 1.715758
## OrderUndirected First:GroupPC:info.typeVerbal 0.551739
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.240891
## GroupPC:train.typeUndirected:info.typeVerbal -0.391385
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal -0.719096
## Std.Error
## (Intercept) 3.0008669
## P_IQ 0.0165959
## WA_SS 0.0246781
## OrderUndirected First 0.5562268
## GroupPC 0.6144174
## train.typeUndirected 0.2887503
## info.typeVerbal 0.2887503
## OrderUndirected First:GroupPC 0.7928222
## OrderUndirected First:train.typeUndirected 0.4304435
## GroupPC:train.typeUndirected 0.4184389
## OrderUndirected First:info.typeVerbal 0.4304435
## GroupPC:info.typeVerbal 0.4184389
## train.typeUndirected:info.typeVerbal 0.4083545
## OrderUndirected First:GroupPC:train.typeUndirected 0.6155492
## OrderUndirected First:GroupPC:info.typeVerbal 0.6155492
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.6087390
## GroupPC:train.typeUndirected:info.typeVerbal 0.5917619
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 0.8705180
## DF
## (Intercept) 105
## P_IQ 33
## WA_SS 33
## OrderUndirected First 33
## GroupPC 33
## train.typeUndirected 105
## info.typeVerbal 105
## OrderUndirected First:GroupPC 33
## OrderUndirected First:train.typeUndirected 105
## GroupPC:train.typeUndirected 105
## OrderUndirected First:info.typeVerbal 105
## GroupPC:info.typeVerbal 105
## train.typeUndirected:info.typeVerbal 105
## OrderUndirected First:GroupPC:train.typeUndirected 105
## OrderUndirected First:GroupPC:info.typeVerbal 105
## OrderUndirected First:train.typeUndirected:info.typeVerbal 105
## GroupPC:train.typeUndirected:info.typeVerbal 105
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 105
## t-value
## (Intercept) -1.572133
## P_IQ 0.340780
## WA_SS 2.200889
## OrderUndirected First 0.743626
## GroupPC -0.358943
## train.typeUndirected -0.817563
## info.typeVerbal 2.226517
## OrderUndirected First:GroupPC -1.470201
## OrderUndirected First:train.typeUndirected -4.508745
## GroupPC:train.typeUndirected 0.205093
## OrderUndirected First:info.typeVerbal -0.421038
## GroupPC:info.typeVerbal -0.938673
## train.typeUndirected:info.typeVerbal 0.835185
## OrderUndirected First:GroupPC:train.typeUndirected 2.787362
## OrderUndirected First:GroupPC:info.typeVerbal 0.896337
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.395722
## GroupPC:train.typeUndirected:info.typeVerbal -0.661389
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal -0.826055
## p-value
## (Intercept) 0.1189
## P_IQ 0.7354
## WA_SS 0.0348
## OrderUndirected First 0.4624
## GroupPC 0.7219
## train.typeUndirected 0.4155
## info.typeVerbal 0.0281
## OrderUndirected First:GroupPC 0.1510
## OrderUndirected First:train.typeUndirected 0.0000
## GroupPC:train.typeUndirected 0.8379
## OrderUndirected First:info.typeVerbal 0.6746
## GroupPC:info.typeVerbal 0.3501
## train.typeUndirected:info.typeVerbal 0.4055
## OrderUndirected First:GroupPC:train.typeUndirected 0.0063
## OrderUndirected First:GroupPC:info.typeVerbal 0.3721
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.6931
## GroupPC:train.typeUndirected:info.typeVerbal 0.5098
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 0.4106
## Correlation:
## (Intr)
## P_IQ -0.495
## WA_SS -0.775
## OrderUndirected First 0.114
## GroupPC -0.428
## train.typeUndirected -0.048
## info.typeVerbal -0.048
## OrderUndirected First:GroupPC -0.117
## OrderUndirected First:train.typeUndirected 0.032
## GroupPC:train.typeUndirected 0.033
## OrderUndirected First:info.typeVerbal 0.032
## GroupPC:info.typeVerbal 0.033
## train.typeUndirected:info.typeVerbal 0.034
## OrderUndirected First:GroupPC:train.typeUndirected -0.023
## OrderUndirected First:GroupPC:info.typeVerbal -0.023
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.023
## GroupPC:train.typeUndirected:info.typeVerbal -0.023
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 0.016
## P_IQ
## P_IQ
## WA_SS -0.151
## OrderUndirected First -0.056
## GroupPC 0.489
## train.typeUndirected 0.000
## info.typeVerbal 0.000
## OrderUndirected First:GroupPC 0.033
## OrderUndirected First:train.typeUndirected 0.000
## GroupPC:train.typeUndirected 0.000
## OrderUndirected First:info.typeVerbal 0.000
## GroupPC:info.typeVerbal 0.000
## train.typeUndirected:info.typeVerbal 0.000
## OrderUndirected First:GroupPC:train.typeUndirected 0.000
## OrderUndirected First:GroupPC:info.typeVerbal 0.000
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.000
## GroupPC:train.typeUndirected:info.typeVerbal 0.000
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 0.000
## WA_SS
## P_IQ
## WA_SS
## OrderUndirected First -0.182
## GroupPC 0.056
## train.typeUndirected 0.000
## info.typeVerbal 0.000
## OrderUndirected First:GroupPC 0.175
## OrderUndirected First:train.typeUndirected 0.000
## GroupPC:train.typeUndirected 0.000
## OrderUndirected First:info.typeVerbal 0.000
## GroupPC:info.typeVerbal 0.000
## train.typeUndirected:info.typeVerbal 0.000
## OrderUndirected First:GroupPC:train.typeUndirected 0.000
## OrderUndirected First:GroupPC:info.typeVerbal 0.000
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.000
## GroupPC:train.typeUndirected:info.typeVerbal 0.000
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 0.000
## OrdrUF
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC 0.338
## train.typeUndirected 0.260
## info.typeVerbal 0.260
## OrderUndirected First:GroupPC -0.710
## OrderUndirected First:train.typeUndirected -0.387
## GroupPC:train.typeUndirected -0.179
## OrderUndirected First:info.typeVerbal -0.387
## GroupPC:info.typeVerbal -0.179
## train.typeUndirected:info.typeVerbal -0.184
## OrderUndirected First:GroupPC:train.typeUndirected 0.271
## OrderUndirected First:GroupPC:info.typeVerbal 0.271
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.274
## GroupPC:train.typeUndirected:info.typeVerbal 0.127
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal -0.191
## GropPC
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected 0.235
## info.typeVerbal 0.235
## OrderUndirected First:GroupPC -0.536
## OrderUndirected First:train.typeUndirected -0.158
## GroupPC:train.typeUndirected -0.341
## OrderUndirected First:info.typeVerbal -0.158
## GroupPC:info.typeVerbal -0.341
## train.typeUndirected:info.typeVerbal -0.166
## OrderUndirected First:GroupPC:train.typeUndirected 0.231
## OrderUndirected First:GroupPC:info.typeVerbal 0.231
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.111
## GroupPC:train.typeUndirected:info.typeVerbal 0.241
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal -0.164
## trn.tU
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal 0.500
## OrderUndirected First:GroupPC -0.182
## OrderUndirected First:train.typeUndirected -0.671
## GroupPC:train.typeUndirected -0.690
## OrderUndirected First:info.typeVerbal -0.335
## GroupPC:info.typeVerbal -0.345
## train.typeUndirected:info.typeVerbal -0.707
## OrderUndirected First:GroupPC:train.typeUndirected 0.469
## OrderUndirected First:GroupPC:info.typeVerbal 0.235
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.474
## GroupPC:train.typeUndirected:info.typeVerbal 0.488
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal -0.332
## inf.tV
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal
## OrderUndirected First:GroupPC -0.182
## OrderUndirected First:train.typeUndirected -0.335
## GroupPC:train.typeUndirected -0.345
## OrderUndirected First:info.typeVerbal -0.671
## GroupPC:info.typeVerbal -0.690
## train.typeUndirected:info.typeVerbal -0.707
## OrderUndirected First:GroupPC:train.typeUndirected 0.235
## OrderUndirected First:GroupPC:info.typeVerbal 0.469
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.474
## GroupPC:train.typeUndirected:info.typeVerbal 0.488
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal -0.332
## OrUF:GPC
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal
## OrderUndirected First:GroupPC
## OrderUndirected First:train.typeUndirected 0.271
## GroupPC:train.typeUndirected 0.264
## OrderUndirected First:info.typeVerbal 0.271
## GroupPC:info.typeVerbal 0.264
## train.typeUndirected:info.typeVerbal 0.129
## OrderUndirected First:GroupPC:train.typeUndirected -0.388
## OrderUndirected First:GroupPC:info.typeVerbal -0.388
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.192
## GroupPC:train.typeUndirected:info.typeVerbal -0.187
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 0.274
## OrUF:.U
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal
## OrderUndirected First:GroupPC
## OrderUndirected First:train.typeUndirected
## GroupPC:train.typeUndirected 0.463
## OrderUndirected First:info.typeVerbal 0.500
## GroupPC:info.typeVerbal 0.231
## train.typeUndirected:info.typeVerbal 0.474
## OrderUndirected First:GroupPC:train.typeUndirected -0.699
## OrderUndirected First:GroupPC:info.typeVerbal -0.350
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.707
## GroupPC:train.typeUndirected:info.typeVerbal -0.327
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 0.494
## GrPC:.U
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal
## OrderUndirected First:GroupPC
## OrderUndirected First:train.typeUndirected
## GroupPC:train.typeUndirected
## OrderUndirected First:info.typeVerbal 0.231
## GroupPC:info.typeVerbal 0.500
## train.typeUndirected:info.typeVerbal 0.488
## OrderUndirected First:GroupPC:train.typeUndirected -0.680
## OrderUndirected First:GroupPC:info.typeVerbal -0.340
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.327
## GroupPC:train.typeUndirected:info.typeVerbal -0.707
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 0.481
## OUF:.V
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal
## OrderUndirected First:GroupPC
## OrderUndirected First:train.typeUndirected
## GroupPC:train.typeUndirected
## OrderUndirected First:info.typeVerbal
## GroupPC:info.typeVerbal 0.463
## train.typeUndirected:info.typeVerbal 0.474
## OrderUndirected First:GroupPC:train.typeUndirected -0.350
## OrderUndirected First:GroupPC:info.typeVerbal -0.699
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.707
## GroupPC:train.typeUndirected:info.typeVerbal -0.327
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 0.494
## GPC:.V
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal
## OrderUndirected First:GroupPC
## OrderUndirected First:train.typeUndirected
## GroupPC:train.typeUndirected
## OrderUndirected First:info.typeVerbal
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal 0.488
## OrderUndirected First:GroupPC:train.typeUndirected -0.340
## OrderUndirected First:GroupPC:info.typeVerbal -0.680
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.327
## GroupPC:train.typeUndirected:info.typeVerbal -0.707
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 0.481
## t.U:.V
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal
## OrderUndirected First:GroupPC
## OrderUndirected First:train.typeUndirected
## GroupPC:train.typeUndirected
## OrderUndirected First:info.typeVerbal
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## OrderUndirected First:GroupPC:train.typeUndirected -0.332
## OrderUndirected First:GroupPC:info.typeVerbal -0.332
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.671
## GroupPC:train.typeUndirected:info.typeVerbal -0.690
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal 0.469
## OrUF:GPC:.U
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal
## OrderUndirected First:GroupPC
## OrderUndirected First:train.typeUndirected
## GroupPC:train.typeUndirected
## OrderUndirected First:info.typeVerbal
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## OrderUndirected First:GroupPC:train.typeUndirected
## OrderUndirected First:GroupPC:info.typeVerbal 0.500
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.494
## GroupPC:train.typeUndirected:info.typeVerbal 0.481
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal -0.707
## OUF:GPC:.V
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal
## OrderUndirected First:GroupPC
## OrderUndirected First:train.typeUndirected
## GroupPC:train.typeUndirected
## OrderUndirected First:info.typeVerbal
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## OrderUndirected First:GroupPC:train.typeUndirected
## OrderUndirected First:GroupPC:info.typeVerbal
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.494
## GroupPC:train.typeUndirected:info.typeVerbal 0.481
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal -0.707
## OUF:.U:
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal
## OrderUndirected First:GroupPC
## OrderUndirected First:train.typeUndirected
## GroupPC:train.typeUndirected
## OrderUndirected First:info.typeVerbal
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## OrderUndirected First:GroupPC:train.typeUndirected
## OrderUndirected First:GroupPC:info.typeVerbal
## OrderUndirected First:train.typeUndirected:info.typeVerbal
## GroupPC:train.typeUndirected:info.typeVerbal 0.463
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal -0.699
## GPC:.U:
## P_IQ
## WA_SS
## OrderUndirected First
## GroupPC
## train.typeUndirected
## info.typeVerbal
## OrderUndirected First:GroupPC
## OrderUndirected First:train.typeUndirected
## GroupPC:train.typeUndirected
## OrderUndirected First:info.typeVerbal
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## OrderUndirected First:GroupPC:train.typeUndirected
## OrderUndirected First:GroupPC:info.typeVerbal
## OrderUndirected First:train.typeUndirected:info.typeVerbal
## GroupPC:train.typeUndirected:info.typeVerbal
## OrderUndirected First:GroupPC:train.typeUndirected:info.typeVerbal -0.680
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.64257744 -0.53735950 0.04165279 0.54898662 2.76645524
##
## Number of Observations: 156
## Number of Groups: 39
anova(m1,m2)## Model df AIC BIC logLik Test L.Ratio p-value
## m1 1 12 456.8754 493.4736 -216.4377
## m2 2 20 431.0470 492.0442 -195.5235 1 vs 2 41.82832 <.0001
## unpacking interaction between group, training type, and order
dirfSubset <- acc_group$Order=="Directed First"
undirfSubset <- acc_group$Order=="Undirected First"
dirfModel <- lme(acc.logit ~ P_IQ + WA_SS + Group * train.type * info.type, random = ~1|Subj, data = acc_group, subset = dirfSubset, method = "ML")
summary(dirfModel)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_group
## Subset: dirfSubset
## AIC BIC logLik
## 225.2272 254.397 -100.6136
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 1.100629 0.5663355
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + Group * train.type * info.type
## Value Std.Error DF
## (Intercept) -11.557206 5.537172 57
## P_IQ 0.024390 0.022768 17
## WA_SS 0.098877 0.042041 17
## GroupPC 0.280734 0.746371 17
## train.typeUndirected -0.236071 0.257286 57
## info.typeVerbal 0.642907 0.257286 57
## GroupPC:train.typeUndirected 0.085819 0.372843 57
## GroupPC:info.typeVerbal -0.392777 0.372843 57
## train.typeUndirected:info.typeVerbal 0.341052 0.363857 57
## GroupPC:train.typeUndirected:info.typeVerbal -0.391385 0.527279 57
## t-value p-value
## (Intercept) -2.0872038 0.0414
## P_IQ 1.0712619 0.2990
## WA_SS 2.3519296 0.0310
## GroupPC 0.3761322 0.7115
## train.typeUndirected -0.9175449 0.3627
## info.typeVerbal 2.4988036 0.0154
## GroupPC:train.typeUndirected 0.2301743 0.8188
## GroupPC:info.typeVerbal -1.0534654 0.2966
## train.typeUndirected:info.typeVerbal 0.9373224 0.3525
## GroupPC:train.typeUndirected:info.typeVerbal -0.7422726 0.4610
## Correlation:
## (Intr) P_IQ WA_SS GropPC
## P_IQ -0.608
## WA_SS -0.886 0.176
## GroupPC -0.554 0.608 0.288
## train.typeUndirected -0.023 0.000 0.000 0.172
## info.typeVerbal -0.023 0.000 0.000 0.172
## GroupPC:train.typeUndirected 0.016 0.000 0.000 -0.250
## GroupPC:info.typeVerbal 0.016 0.000 0.000 -0.250
## train.typeUndirected:info.typeVerbal 0.016 0.000 0.000 -0.122
## GroupPC:train.typeUndirected:info.typeVerbal -0.011 0.000 0.000 0.177
## trn.tU inf.tV GrPC:.U GPC:.V
## P_IQ
## WA_SS
## GroupPC
## train.typeUndirected
## info.typeVerbal 0.500
## GroupPC:train.typeUndirected -0.690 -0.345
## GroupPC:info.typeVerbal -0.345 -0.690 0.500
## train.typeUndirected:info.typeVerbal -0.707 -0.707 0.488 0.488
## GroupPC:train.typeUndirected:info.typeVerbal 0.488 0.488 -0.707 -0.707
## t.U:.V
## P_IQ
## WA_SS
## GroupPC
## train.typeUndirected
## info.typeVerbal
## GroupPC:train.typeUndirected
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## GroupPC:train.typeUndirected:info.typeVerbal -0.690
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.46400991 -0.49765893 -0.02766138 0.44774444 2.59760901
##
## Number of Observations: 84
## Number of Groups: 21
undirfModel <- lme(acc.logit ~ P_IQ + WA_SS + Group * train.type * info.type, random = ~1|Subj, data = acc_group, subset = undirfSubset, method = "ML")
summary(undirfModel)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_group
## Subset: undirfSubset
## AIC BIC logLik
## 201.5436 228.8636 -88.7718
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.5593535 0.7104487
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + Group * train.type * info.type
## Value Std.Error DF
## (Intercept) 0.2447136 2.6818412 48
## P_IQ -0.0196630 0.0254951 14
## WA_SS 0.0397407 0.0281648 14
## GroupPC -2.0719266 0.6396112 14
## train.typeUndirected -2.1768314 0.3609083 48
## info.typeVerbal 0.4616742 0.3609083 48
## GroupPC:train.typeUndirected 1.8015773 0.5104014 48
## GroupPC:info.typeVerbal 0.1589623 0.5104014 48
## train.typeUndirected:info.typeVerbal 0.5819431 0.5104014 48
## GroupPC:train.typeUndirected:info.typeVerbal -1.1104808 0.7218166 48
## t-value p-value
## (Intercept) 0.091248 0.9277
## P_IQ -0.771247 0.4534
## WA_SS 1.411008 0.1801
## GroupPC -3.239353 0.0059
## train.typeUndirected -6.031536 0.0000
## info.typeVerbal 1.279201 0.2070
## GroupPC:train.typeUndirected 3.529727 0.0009
## GroupPC:info.typeVerbal 0.311446 0.7568
## train.typeUndirected:info.typeVerbal 1.140167 0.2599
## GroupPC:train.typeUndirected:info.typeVerbal -1.538453 0.1305
## Correlation:
## (Intr) P_IQ WA_SS GropPC
## P_IQ -0.392
## WA_SS -0.477 -0.616
## GroupPC -0.595 0.618 -0.129
## train.typeUndirected -0.067 0.000 0.000 0.282
## info.typeVerbal -0.067 0.000 0.000 0.282
## GroupPC:train.typeUndirected 0.048 0.000 0.000 -0.399
## GroupPC:info.typeVerbal 0.048 0.000 0.000 -0.399
## train.typeUndirected:info.typeVerbal 0.048 0.000 0.000 -0.199
## GroupPC:train.typeUndirected:info.typeVerbal -0.034 0.000 0.000 0.282
## trn.tU inf.tV GrPC:.U GPC:.V
## P_IQ
## WA_SS
## GroupPC
## train.typeUndirected
## info.typeVerbal 0.500
## GroupPC:train.typeUndirected -0.707 -0.354
## GroupPC:info.typeVerbal -0.354 -0.707 0.500
## train.typeUndirected:info.typeVerbal -0.707 -0.707 0.500 0.500
## GroupPC:train.typeUndirected:info.typeVerbal 0.500 0.500 -0.707 -0.707
## t.U:.V
## P_IQ
## WA_SS
## GroupPC
## train.typeUndirected
## info.typeVerbal
## GroupPC:train.typeUndirected
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## GroupPC:train.typeUndirected:info.typeVerbal -0.707
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.4823932 -0.5095702 0.1595342 0.6010480 2.2698934
##
## Number of Observations: 72
## Number of Groups: 18
## unpacking interaction between group and training type in undirected-first condition
p.implicitfSubset <- acc_group$Order=="Undirected First" & acc_group$Group =="PC"
t.implicitfSubset <- acc_group$Order=="Undirected First" & acc_group$Group =="TD"
p.undirfModel <- lme(acc.logit ~ P_IQ + WA_SS + train.type * info.type, random = ~1|Subj, data = acc_group, subset = p.implicitfSubset, method = "ML")
summary(p.undirfModel)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_group
## Subset: p.implicitfSubset
## AIC BIC logLik
## 95.65673 108.3249 -39.82837
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.3863598 0.6561674
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + train.type * info.type
## Value Std.Error DF t-value
## (Intercept) 2.8571825 4.445148 24 0.6427644
## P_IQ -0.0000869 0.037982 6 -0.0022881
## WA_SS -0.0249450 0.033716 6 -0.7398447
## train.typeUndirected -0.3752541 0.338843 24 -1.1074557
## info.typeVerbal 0.6206364 0.338843 24 1.8316321
## train.typeUndirected:info.typeVerbal -0.5285377 0.479197 24 -1.1029656
## p-value
## (Intercept) 0.5265
## P_IQ 0.9982
## WA_SS 0.4873
## train.typeUndirected 0.2791
## info.typeVerbal 0.0794
## train.typeUndirected:info.typeVerbal 0.2810
## Correlation:
## (Intr) P_IQ WA_SS trn.tU inf.tV
## P_IQ -0.657
## WA_SS -0.608 -0.196
## train.typeUndirected -0.038 0.000 0.000
## info.typeVerbal -0.038 0.000 0.000 0.500
## train.typeUndirected:info.typeVerbal 0.027 0.000 0.000 -0.707 -0.707
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.83337084 -0.60269591 0.04352239 0.58724174 2.77328674
##
## Number of Observations: 36
## Number of Groups: 9
t.undirfModel <- lme(acc.logit ~ P_IQ + WA_SS + train.type * info.type, random = ~1|Subj, data = acc_group, subset = t.implicitfSubset, method = "ML")
summary(t.undirfModel)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_group
## Subset: t.implicitfSubset
## AIC BIC logLik
## 107.3706 120.0388 -45.68532
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.4936237 0.7608677
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + train.type * info.type
## Value Std.Error DF t-value
## (Intercept) -1.5834561 2.8342056 24 -0.558695
## P_IQ -0.0540717 0.0314484 6 -1.719376
## WA_SS 0.0926086 0.0369728 6 2.504775
## train.typeUndirected -2.1768314 0.3929104 24 -5.540275
## info.typeVerbal 0.4616742 0.3929104 24 1.175011
## train.typeUndirected:info.typeVerbal 0.5819431 0.5556592 24 1.047302
## p-value
## (Intercept) 0.5815
## P_IQ 0.1363
## WA_SS 0.0462
## train.typeUndirected 0.0000
## info.typeVerbal 0.2515
## train.typeUndirected:info.typeVerbal 0.3054
## Correlation:
## (Intr) P_IQ WA_SS trn.tU inf.tV
## P_IQ -0.226
## WA_SS -0.484 -0.740
## train.typeUndirected -0.069 0.000 0.000
## info.typeVerbal -0.069 0.000 0.000 0.500
## train.typeUndirected:info.typeVerbal 0.049 0.000 0.000 -0.707 -0.707
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.3183410 -0.3831687 0.2084961 0.5069578 1.9480321
##
## Number of Observations: 36
## Number of Groups: 9
acc_summary1 <- summarySE(data = acc_group, "Acc", groupvars = c("train.type", "Group", "info.type"))
p1 <- ggplot(acc_group, aes(info.type, Acc, fill = Group)) + geom_violin() +
xlab("Training Type") + ylab("Accuracy") +
facet_grid(.~train.type) +
geom_point(aes(y = Acc), size = 2, position = position_dodge((width = 0.90)), data = acc_summary1) +
geom_errorbar(aes(ymin = Acc-se, ymax = Acc+se),
width = 0.20, position = position_dodge((width = 0.90)), data = acc_summary1) + theme_bw()
p1
acc_summary2 <- summarySE(data = acc_group, "Acc", groupvars = c("train.type", "Group", "Order"))
p2 <- ggplot(acc_group, aes(train.type, Acc, fill = Group)) + geom_violin() +
xlab("Training Type") + ylab("Accuracy") +
facet_grid(.~Order) +
geom_point(aes(y = Acc), size = 2, position = position_dodge((width = 0.90)), data = acc_summary2) +
geom_errorbar(aes(ymin = Acc-se, ymax = Acc+se),
width = 0.20, position = position_dodge((width = 0.90)), data = acc_summary2) + theme_bw()
p2
acc_beh$acc.logit <- car::logit(acc_beh$Acc)
# base model
m0 <- lme(acc.logit ~ 1, random = ~1|Subj, data = acc_beh, method = "ML")
summary(m0)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_beh
## AIC BIC logLik
## 614.9412 624.586 -304.4706
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 1.043025 1.033272
##
## Fixed effects: acc.logit ~ 1
## Value Std.Error DF t-value p-value
## (Intercept) 1.375768 0.1720856 138 7.994675 0
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.30071311 -0.55892441 -0.06396818 0.55626204 2.14075704
##
## Number of Observations: 184
## Number of Groups: 46
# adding in task conditions
m0.0 <- lme(acc.logit ~ train.type + info.type, random = ~1|Subj, data = acc_beh, method = "ML")
anova(m0,m0.0)## Model df AIC BIC logLik Test L.Ratio p-value
## m0 1 3 614.9412 624.5860 -304.4706
## m0.0 2 5 565.0608 581.1355 -277.5304 1 vs 2 53.88047 <.0001
# adding in individual difference measures
m0.5 <- lme(acc.logit ~ P_IQ + WA_SS, random = ~1|Subj, data = acc_beh, method = "ML")
# full model
m1 <- lme(acc.logit ~ P_IQ + WA_SS + KTEA_SS * train.type * info.type, random = ~1|Subj, data = acc_beh, method = "ML")
summary(m1)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_beh
## AIC BIC logLik
## 560.0551 598.6343 -268.0275
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.9318191 0.8291919
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + KTEA_SS * train.type * info.type
## Value Std.Error DF
## (Intercept) -5.903591 2.3850928 132
## P_IQ 0.006200 0.0179290 42
## WA_SS 0.040330 0.0233614 42
## KTEA_SS 0.026714 0.0186607 42
## train.typeUndirected 1.526079 1.1921267 132
## info.typeVerbal 0.350692 1.1921267 132
## KTEA_SS:train.typeUndirected -0.025074 0.0120125 132
## KTEA_SS:info.typeVerbal 0.001202 0.0120125 132
## train.typeUndirected:info.typeVerbal -1.844699 1.6859218 132
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.020292 0.0169883 132
## t-value p-value
## (Intercept) -2.4752040 0.0146
## P_IQ 0.3458039 0.7312
## WA_SS 1.7263365 0.0916
## KTEA_SS 1.4315555 0.1597
## train.typeUndirected 1.2801312 0.2027
## info.typeVerbal 0.2941733 0.7691
## KTEA_SS:train.typeUndirected -2.0873389 0.0388
## KTEA_SS:info.typeVerbal 0.1000733 0.9204
## train.typeUndirected:info.typeVerbal -1.0941781 0.2759
## KTEA_SS:train.typeUndirected:info.typeVerbal 1.1944877 0.2344
## Correlation:
## (Intr) P_IQ WA_SS KTEA_SS
## P_IQ -0.247
## WA_SS -0.805 -0.022
## KTEA_SS 0.029 -0.675 -0.260
## train.typeUndirected -0.250 0.000 0.000 0.318
## info.typeVerbal -0.250 0.000 0.000 0.318
## KTEA_SS:train.typeUndirected 0.247 0.000 0.000 -0.322
## KTEA_SS:info.typeVerbal 0.247 0.000 0.000 -0.322
## train.typeUndirected:info.typeVerbal 0.177 0.000 0.000 -0.225
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.175 0.000 0.000 0.228
## trn.tU inf.tV KTEA_SS:t.U
## P_IQ
## WA_SS
## KTEA_SS
## train.typeUndirected
## info.typeVerbal 0.500
## KTEA_SS:train.typeUndirected -0.989 -0.494
## KTEA_SS:info.typeVerbal -0.494 -0.989 0.500
## train.typeUndirected:info.typeVerbal -0.707 -0.707 0.699
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.699 0.699 -0.707
## KTEA_SS:.V t.U:.V
## P_IQ
## WA_SS
## KTEA_SS
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:train.typeUndirected
## KTEA_SS:info.typeVerbal
## train.typeUndirected:info.typeVerbal 0.699
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.707 -0.989
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.97220783 -0.68441712 -0.01637841 0.59661085 2.15859085
##
## Number of Observations: 184
## Number of Groups: 46
anova(m0.5,m1)## Model df AIC BIC logLik Test L.Ratio p-value
## m0.5 1 5 608.1801 624.2548 -299.0900
## m1 2 12 560.0551 598.6343 -268.0275 1 vs 2 62.125 <.0001
m2 <- lme(acc.logit ~ P_IQ + WA_SS + KTEA_SS * Order * train.type * info.type, random = ~1|Subj, data = acc_beh, method = "ML")
summary(m2)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_beh
## AIC BIC logLik
## 540.2542 604.5529 -250.1271
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.9022837 0.7393111
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + KTEA_SS * Order * train.type * info.type
## Value
## (Intercept) -4.304064
## P_IQ 0.008264
## WA_SS 0.040175
## KTEA_SS 0.008344
## OrderUndirected First -3.536988
## train.typeUndirected -0.475705
## info.typeVerbal -0.640438
## KTEA_SS:OrderUndirected First 0.035674
## KTEA_SS:train.typeUndirected 0.000891
## OrderUndirected First:train.typeUndirected 3.612329
## KTEA_SS:info.typeVerbal 0.011025
## OrderUndirected First:info.typeVerbal 2.147590
## train.typeUndirected:info.typeVerbal -0.270950
## KTEA_SS:OrderUndirected First:train.typeUndirected -0.048411
## KTEA_SS:OrderUndirected First:info.typeVerbal -0.021021
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.004537
## OrderUndirected First:train.typeUndirected:info.typeVerbal -3.391421
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.033531
## Std.Error
## (Intercept) 2.7023992
## P_IQ 0.0177425
## WA_SS 0.0232701
## KTEA_SS 0.0221024
## OrderUndirected First 2.4853564
## train.typeUndirected 1.4930924
## info.typeVerbal 1.4930924
## KTEA_SS:OrderUndirected First 0.0250235
## KTEA_SS:train.typeUndirected 0.0152056
## OrderUndirected First:train.typeUndirected 2.1881319
## KTEA_SS:info.typeVerbal 0.0152056
## OrderUndirected First:info.typeVerbal 2.1881319
## train.typeUndirected:info.typeVerbal 2.1115515
## KTEA_SS:OrderUndirected First:train.typeUndirected 0.0220152
## KTEA_SS:OrderUndirected First:info.typeVerbal 0.0220152
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.0215040
## OrderUndirected First:train.typeUndirected:info.typeVerbal 3.0944858
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.0311342
## DF
## (Intercept) 126
## P_IQ 40
## WA_SS 40
## KTEA_SS 40
## OrderUndirected First 40
## train.typeUndirected 126
## info.typeVerbal 126
## KTEA_SS:OrderUndirected First 40
## KTEA_SS:train.typeUndirected 126
## OrderUndirected First:train.typeUndirected 126
## KTEA_SS:info.typeVerbal 126
## OrderUndirected First:info.typeVerbal 126
## train.typeUndirected:info.typeVerbal 126
## KTEA_SS:OrderUndirected First:train.typeUndirected 126
## KTEA_SS:OrderUndirected First:info.typeVerbal 126
## KTEA_SS:train.typeUndirected:info.typeVerbal 126
## OrderUndirected First:train.typeUndirected:info.typeVerbal 126
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 126
## t-value
## (Intercept) -1.5926825
## P_IQ 0.4657534
## WA_SS 1.7264873
## KTEA_SS 0.3775193
## OrderUndirected First -1.4231310
## train.typeUndirected -0.3186039
## info.typeVerbal -0.4289340
## KTEA_SS:OrderUndirected First 1.4256122
## KTEA_SS:train.typeUndirected 0.0586091
## OrderUndirected First:train.typeUndirected 1.6508735
## KTEA_SS:info.typeVerbal 0.7250916
## OrderUndirected First:info.typeVerbal 0.9814720
## train.typeUndirected:info.typeVerbal -0.1283180
## KTEA_SS:OrderUndirected First:train.typeUndirected -2.1989635
## KTEA_SS:OrderUndirected First:info.typeVerbal -0.9548449
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.2110014
## OrderUndirected First:train.typeUndirected:info.typeVerbal -1.0959563
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 1.0769722
## p-value
## (Intercept) 0.1137
## P_IQ 0.6439
## WA_SS 0.0920
## KTEA_SS 0.7078
## OrderUndirected First 0.1625
## train.typeUndirected 0.7506
## info.typeVerbal 0.6687
## KTEA_SS:OrderUndirected First 0.1617
## KTEA_SS:train.typeUndirected 0.9534
## OrderUndirected First:train.typeUndirected 0.1013
## KTEA_SS:info.typeVerbal 0.4697
## OrderUndirected First:info.typeVerbal 0.3282
## train.typeUndirected:info.typeVerbal 0.8981
## KTEA_SS:OrderUndirected First:train.typeUndirected 0.0297
## KTEA_SS:OrderUndirected First:info.typeVerbal 0.3415
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.8332
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.2752
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.2836
## Correlation:
## (Intr)
## P_IQ -0.145
## WA_SS -0.766
## KTEA_SS -0.262
## OrderUndirected First -0.504
## train.typeUndirected -0.276
## info.typeVerbal -0.276
## KTEA_SS:OrderUndirected First 0.510
## KTEA_SS:train.typeUndirected 0.273
## OrderUndirected First:train.typeUndirected 0.189
## KTEA_SS:info.typeVerbal 0.273
## OrderUndirected First:info.typeVerbal 0.189
## train.typeUndirected:info.typeVerbal 0.195
## KTEA_SS:OrderUndirected First:train.typeUndirected -0.189
## KTEA_SS:OrderUndirected First:info.typeVerbal -0.189
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.193
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.133
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.133
## P_IQ
## P_IQ
## WA_SS -0.047
## KTEA_SS -0.623
## OrderUndirected First -0.124
## train.typeUndirected 0.000
## info.typeVerbal 0.000
## KTEA_SS:OrderUndirected First 0.122
## KTEA_SS:train.typeUndirected 0.000
## OrderUndirected First:train.typeUndirected 0.000
## KTEA_SS:info.typeVerbal 0.000
## OrderUndirected First:info.typeVerbal 0.000
## train.typeUndirected:info.typeVerbal 0.000
## KTEA_SS:OrderUndirected First:train.typeUndirected 0.000
## KTEA_SS:OrderUndirected First:info.typeVerbal 0.000
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.000
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.000
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.000
## WA_SS
## P_IQ
## WA_SS
## KTEA_SS -0.120
## OrderUndirected First 0.146
## train.typeUndirected 0.000
## info.typeVerbal 0.000
## KTEA_SS:OrderUndirected First -0.153
## KTEA_SS:train.typeUndirected 0.000
## OrderUndirected First:train.typeUndirected 0.000
## KTEA_SS:info.typeVerbal 0.000
## OrderUndirected First:info.typeVerbal 0.000
## train.typeUndirected:info.typeVerbal 0.000
## KTEA_SS:OrderUndirected First:train.typeUndirected 0.000
## KTEA_SS:OrderUndirected First:info.typeVerbal 0.000
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.000
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.000
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.000
## KTEA_SS
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First 0.565
## train.typeUndirected 0.340
## info.typeVerbal 0.340
## KTEA_SS:OrderUndirected First -0.574
## KTEA_SS:train.typeUndirected -0.344
## OrderUndirected First:train.typeUndirected -0.232
## KTEA_SS:info.typeVerbal -0.344
## OrderUndirected First:info.typeVerbal -0.232
## train.typeUndirected:info.typeVerbal -0.241
## KTEA_SS:OrderUndirected First:train.typeUndirected 0.238
## KTEA_SS:OrderUndirected First:info.typeVerbal 0.238
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.243
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.164
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal -0.168
## OrdrUF
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected 0.300
## info.typeVerbal 0.300
## KTEA_SS:OrderUndirected First -0.989
## KTEA_SS:train.typeUndirected -0.297
## OrderUndirected First:train.typeUndirected -0.440
## KTEA_SS:info.typeVerbal -0.297
## OrderUndirected First:info.typeVerbal -0.440
## train.typeUndirected:info.typeVerbal -0.212
## KTEA_SS:OrderUndirected First:train.typeUndirected 0.435
## KTEA_SS:OrderUndirected First:info.typeVerbal 0.435
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.210
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.311
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal -0.308
## trn.tU
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal 0.500
## KTEA_SS:OrderUndirected First -0.301
## KTEA_SS:train.typeUndirected -0.989
## OrderUndirected First:train.typeUndirected -0.682
## KTEA_SS:info.typeVerbal -0.495
## OrderUndirected First:info.typeVerbal -0.341
## train.typeUndirected:info.typeVerbal -0.707
## KTEA_SS:OrderUndirected First:train.typeUndirected 0.683
## KTEA_SS:OrderUndirected First:info.typeVerbal 0.342
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.699
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.483
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal -0.483
## inf.tV
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:OrderUndirected First -0.301
## KTEA_SS:train.typeUndirected -0.495
## OrderUndirected First:train.typeUndirected -0.341
## KTEA_SS:info.typeVerbal -0.989
## OrderUndirected First:info.typeVerbal -0.682
## train.typeUndirected:info.typeVerbal -0.707
## KTEA_SS:OrderUndirected First:train.typeUndirected 0.342
## KTEA_SS:OrderUndirected First:info.typeVerbal 0.683
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.699
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.483
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal -0.483
## KTEA_SS:OrUF
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:OrderUndirected First
## KTEA_SS:train.typeUndirected 0.304
## OrderUndirected First:train.typeUndirected 0.435
## KTEA_SS:info.typeVerbal 0.304
## OrderUndirected First:info.typeVerbal 0.435
## train.typeUndirected:info.typeVerbal 0.212
## KTEA_SS:OrderUndirected First:train.typeUndirected -0.440
## KTEA_SS:OrderUndirected First:info.typeVerbal -0.440
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.215
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.308
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.311
## KTEA_SS:t.U
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:OrderUndirected First
## KTEA_SS:train.typeUndirected
## OrderUndirected First:train.typeUndirected 0.675
## KTEA_SS:info.typeVerbal 0.500
## OrderUndirected First:info.typeVerbal 0.337
## train.typeUndirected:info.typeVerbal 0.699
## KTEA_SS:OrderUndirected First:train.typeUndirected -0.691
## KTEA_SS:OrderUndirected First:info.typeVerbal -0.345
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.707
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.477
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.488
## OrUF:.U
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:OrderUndirected First
## KTEA_SS:train.typeUndirected
## OrderUndirected First:train.typeUndirected
## KTEA_SS:info.typeVerbal 0.337
## OrderUndirected First:info.typeVerbal 0.500
## train.typeUndirected:info.typeVerbal 0.483
## KTEA_SS:OrderUndirected First:train.typeUndirected -0.989
## KTEA_SS:OrderUndirected First:info.typeVerbal -0.494
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.477
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.707
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.699
## KTEA_SS:.V
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:OrderUndirected First
## KTEA_SS:train.typeUndirected
## OrderUndirected First:train.typeUndirected
## KTEA_SS:info.typeVerbal
## OrderUndirected First:info.typeVerbal 0.675
## train.typeUndirected:info.typeVerbal 0.699
## KTEA_SS:OrderUndirected First:train.typeUndirected -0.345
## KTEA_SS:OrderUndirected First:info.typeVerbal -0.691
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.707
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.477
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.488
## OUF:.V
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:OrderUndirected First
## KTEA_SS:train.typeUndirected
## OrderUndirected First:train.typeUndirected
## KTEA_SS:info.typeVerbal
## OrderUndirected First:info.typeVerbal
## train.typeUndirected:info.typeVerbal 0.483
## KTEA_SS:OrderUndirected First:train.typeUndirected -0.494
## KTEA_SS:OrderUndirected First:info.typeVerbal -0.989
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.477
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.707
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.699
## t.U:.V
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:OrderUndirected First
## KTEA_SS:train.typeUndirected
## OrderUndirected First:train.typeUndirected
## KTEA_SS:info.typeVerbal
## OrderUndirected First:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## KTEA_SS:OrderUndirected First:train.typeUndirected -0.483
## KTEA_SS:OrderUndirected First:info.typeVerbal -0.483
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.989
## OrderUndirected First:train.typeUndirected:info.typeVerbal -0.682
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal 0.683
## KTEA_SS:OrUF:.U
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:OrderUndirected First
## KTEA_SS:train.typeUndirected
## OrderUndirected First:train.typeUndirected
## KTEA_SS:info.typeVerbal
## OrderUndirected First:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## KTEA_SS:OrderUndirected First:train.typeUndirected
## KTEA_SS:OrderUndirected First:info.typeVerbal 0.500
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.488
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.699
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal -0.707
## KTEA_SS:OUF:.V
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:OrderUndirected First
## KTEA_SS:train.typeUndirected
## OrderUndirected First:train.typeUndirected
## KTEA_SS:info.typeVerbal
## OrderUndirected First:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## KTEA_SS:OrderUndirected First:train.typeUndirected
## KTEA_SS:OrderUndirected First:info.typeVerbal
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.488
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.699
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal -0.707
## KTEA_SS:.U:
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:OrderUndirected First
## KTEA_SS:train.typeUndirected
## OrderUndirected First:train.typeUndirected
## KTEA_SS:info.typeVerbal
## OrderUndirected First:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## KTEA_SS:OrderUndirected First:train.typeUndirected
## KTEA_SS:OrderUndirected First:info.typeVerbal
## KTEA_SS:train.typeUndirected:info.typeVerbal
## OrderUndirected First:train.typeUndirected:info.typeVerbal 0.675
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal -0.691
## OUF:.U:
## P_IQ
## WA_SS
## KTEA_SS
## OrderUndirected First
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:OrderUndirected First
## KTEA_SS:train.typeUndirected
## OrderUndirected First:train.typeUndirected
## KTEA_SS:info.typeVerbal
## OrderUndirected First:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## KTEA_SS:OrderUndirected First:train.typeUndirected
## KTEA_SS:OrderUndirected First:info.typeVerbal
## KTEA_SS:train.typeUndirected:info.typeVerbal
## OrderUndirected First:train.typeUndirected:info.typeVerbal
## KTEA_SS:OrderUndirected First:train.typeUndirected:info.typeVerbal -0.989
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.572055018 -0.587995258 0.002399288 0.576033346 2.364343291
##
## Number of Observations: 184
## Number of Groups: 46
anova(m1,m2)## Model df AIC BIC logLik Test L.Ratio p-value
## m1 1 12 560.0551 598.6343 -268.0275
## m2 2 20 540.2542 604.5529 -250.1271 1 vs 2 35.80089 <.0001
dirfSubset <- acc_beh$Order=="Directed First"
undirfSubset <- acc_beh$Order=="Undirected First"
dirfModel <- lme(acc.logit ~ P_IQ + WA_SS + KTEA_SS * train.type * info.type, random = ~1|Subj, data = acc_beh, subset = dirfSubset, method = "ML")
summary(dirfModel)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_beh
## Subset: dirfSubset
## AIC BIC logLik
## 287.7657 319.0277 -131.8828
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 1.003017 0.6813165
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + KTEA_SS * train.type * info.type
## Value Std.Error DF
## (Intercept) -11.266044 4.540860 69
## P_IQ 0.041129 0.030495 21
## WA_SS 0.107484 0.041826 21
## KTEA_SS -0.027036 0.032311 21
## train.typeUndirected -0.475705 1.377629 69
## info.typeVerbal -0.640438 1.377629 69
## KTEA_SS:train.typeUndirected 0.000891 0.014030 69
## KTEA_SS:info.typeVerbal 0.011025 0.014030 69
## train.typeUndirected:info.typeVerbal -0.270950 1.948262 69
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.004537 0.019841 69
## t-value p-value
## (Intercept) -2.4810377 0.0155
## P_IQ 1.3487061 0.1918
## WA_SS 2.5697950 0.0179
## KTEA_SS -0.8367382 0.4122
## train.typeUndirected -0.3453071 0.7309
## info.typeVerbal -0.4648844 0.6435
## KTEA_SS:train.typeUndirected 0.0635213 0.9495
## KTEA_SS:info.typeVerbal 0.7858638 0.4346
## train.typeUndirected:info.typeVerbal -0.1390727 0.8898
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.2286861 0.8198
## Correlation:
## (Intr) P_IQ WA_SS KTEA_SS
## P_IQ -0.545
## WA_SS -0.909 0.434
## KTEA_SS 0.365 -0.821 -0.506
## train.typeUndirected -0.152 0.000 0.000 0.215
## info.typeVerbal -0.152 0.000 0.000 0.215
## KTEA_SS:train.typeUndirected 0.150 0.000 0.000 -0.217
## KTEA_SS:info.typeVerbal 0.150 0.000 0.000 -0.217
## train.typeUndirected:info.typeVerbal 0.107 0.000 0.000 -0.152
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.106 0.000 0.000 0.154
## trn.tU inf.tV KTEA_SS:t.U
## P_IQ
## WA_SS
## KTEA_SS
## train.typeUndirected
## info.typeVerbal 0.500
## KTEA_SS:train.typeUndirected -0.989 -0.495
## KTEA_SS:info.typeVerbal -0.495 -0.989 0.500
## train.typeUndirected:info.typeVerbal -0.707 -0.707 0.699
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.699 0.699 -0.707
## KTEA_SS:.V t.U:.V
## P_IQ
## WA_SS
## KTEA_SS
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:train.typeUndirected
## KTEA_SS:info.typeVerbal
## train.typeUndirected:info.typeVerbal 0.699
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.707 -0.989
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.07796527 -0.58288654 -0.08523717 0.56883748 2.51809879
##
## Number of Observations: 100
## Number of Groups: 25
undirfModel <- lme(acc.logit ~ P_IQ + WA_SS + KTEA_SS * train.type * info.type, random = ~1|Subj, data = acc_beh, subset = undirfSubset, method = "ML")
summary(undirfModel)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_beh
## Subset: undirfSubset
## AIC BIC logLik
## 249.4128 278.5826 -112.7064
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.5848364 0.8029079
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + KTEA_SS * train.type * info.type
## Value Std.Error DF
## (Intercept) -4.908606 2.3140986 57
## P_IQ 0.012778 0.0208033 17
## WA_SS -0.001613 0.0262183 17
## KTEA_SS 0.053779 0.0191681 17
## train.typeUndirected 3.136624 1.7579572 57
## info.typeVerbal 1.507152 1.7579572 57
## KTEA_SS:train.typeUndirected -0.047519 0.0174969 57
## KTEA_SS:info.typeVerbal -0.009996 0.0174969 57
## train.typeUndirected:info.typeVerbal -3.662371 2.4861270 57
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.038068 0.0247443 57
## t-value p-value
## (Intercept) -2.1211741 0.0383
## P_IQ 0.6142184 0.5472
## WA_SS -0.0615366 0.9516
## KTEA_SS 2.8056327 0.0122
## train.typeUndirected 1.7842436 0.0797
## info.typeVerbal 0.8573314 0.3949
## KTEA_SS:train.typeUndirected -2.7158838 0.0087
## KTEA_SS:info.typeVerbal -0.5712809 0.5701
## train.typeUndirected:info.typeVerbal -1.4731231 0.1462
## KTEA_SS:train.typeUndirected:info.typeVerbal 1.5384574 0.1295
## Correlation:
## (Intr) P_IQ WA_SS KTEA_SS
## P_IQ 0.007
## WA_SS -0.661 -0.475
## KTEA_SS -0.258 -0.481 -0.091
## train.typeUndirected -0.380 0.000 0.000 0.451
## info.typeVerbal -0.380 0.000 0.000 0.451
## KTEA_SS:train.typeUndirected 0.376 0.000 0.000 -0.456
## KTEA_SS:info.typeVerbal 0.376 0.000 0.000 -0.456
## train.typeUndirected:info.typeVerbal 0.269 0.000 0.000 -0.319
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.266 0.000 0.000 0.323
## trn.tU inf.tV KTEA_SS:t.U
## P_IQ
## WA_SS
## KTEA_SS
## train.typeUndirected
## info.typeVerbal 0.500
## KTEA_SS:train.typeUndirected -0.989 -0.494
## KTEA_SS:info.typeVerbal -0.494 -0.989 0.500
## train.typeUndirected:info.typeVerbal -0.707 -0.707 0.699
## KTEA_SS:train.typeUndirected:info.typeVerbal 0.699 0.699 -0.707
## KTEA_SS:.V t.U:.V
## P_IQ
## WA_SS
## KTEA_SS
## train.typeUndirected
## info.typeVerbal
## KTEA_SS:train.typeUndirected
## KTEA_SS:info.typeVerbal
## train.typeUndirected:info.typeVerbal 0.699
## KTEA_SS:train.typeUndirected:info.typeVerbal -0.707 -0.989
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -2.59750767 -0.54796581 0.07366354 0.58448211 1.99654561
##
## Number of Observations: 84
## Number of Groups: 21
p2 <- ggplot(acc_beh, aes(KTEA_SS, Acc, color = train.type)) + geom_point() +
xlab("Reading Comprehension") + ylab("Accuracy") +
facet_grid(.~Order) + theme_bw() + geom_smooth(method = "lm", se = FALSE)
p2
reliability <- dat3[,c(1,2,3,7)]
rel <- cast(reliability, Subj ~ condition, sum, value = "Acc")
alpha(rel[,-1])
rel_ENV <- cast(subset(reliability, condition == "Explicit_Nonverbal"), Subj ~ condition + Trial, sum, value = "Acc")
rel_EV <- cast(subset(reliability, condition == "Explicit_Verbal"), Subj ~ condition + Trial, sum, value = "Acc")
rel_INV <- cast(subset(reliability, condition == "Implicit_Nonverbal"), Subj ~ condition + Trial, sum, value = "Acc")
rel_IV <- cast(subset(reliability, condition == "Implicit_Verbal"), Subj ~ condition + Trial, sum, value = "Acc")
alpha(rel_ENV[,-1])
alpha(rel_EV[,-1])
alpha(rel_IV[,-1])
alpha(rel_INV[,-1])ENV <- subset(acc_group, Cond == "Explicit_Nonverbal")
EV <- subset(acc_group, Cond == "Explicit_Verbal")
INV <- subset(acc_group, Cond == "Implicit_Nonverbal")
IV <- subset(acc_group, Cond == "Implicit_Verbal")
dagoTest(ENV$Acc)##
## Title:
## D'Agostino Normality Test
##
## Test Results:
## STATISTIC:
## Chi2 | Omnibus: 8.2245
## Z3 | Skewness: -0.9173
## Z4 | Kurtosis: -2.7172
## P VALUE:
## Omnibus Test: 0.01637
## Skewness Test: 0.359
## Kurtosis Test: 0.006584
##
## Description:
## Wed Nov 14 15:40:53 2018 by user:
dagoTest(EV$Acc)##
## Title:
## D'Agostino Normality Test
##
## Test Results:
## STATISTIC:
## Chi2 | Omnibus: 5.9944
## Z3 | Skewness: -2.3873
## Z4 | Kurtosis: -0.5434
## P VALUE:
## Omnibus Test: 0.04993
## Skewness Test: 0.01697
## Kurtosis Test: 0.5868
##
## Description:
## Wed Nov 14 15:40:53 2018 by user:
dagoTest(INV$Acc)##
## Title:
## D'Agostino Normality Test
##
## Test Results:
## STATISTIC:
## Chi2 | Omnibus: 2.8584
## Z3 | Skewness: 1.4136
## Z4 | Kurtosis: -0.9274
## P VALUE:
## Omnibus Test: 0.2395
## Skewness Test: 0.1575
## Kurtosis Test: 0.3537
##
## Description:
## Wed Nov 14 15:40:53 2018 by user:
dagoTest(IV$Acc)##
## Title:
## D'Agostino Normality Test
##
## Test Results:
## STATISTIC:
## Chi2 | Omnibus: 10.8364
## Z3 | Skewness: -0.5021
## Z4 | Kurtosis: -3.2534
## P VALUE:
## Omnibus Test: 0.004435
## Skewness Test: 0.6156
## Kurtosis Test: 0.001141
##
## Description:
## Wed Nov 14 15:40:53 2018 by user:
EV_high <- subset(EV, Acc > 0.95)
table(EV_high$Group)##
## TD PC
## 12 4
ggplot(acc_beh, aes(V_IQ, Acc, shape = Group, color = Group)) + geom_point() + theme_bw() +
#facet_grid(info.type ~ train.type) +
geom_smooth(method = "lm", se = FALSE) + xlab("Verbal IQ") + ylab("Accuracy") + guides(color=guide_legend(title="Group"), shape=guide_legend(title="Group"))
viq <- lme(acc.logit ~ P_IQ + WA_SS + V_IQ + Group * train.type * info.type, random = ~1|Subj, data = acc_beh, method = "ML", na.action = na.omit)
summary(viq)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_beh
## AIC BIC logLik
## 551.74 593.2484 -262.87
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.9428383 0.8305138
##
## Fixed effects: acc.logit ~ P_IQ + WA_SS + V_IQ + Group * train.type * info.type
## Value Std.Error DF
## (Intercept) -4.428535 2.8374278 129
## P_IQ 0.011404 0.0155935 40
## WA_SS 0.039802 0.0240475 40
## V_IQ 0.008341 0.0137293 40
## GroupPC -0.366332 0.4747540 40
## train.typeUndirected -1.299383 0.2527498 129
## info.typeVerbal 0.549816 0.2527498 129
## GroupPC:train.typeUndirected 0.700671 0.3614812 129
## GroupPC:info.typeVerbal -0.149611 0.3614812 129
## train.typeUndirected:info.typeVerbal 0.447540 0.3574422 129
## GroupPC:train.typeUndirected:info.typeVerbal -0.616886 0.5112116 129
## t-value p-value
## (Intercept) -1.560757 0.1210
## P_IQ 0.731324 0.4688
## WA_SS 1.655152 0.1057
## V_IQ 0.607517 0.5469
## GroupPC -0.771624 0.4449
## train.typeUndirected -5.140986 0.0000
## info.typeVerbal 2.175335 0.0314
## GroupPC:train.typeUndirected 1.938333 0.0548
## GroupPC:info.typeVerbal -0.413884 0.6796
## train.typeUndirected:info.typeVerbal 1.252064 0.2128
## GroupPC:train.typeUndirected:info.typeVerbal -1.206714 0.2298
## Correlation:
## (Intr) P_IQ WA_SS V_IQ
## P_IQ -0.434
## WA_SS -0.688 -0.114
## V_IQ -0.184 -0.159 -0.274
## GroupPC -0.520 0.447 0.081 0.216
## train.typeUndirected -0.045 0.000 0.000 0.000
## info.typeVerbal -0.045 0.000 0.000 0.000
## GroupPC:train.typeUndirected 0.031 0.000 0.000 0.000
## GroupPC:info.typeVerbal 0.031 0.000 0.000 0.000
## train.typeUndirected:info.typeVerbal 0.031 0.000 0.000 0.000
## GroupPC:train.typeUndirected:info.typeVerbal -0.022 0.000 0.000 0.000
## GropPC trn.tU inf.tV GrPC:.U
## P_IQ
## WA_SS
## V_IQ
## GroupPC
## train.typeUndirected 0.266
## info.typeVerbal 0.266 0.500
## GroupPC:train.typeUndirected -0.381 -0.699 -0.350
## GroupPC:info.typeVerbal -0.381 -0.350 -0.699 0.500
## train.typeUndirected:info.typeVerbal -0.188 -0.707 -0.707 0.494
## GroupPC:train.typeUndirected:info.typeVerbal 0.269 0.494 0.494 -0.707
## GPC:.V t.U:.V
## P_IQ
## WA_SS
## V_IQ
## GroupPC
## train.typeUndirected
## info.typeVerbal
## GroupPC:train.typeUndirected
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal 0.494
## GroupPC:train.typeUndirected:info.typeVerbal -0.707 -0.699
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.89725093 -0.68952710 0.02410329 0.58026041 2.20762460
##
## Number of Observations: 180
## Number of Groups: 45
viq <- lme(acc.logit ~ V_IQ * Group * train.type * info.type, random = ~1|Subj, data = acc_beh, method = "ML", na.action = na.omit)
summary(viq)## Linear mixed-effects model fit by maximum likelihood
## Data: acc_beh
## AIC BIC logLik
## 558.7452 616.2184 -261.3726
##
## Random effects:
## Formula: ~1 | Subj
## (Intercept) Residual
## StdDev: 0.9610289 0.8176305
##
## Fixed effects: acc.logit ~ V_IQ * Group * train.type * info.type
## Value Std.Error DF
## (Intercept) -3.529313 2.483511 123
## V_IQ 0.050592 0.022465 41
## GroupPC 5.233089 3.355814 41
## train.typeUndirected 2.321703 2.275902 123
## info.typeVerbal 1.938076 2.275902 123
## V_IQ:GroupPC -0.055964 0.032180 41
## V_IQ:train.typeUndirected -0.032958 0.020587 123
## GroupPC:train.typeUndirected -1.867256 3.075284 123
## V_IQ:info.typeVerbal -0.012636 0.020587 123
## GroupPC:info.typeVerbal -2.578602 3.075284 123
## train.typeUndirected:info.typeVerbal -3.455747 3.218611 123
## V_IQ:GroupPC:train.typeUndirected 0.022121 0.029490 123
## V_IQ:GroupPC:info.typeVerbal 0.023345 0.029490 123
## V_IQ:train.typeUndirected:info.typeVerbal 0.035527 0.029114 123
## GroupPC:train.typeUndirected:info.typeVerbal 2.602008 4.349109 123
## V_IQ:GroupPC:train.typeUndirected:info.typeVerbal -0.028484 0.041706 123
## t-value p-value
## (Intercept) -1.4210981 0.1578
## V_IQ 2.2520686 0.0297
## GroupPC 1.5594099 0.1266
## train.typeUndirected 1.0201245 0.3097
## info.typeVerbal 0.8515640 0.3961
## V_IQ:GroupPC -1.7390585 0.0895
## V_IQ:train.typeUndirected -1.6009464 0.1120
## GroupPC:train.typeUndirected -0.6071816 0.5448
## V_IQ:info.typeVerbal -0.6137745 0.5405
## GroupPC:info.typeVerbal -0.8384924 0.4034
## train.typeUndirected:info.typeVerbal -1.0736763 0.2851
## V_IQ:GroupPC:train.typeUndirected 0.7501121 0.4546
## V_IQ:GroupPC:info.typeVerbal 0.7916034 0.4301
## V_IQ:train.typeUndirected:info.typeVerbal 1.2202628 0.2247
## GroupPC:train.typeUndirected:info.typeVerbal 0.5982853 0.5507
## V_IQ:GroupPC:train.typeUndirected:info.typeVerbal -0.6829813 0.4959
## Correlation:
## (Intr) V_IQ GropPC
## V_IQ -0.994
## GroupPC -0.740 0.735
## train.typeUndirected -0.458 0.455 0.339
## info.typeVerbal -0.458 0.455 0.339
## V_IQ:GroupPC 0.694 -0.698 -0.991
## V_IQ:train.typeUndirected 0.455 -0.458 -0.337
## GroupPC:train.typeUndirected 0.339 -0.337 -0.458
## V_IQ:info.typeVerbal 0.455 -0.458 -0.337
## GroupPC:info.typeVerbal 0.339 -0.337 -0.458
## train.typeUndirected:info.typeVerbal 0.324 -0.322 -0.240
## V_IQ:GroupPC:train.typeUndirected -0.318 0.320 0.454
## V_IQ:GroupPC:info.typeVerbal -0.318 0.320 0.454
## V_IQ:train.typeUndirected:info.typeVerbal -0.322 0.324 0.238
## GroupPC:train.typeUndirected:info.typeVerbal -0.240 0.238 0.324
## V_IQ:GroupPC:train.typeUndirected:info.typeVerbal 0.225 -0.226 -0.321
## trn.tU inf.tV V_IQ:GrPC
## V_IQ
## GroupPC
## train.typeUndirected
## info.typeVerbal 0.500
## V_IQ:GroupPC -0.318 -0.318
## V_IQ:train.typeUndirected -0.994 -0.497 0.320
## GroupPC:train.typeUndirected -0.740 -0.370 0.454
## V_IQ:info.typeVerbal -0.497 -0.994 0.320
## GroupPC:info.typeVerbal -0.370 -0.740 0.454
## train.typeUndirected:info.typeVerbal -0.707 -0.707 0.225
## V_IQ:GroupPC:train.typeUndirected 0.694 0.347 -0.458
## V_IQ:GroupPC:info.typeVerbal 0.347 0.694 -0.458
## V_IQ:train.typeUndirected:info.typeVerbal 0.703 0.703 -0.226
## GroupPC:train.typeUndirected:info.typeVerbal 0.523 0.523 -0.321
## V_IQ:GroupPC:train.typeUndirected:info.typeVerbal -0.491 -0.491 0.324
## V_IQ:t.U GrPC:.U V_IQ:.V
## V_IQ
## GroupPC
## train.typeUndirected
## info.typeVerbal
## V_IQ:GroupPC
## V_IQ:train.typeUndirected
## GroupPC:train.typeUndirected 0.735
## V_IQ:info.typeVerbal 0.500 0.368
## GroupPC:info.typeVerbal 0.368 0.500 0.735
## train.typeUndirected:info.typeVerbal 0.703 0.523 0.703
## V_IQ:GroupPC:train.typeUndirected -0.698 -0.991 -0.349
## V_IQ:GroupPC:info.typeVerbal -0.349 -0.496 -0.698
## V_IQ:train.typeUndirected:info.typeVerbal -0.707 -0.520 -0.707
## GroupPC:train.typeUndirected:info.typeVerbal -0.520 -0.707 -0.520
## V_IQ:GroupPC:train.typeUndirected:info.typeVerbal 0.494 0.701 0.494
## GPC:.V t.U:.V
## V_IQ
## GroupPC
## train.typeUndirected
## info.typeVerbal
## V_IQ:GroupPC
## V_IQ:train.typeUndirected
## GroupPC:train.typeUndirected
## V_IQ:info.typeVerbal
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal 0.523
## V_IQ:GroupPC:train.typeUndirected -0.496 -0.491
## V_IQ:GroupPC:info.typeVerbal -0.991 -0.491
## V_IQ:train.typeUndirected:info.typeVerbal -0.520 -0.994
## GroupPC:train.typeUndirected:info.typeVerbal -0.707 -0.740
## V_IQ:GroupPC:train.typeUndirected:info.typeVerbal 0.701 0.694
## V_IQ:GrPC:.U V_IQ:GPC:.V
## V_IQ
## GroupPC
## train.typeUndirected
## info.typeVerbal
## V_IQ:GroupPC
## V_IQ:train.typeUndirected
## GroupPC:train.typeUndirected
## V_IQ:info.typeVerbal
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## V_IQ:GroupPC:train.typeUndirected
## V_IQ:GroupPC:info.typeVerbal 0.500
## V_IQ:train.typeUndirected:info.typeVerbal 0.494 0.494
## GroupPC:train.typeUndirected:info.typeVerbal 0.701 0.701
## V_IQ:GroupPC:train.typeUndirected:info.typeVerbal -0.707 -0.707
## V_IQ:.U: GPC:.U:
## V_IQ
## GroupPC
## train.typeUndirected
## info.typeVerbal
## V_IQ:GroupPC
## V_IQ:train.typeUndirected
## GroupPC:train.typeUndirected
## V_IQ:info.typeVerbal
## GroupPC:info.typeVerbal
## train.typeUndirected:info.typeVerbal
## V_IQ:GroupPC:train.typeUndirected
## V_IQ:GroupPC:info.typeVerbal
## V_IQ:train.typeUndirected:info.typeVerbal
## GroupPC:train.typeUndirected:info.typeVerbal 0.735
## V_IQ:GroupPC:train.typeUndirected:info.typeVerbal -0.698 -0.991
##
## Standardized Within-Group Residuals:
## Min Q1 Med Q3 Max
## -1.96281876 -0.71710377 -0.05812295 0.58863346 2.20809813
##
## Number of Observations: 180
## Number of Groups: 45
PC <- subset(acc_beh, Group == "PC")
TD <- subset(acc_beh, Group == "TD")
cor.test(acc_beh$Acc, acc_beh$V_IQ)##
## Pearson's product-moment correlation
##
## data: acc_beh$Acc and acc_beh$V_IQ
## t = 2.6228, df = 178, p-value = 0.009477
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.04798702 0.32985339
## sample estimates:
## cor
## 0.1928964
cor.test(PC$Acc, PC$V_IQ)##
## Pearson's product-moment correlation
##
## data: PC$Acc and PC$V_IQ
## t = -0.70206, df = 86, p-value = 0.4845
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2804967 0.1361049
## sample estimates:
## cor
## -0.0754893
cor.test(TD$Acc, TD$V_IQ)##
## Pearson's product-moment correlation
##
## data: TD$Acc and TD$V_IQ
## t = 2.5546, df = 90, p-value = 0.01231
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05830321 0.44132971
## sample estimates:
## cor
## 0.2600156