#Load packages
library(nlme)
#install.packages("MuMIn")
library(MuMIn)
#install.packages("reghelper")
library(reghelper)
#install.packages("car")
library(car)
#install.packages("stats")
library(stats)
#install.packages("lsr")
library(lsr)
#install.packages("psych")
library(psych)
#install.packages("devtools")
library(devtools)
#install_github("easyGgplot2", "kassambara")
library(easyGgplot2)
#install.packages("lme4")
library(lme4)
Attaching package: 'reghelper' The following object is masked from 'package:base': beta Loading required package: carData Warning message: "package 'carData' was built under R version 3.5.2" Attaching package: 'psych' The following object is masked from 'package:car': logit The following object is masked from 'package:reghelper': ICC Loading required package: ggplot2 Attaching package: 'ggplot2' The following objects are masked from 'package:psych': %+%, alpha Loading required package: Matrix Attaching package: 'lme4' The following object is masked from 'package:nlme': lmList
myNoGoData_d <- read.csv('nogostatssheet_full.csv')
myGoData_d <- read.csv('gostatssheet_full.csv')
#head(myNoGoData_d)
#head(myGoData_d)
df_FamCongNoGo_d <- subset(myNoGoData_d, StimulusType=="Familiar" & Congruency=="Congruent" & FeedbackCond=="NoFeedback")
df_FamCongGo_d <- subset(myGoData_d, StimulusType=="Familiar" & Congruency=="Congruent" & FeedbackCond=="NoFeedback")
df_FamCong_dprime <- data.frame(Subject = df_FamCongNoGo_d[, c("Subject")], Perc_Hit = df_FamCongGo_d[, c("Accuracy")], Perc_FA = (1-df_FamCongNoGo_d[, c("Accuracy")]), Perc_Miss = (1-df_FamCongGo_d[, c("Accuracy")]), Perc_CR = df_FamCongNoGo_d[, c("Accuracy")])
head(df_FamCong_dprime)
nrow(df_FamCong_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR |
---|---|---|---|---|
1 | 0.81 | 0.30 | 0.19 | 0.70 |
2 | 0.97 | 0.15 | 0.03 | 0.85 |
3 | 0.96 | 0.45 | 0.04 | 0.55 |
4 | 0.92 | 0.30 | 0.08 | 0.70 |
5 | 0.97 | 0.15 | 0.03 | 0.85 |
6 | 0.93 | 0.35 | 0.07 | 0.65 |
df_FamIncongNoGo_d <- subset(myNoGoData_d, StimulusType=="Familiar" & Congruency=="Incongruent" & FeedbackCond=="NoFeedback")
df_FamIncongGo_d <- subset(myGoData_d, StimulusType=="Familiar" & Congruency=="Incongruent" & FeedbackCond=="NoFeedback")
df_FamIncong_dprime <- data.frame(Subject = df_FamIncongNoGo_d[, c("Subject")], Perc_Hit = df_FamIncongGo_d[, c("Accuracy")], Perc_FA = (1-df_FamIncongNoGo_d[, c("Accuracy")]), Perc_Miss = (1-df_FamIncongGo_d[, c("Accuracy")]), Perc_CR = df_FamIncongNoGo_d[, c("Accuracy")])
head(df_FamIncong_dprime)
nrow(df_FamIncong_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR |
---|---|---|---|---|
1 | 0.77 | 0.75 | 0.23 | 0.25 |
2 | 0.96 | 0.25 | 0.04 | 0.75 |
3 | 0.93 | 0.55 | 0.07 | 0.45 |
4 | 0.95 | 0.25 | 0.05 | 0.75 |
5 | 0.95 | 0.25 | 0.05 | 0.75 |
6 | 0.92 | 0.50 | 0.08 | 0.50 |
df_NovCongNoGo_d <- subset(myNoGoData_d, StimulusType=="Novel" & Congruency=="Congruent" & FeedbackCond=="NoFeedback")
df_NovCongGo_d <- subset(myGoData_d, StimulusType=="Novel" & Congruency=="Congruent" & FeedbackCond=="NoFeedback")
df_NovCong_dprime <- data.frame(Subject = df_NovCongNoGo_d[, c("Subject")], Perc_Hit = df_NovCongGo_d[, c("Accuracy")], Perc_FA = (1-df_NovCongNoGo_d[, c("Accuracy")]), Perc_Miss = (1-df_NovCongGo_d[, c("Accuracy")]), Perc_CR = df_NovCongNoGo_d[, c("Accuracy")])
head(df_NovCong_dprime)
nrow(df_NovCong_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR |
---|---|---|---|---|
1 | 0.77 | 0.45 | 0.23 | 0.55 |
2 | 0.95 | 0.25 | 0.05 | 0.75 |
3 | 0.93 | 0.40 | 0.07 | 0.60 |
4 | 0.92 | 0.30 | 0.08 | 0.70 |
5 | 0.93 | 0.25 | 0.07 | 0.75 |
6 | 0.93 | 0.35 | 0.07 | 0.65 |
df_NovIncongNoGo_d <- subset(myNoGoData_d, StimulusType=="Novel" & Congruency=="Incongruent" & FeedbackCond=="NoFeedback")
df_NovIncongGo_d <- subset(myGoData_d, StimulusType=="Novel" & Congruency=="Incongruent" & FeedbackCond=="NoFeedback")
df_NovIncong_dprime <- data.frame(Subject = df_NovIncongNoGo_d[, c("Subject")], Perc_Hit = df_NovIncongGo_d[, c("Accuracy")], Perc_FA = (1-df_NovIncongNoGo_d[, c("Accuracy")]), Perc_Miss = (1-df_NovIncongGo_d[, c("Accuracy")]), Perc_CR = df_NovIncongNoGo_d[, c("Accuracy")])
head(df_NovIncong_dprime)
nrow(df_NovIncong_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR |
---|---|---|---|---|
1 | 0.83 | 0.60 | 0.17 | 0.40 |
2 | 1.00 | 0.25 | 0.00 | 0.75 |
3 | 0.91 | 0.35 | 0.09 | 0.65 |
4 | 0.91 | 0.45 | 0.09 | 0.55 |
5 | 0.95 | 0.10 | 0.05 | 0.90 |
6 | 0.89 | 0.50 | 0.11 | 0.50 |
df_FamCongNoGoFB_d <- subset(myNoGoData_d, StimulusType=="Familiar" & Congruency=="Congruent" & FeedbackCond=="Feedback")
df_FamCongGoFB_d <- subset(myGoData_d, StimulusType=="Familiar" & Congruency=="Congruent" & FeedbackCond=="Feedback")
df_FamCongFB_dprime <- data.frame(Subject = df_FamCongNoGoFB_d[, c("Subject")], Perc_Hit = df_FamCongGoFB_d[, c("Accuracy")], Perc_FA = (1-df_FamCongNoGoFB_d[, c("Accuracy")]), Perc_Miss = (1-df_FamCongGoFB_d[, c("Accuracy")]), Perc_CR = df_FamCongNoGoFB_d[, c("Accuracy")])
head(df_FamCongFB_dprime)
nrow(df_FamCongFB_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR |
---|---|---|---|---|
1 | 0.92 | 0.20 | 0.08 | 0.80 |
2 | 0.98 | 0.10 | 0.02 | 0.90 |
3 | 0.99 | 0.45 | 0.01 | 0.55 |
4 | 0.89 | 0.20 | 0.11 | 0.80 |
5 | 1.00 | 0.25 | 0.00 | 0.75 |
6 | 0.97 | 0.45 | 0.03 | 0.55 |
df_FamIncongNoGoFB_d <- subset(myNoGoData_d, StimulusType=="Familiar" & Congruency=="Incongruent" & FeedbackCond=="Feedback")
df_FamIncongGoFB_d <- subset(myGoData_d, StimulusType=="Familiar" & Congruency=="Incongruent" & FeedbackCond=="Feedback")
df_FamIncongFB_dprime <- data.frame(Subject = df_FamIncongNoGoFB_d[, c("Subject")], Perc_Hit = df_FamIncongGoFB_d[, c("Accuracy")], Perc_FA = (1-df_FamIncongNoGoFB_d[, c("Accuracy")]), Perc_Miss = (1-df_FamIncongGoFB_d[, c("Accuracy")]), Perc_CR = df_FamIncongNoGoFB_d[, c("Accuracy")])
head(df_FamIncongFB_dprime)
nrow(df_FamIncongFB_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR |
---|---|---|---|---|
1 | 0.88 | 0.65 | 0.12 | 0.35 |
2 | 0.99 | 0.15 | 0.01 | 0.85 |
3 | 0.94 | 0.30 | 0.06 | 0.70 |
4 | 0.98 | 0.50 | 0.02 | 0.50 |
5 | 0.96 | 0.45 | 0.04 | 0.55 |
6 | 1.00 | 0.55 | 0.00 | 0.45 |
#install.packages("psycho")
library(psycho)
FamCong_dprime_indices <- psycho::dprime(df_FamCong_dprime$Perc_Hit, df_FamCong_dprime$Perc_FA, df_FamCong_dprime$Perc_Miss, df_FamCong_dprime$Perc_CR)
FamCong_dprime <- cbind(df_FamCong_dprime, FamCong_dprime_indices)
head(FamCong_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR | dprime | beta | aprime | bppd | c |
---|---|---|---|---|---|---|---|---|---|
1 | 0.81 | 0.30 | 0.19 | 0.70 | 0.5278942 | 1.113557 | 0.8395503 | -0.2925532 | 0.20375169 |
2 | 0.97 | 0.15 | 0.03 | 0.85 | 0.8657486 | 1.199872 | 0.9525167 | -0.7017544 | 0.21047109 |
3 | 0.96 | 0.45 | 0.04 | 0.55 | 0.5176180 | 1.024452 | 0.8646307 | -0.9030837 | 0.04667179 |
4 | 0.92 | 0.30 | 0.08 | 0.70 | 0.6389834 | 1.099331 | 0.8899068 | -0.6626506 | 0.14820711 |
5 | 0.97 | 0.15 | 0.03 | 0.85 | 0.8657486 | 1.199872 | 0.9525167 | -0.7017544 | 0.21047109 |
6 | 0.93 | 0.35 | 0.07 | 0.65 | 0.5939314 | 1.071005 | 0.8789909 | -0.7547170 | 0.11549741 |
FamIncong_dprime_indices <- psycho::dprime(df_FamIncong_dprime$Perc_Hit, df_FamIncong_dprime$Perc_FA, df_FamIncong_dprime$Perc_Miss, df_FamIncong_dprime$Perc_CR)
FamIncong_dprime <- cbind(df_FamIncong_dprime, FamIncong_dprime_indices)
head(FamIncong_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR | dprime | beta | aprime | bppd | c |
---|---|---|---|---|---|---|---|---|---|
1 | 0.77 | 0.75 | 0.23 | 0.25 | 0.02005437 | 0.9997989 | 0.5264935 | -0.8188976 | -0.01002719 |
2 | 0.96 | 0.25 | 0.04 | 0.75 | 0.73653771 | 1.1218699 | 0.9215625 | -0.7777778 | 0.15613166 |
3 | 0.93 | 0.55 | 0.07 | 0.45 | 0.38336180 | 1.0039228 | 0.8132616 | -0.8839779 | 0.01021258 |
4 | 0.95 | 0.25 | 0.05 | 0.75 | 0.72629399 | 1.1242515 | 0.9175439 | -0.7272727 | 0.16125352 |
5 | 0.95 | 0.25 | 0.05 | 0.75 | 0.72629399 | 1.1242515 | 0.9175439 | -0.7272727 | 0.16125352 |
6 | 0.92 | 0.50 | 0.08 | 0.50 | 0.42463169 | 1.0175758 | 0.8241304 | -0.8400000 | 0.04103126 |
NovCong_dprime_indices <- psycho::dprime(df_NovCong_dprime$Perc_Hit, df_NovCong_dprime$Perc_FA, df_NovCong_dprime$Perc_Miss, df_NovCong_dprime$Perc_CR)
NovCong_dprime <- cbind(df_NovCong_dprime, NovCong_dprime_indices)
head(NovCong_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR | dprime | beta | aprime | bppd | c |
---|---|---|---|---|---|---|---|---|---|
1 | 0.77 | 0.45 | 0.23 | 0.55 | 0.3255352 | 1.047554 | 0.7493506 | -0.4651163 | 0.14271321 |
2 | 0.95 | 0.25 | 0.05 | 0.75 | 0.7262940 | 1.124251 | 0.9175439 | -0.7272727 | 0.16125352 |
3 | 0.93 | 0.40 | 0.07 | 0.60 | 0.5399271 | 1.048941 | 0.8633065 | -0.7971014 | 0.08849524 |
4 | 0.92 | 0.30 | 0.08 | 0.70 | 0.6389834 | 1.099331 | 0.8899068 | -0.6626506 | 0.14820711 |
5 | 0.93 | 0.25 | 0.07 | 0.75 | 0.7058688 | 1.128662 | 0.9094624 | -0.6315789 | 0.17146610 |
6 | 0.93 | 0.35 | 0.07 | 0.65 | 0.5939314 | 1.071005 | 0.8789909 | -0.7547170 | 0.11549741 |
NovIncong_dprime_indices <- psycho::dprime(df_NovIncong_dprime$Perc_Hit, df_NovIncong_dprime$Perc_FA, df_NovIncong_dprime$Perc_Miss, df_NovIncong_dprime$Perc_CR)
NovIncong_dprime <- cbind(df_NovIncong_dprime, NovIncong_dprime_indices)
head(NovIncong_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR | dprime | beta | aprime | bppd | c |
---|---|---|---|---|---|---|---|---|---|
1 | 0.83 | 0.60 | 0.17 | 0.40 | 0.2312675 | 1.008205 | 0.7130271 | -0.7597173 | 0.03533545 |
2 | 1.00 | 0.25 | 0.00 | 0.75 | 0.7777476 | 1.111161 | 0.9375000 | -1.0000000 | 0.13552670 |
3 | 0.91 | 0.35 | 0.09 | 0.65 | 0.5735817 | 1.074745 | 0.8692308 | -0.6896552 | 0.12567227 |
4 | 0.91 | 0.45 | 0.09 | 0.55 | 0.4665994 | 1.034253 | 0.8354645 | -0.7843137 | 0.07218110 |
5 | 0.95 | 0.10 | 0.05 | 0.90 | 0.9081960 | 1.257406 | 0.9597953 | -0.3571429 | 0.25220454 |
6 | 0.89 | 0.50 | 0.11 | 0.50 | 0.3941825 | 1.022423 | 0.8045506 | -0.7800000 | 0.05625587 |
FamCongFB_dprime_indices <- psycho::dprime(df_FamCongFB_dprime$Perc_Hit, df_FamCongFB_dprime$Perc_FA, df_FamCongFB_dprime$Perc_Miss, df_FamCongFB_dprime$Perc_CR)
FamCongFB_dprime <- cbind(df_FamCongFB_dprime, FamCongFB_dprime_indices)
head(FamCongFB_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR | dprime | beta | aprime | bppd | c |
---|---|---|---|---|---|---|---|---|---|
1 | 0.92 | 0.20 | 0.08 | 0.80 | 0.7541261 | 1.167872 | 0.9206522 | -0.4838710 | 0.20577846 |
2 | 0.98 | 0.10 | 0.02 | 0.90 | 0.9389953 | 1.249019 | 0.9689342 | -0.6896552 | 0.23680491 |
3 | 0.99 | 0.45 | 0.01 | 0.55 | 0.5484878 | 1.017281 | 0.8818182 | -0.9756098 | 0.03123691 |
4 | 0.89 | 0.20 | 0.11 | 0.80 | 0.7236769 | 1.173434 | 0.9094452 | -0.3383459 | 0.22100307 |
5 | 1.00 | 0.25 | 0.00 | 0.75 | 0.7777476 | 1.111161 | 0.9375000 | -1.0000000 | 0.13552670 |
6 | 0.97 | 0.45 | 0.03 | 0.55 | 0.5278840 | 1.022170 | 0.8703843 | -0.9271523 | 0.04153878 |
FamIncongFB_dprime_indices <- psycho::dprime(df_FamIncongFB_dprime$Perc_Hit, df_FamIncongFB_dprime$Perc_FA, df_FamIncongFB_dprime$Perc_Miss, df_FamIncongFB_dprime$Perc_CR)
FamIncongFB_dprime <- cbind(df_FamIncongFB_dprime, FamIncongFB_dprime_indices)
head(FamIncongFB_dprime)
Subject | Perc_Hit | Perc_FA | Perc_Miss | Perc_CR | dprime | beta | aprime | bppd | c |
---|---|---|---|---|---|---|---|---|---|
1 | 0.88 | 0.65 | 0.12 | 0.35 | 0.2311497 | 0.9965063 | 0.7296266 | -0.8631922 | -0.01514112 |
2 | 0.99 | 0.15 | 0.01 | 0.85 | 0.8863524 | 1.1941331 | 0.9591800 | -0.8917197 | 0.20016922 |
3 | 0.94 | 0.30 | 0.06 | 0.70 | 0.6593697 | 1.0952714 | 0.8987842 | -0.7407407 | 0.13801395 |
4 | 0.98 | 0.50 | 0.02 | 0.50 | 0.4860399 | 1.0050320 | 0.8624490 | -0.9600000 | 0.01032718 |
5 | 0.96 | 0.45 | 0.04 | 0.55 | 0.5176180 | 1.0244523 | 0.8646307 | -0.9030837 | 0.04667179 |
6 | 1.00 | 0.55 | 0.00 | 0.45 | 0.4552406 | 0.9883564 | 0.8625000 | -1.0000000 | -0.02572681 |
df_dprime = data.frame(Subject = df_FamCongNoGo_d[, c("Subject")], FamCongDay1_dprime = FamCong_dprime[, ("dprime")], FamIncongDay1_dprime = FamIncong_dprime[, ("dprime")], NovCongDay1_dprime = NovCong_dprime[, ("dprime")], NovIncongDay1_dprime = NovIncong_dprime[, ("dprime")], FamCongDay2_dprime = FamCongFB_dprime[, ("dprime")], FamIncongDay2_dprime = FamIncongFB_dprime[, ("dprime")])
head(df_dprime)
#write to csv so it can be melted in python
#write.csv(df_dprime, file = "df_dprime_wide.csv")
Subject | FamCongDay1_dprime | FamIncongDay1_dprime | NovCongDay1_dprime | NovIncongDay1_dprime | FamCongDay2_dprime | FamIncongDay2_dprime |
---|---|---|---|---|---|---|
1 | 0.5278942 | 0.02005437 | 0.3255352 | 0.2312675 | 0.7541261 | 0.2311497 |
2 | 0.8657486 | 0.73653771 | 0.7262940 | 0.7777476 | 0.9389953 | 0.8863524 |
3 | 0.5176180 | 0.38336180 | 0.5399271 | 0.5735817 | 0.5484878 | 0.6593697 |
4 | 0.6389834 | 0.72629399 | 0.6389834 | 0.4665994 | 0.7236769 | 0.4860399 |
5 | 0.8657486 | 0.72629399 | 0.7058688 | 0.9081960 | 0.7777476 | 0.5176180 |
6 | 0.5939314 | 0.42463169 | 0.5939314 | 0.3941825 | 0.5278840 | 0.4552406 |
#read long dprime data from python csv
df_dprime_long <- read.csv("dprime_long.csv")
head(df_dprime_long)
X | Subject | StimulusType | Congruency | FeedbackCond | DV | dprime |
---|---|---|---|---|---|---|
0 | 1 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.5278942 |
1 | 2 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.8657486 |
2 | 3 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.5176180 |
3 | 4 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.6389834 |
4 | 5 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.8657486 |
5 | 6 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.5939314 |
library(ez)
print("Day 1 (no feedback) StimulusType*Congruency interaction of dprime values")
dprime_anova <- ezANOVA(
data = subset(df_dprime_long, FeedbackCond=="NoFeedback")
, dv = .(dprime)
, wid = .(Subject)
, within = .(StimulusType, Congruency)
, type = 1
)
print(dprime_anova)
print("Day 2 (feedback) FeedbackCond*Congruency interaction of dprime values")
dprime_anova_fb <- ezANOVA(
data = subset(df_dprime_long, StimulusType=="Familiar")
, dv = .(dprime)
, wid = .(Subject)
, within = .(FeedbackCond, Congruency)
, type = 1
)
print(dprime_anova_fb)
Warning message: "package 'ez' was built under R version 3.5.3"
[1] "Day 1 (no feedback) StimulusType*Congruency interaction of dprime values"
Warning message: "Converting "Subject" to factor for ANOVA."
$ANOVA Effect DFn DFd F p p<.05 ges 1 StimulusType 1 103 0.2653472 6.075739e-01 0.001092392 2 Congruency 1 103 13.0718664 4.658828e-04 * 0.030638504 3 StimulusType:Congruency 1 103 25.4235121 1.980098e-06 * 0.074570098 [1] "Day 2 (feedback) FeedbackCond*Congruency interaction of dprime values"
Warning message: "Converting "Subject" to factor for ANOVA."
$ANOVA Effect DFn DFd F p p<.05 ges 1 FeedbackCond 1 103 3.954874 4.938841e-02 * 0.01832967 2 Congruency 1 103 14.967710 1.919456e-04 * 0.03688528 3 FeedbackCond:Congruency 1 103 24.202606 3.297861e-06 * 0.05551951
print("Familiar stimuli, congruent vs. incongruent paired t-test")
t.test(subset(df_dprime_long, StimulusType=="Familiar" & FeedbackCond=="NoFeedback" & Congruency=="Congruent")$dprime, subset(df_dprime_long, StimulusType=="Familiar" & FeedbackCond=="NoFeedback" & Congruency=="Incongruent")$dprime, paired=TRUE)[1:3]
print("Novel stimuli, congruent vs. incongruent paired t-test")
t.test(subset(df_dprime_long, StimulusType=="Novel" & FeedbackCond=="NoFeedback" & Congruency=="Congruent")$dprime, subset(df_dprime_long, StimulusType=="Novel" & FeedbackCond=="NoFeedback" & Congruency=="Incongruent")$dprime, paired=TRUE)[1:3]
print("Congruent phase, Familiar vs. Novel paired t-test")
t.test(subset(df_dprime_long, StimulusType=="Familiar" & FeedbackCond=="NoFeedback" & Congruency=="Congruent")$dprime, subset(df_dprime_long, StimulusType=="Novel" & FeedbackCond=="NoFeedback" & Congruency=="Congruent")$dprime, paired=TRUE)[1:3]
print("Incongruent phase, Familiar vs. Novel paired t-test")
t.test(subset(df_dprime_long, StimulusType=="Familiar" & FeedbackCond=="NoFeedback" & Congruency=="Incongruent")$dprime, subset(df_dprime_long, StimulusType=="Novel" & FeedbackCond=="NoFeedback" & Congruency=="Incongruent")$dprime, paired=TRUE)[1:3]
print("Familiar stimuli, Feedback condition, congruent vs. incongruent paired t-test")
t.test(subset(df_dprime_long, StimulusType=="Familiar" & FeedbackCond=="Feedback" & Congruency=="Congruent")$dprime, subset(df_dprime_long, StimulusType=="Familiar" & FeedbackCond=="Feedback" & Congruency=="Incongruent")$dprime, paired=TRUE)[1:3]
[1] "Familiar stimuli, congruent vs. incongruent paired t-test"
[1] "Novel stimuli, congruent vs. incongruent paired t-test"
[1] "Congruent phase, Familiar vs. Novel paired t-test"
[1] "Incongruent phase, Familiar vs. Novel paired t-test"
[1] "Familiar stimuli, Feedback condition, congruent vs. incongruent paired t-test"
summary(subset(df_dprime_long, FeedbackCond=="NoFeedback" & Congruency=="Congruent" & StimulusType=="Familiar")$dprime)
summary(subset(df_dprime_long, FeedbackCond=="NoFeedback" & Congruency=="Incongruent" & StimulusType=="Familiar")$dprime)
summary(subset(df_dprime_long, FeedbackCond=="NoFeedback" & Congruency=="Congruent" & StimulusType=="Novel")$dprime)
summary(subset(df_dprime_long, FeedbackCond=="NoFeedback" & Congruency=="Incongruent" & StimulusType=="Novel")$dprime)
summary(subset(df_dprime_long, FeedbackCond=="Feedback" & Congruency=="Congruent" & StimulusType=="Familiar")$dprime)
summary(subset(df_dprime_long, FeedbackCond=="Feedback" & Congruency=="Incongruent" & StimulusType=="Familiar")$dprime)
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.09036 0.52788 0.69569 0.68291 0.85548 1.06402
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.02005 0.37376 0.55683 0.56194 0.74467 1.00489
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.4246 0.6165 0.5999 0.7486 1.0743
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0407 0.4860 0.6463 0.6277 0.7794 1.0640
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0000 0.5130 0.7059 0.6537 0.8284 1.0950
Min. 1st Qu. Median Mean 3rd Qu. Max. 0.03014 0.47824 0.71215 0.66660 0.85294 1.09497
##take individual difference variables from Exp8_NoGo_Full.csv, join them with the dprime long dataframe
#head(read.csv("Exp8_NoGo_Full.csv"))
idvar <- read.csv("Exp8_NoGo_Full.csv")[, 5:14]
idvar_long <- rbind(idvar,idvar[])
df_dprime_long_mm <- cbind(df_dprime_long, idvar_long)
nrow(df_dprime_long_mm)
ncol(df_dprime_long_mm)
df_dprime_long_mm$X <- NULL
head(df_dprime_long_mm)
#write.csv(df_dprime_long_mm, file="df_dprime_long_mm.csv")
Subject | StimulusType | Congruency | FeedbackCond | DV | dprime | Cong_Order | Stim_Order | ASRS_A | ASRS_B | ASRS_Total | COHS | Age | Gender | Drive | Diagnosis |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.5278942 | 0 | 0 | 18 | 10 | 28 | 131 | 23 | 0 | 84 | 0 |
2 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.8657486 | 0 | 0 | 12 | 10 | 22 | 111 | 19 | 0 | 16 | 0 |
3 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.5176180 | 1 | 0 | 10 | 10 | 20 | 83 | 19 | 1 | 1 | 0 |
4 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.6389834 | 1 | 0 | 18 | 13 | 31 | 105 | 18 | 0 | 18 | 0 |
5 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.8657486 | 2 | 1 | 22 | 17 | 39 | 77 | 20 | 0 | 27 | 1 |
6 | Familiar | Congruent | NoFeedback | FamCongDay1_dprime | 0.5939314 | 2 | 1 | 8 | 18 | 26 | 105 | 18 | 1 | 6 | 0 |
#Day 1 model with dprime
dprime_nofb_model1_r <- lme(dprime ~ Gender + Cong_Order + Drive, random=~1|Subject, method="ML", data=subset(df_dprime_long_mm, FeedbackCond=="NoFeedback"))
dprime_nofb_model2_r <- lme(dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS, random=~1|Subject, method="ML", data=subset(df_dprime_long_mm, FeedbackCond=="NoFeedback"))
dprime_nofb_model3_r <- lme(dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + StimulusType + Congruency + StimulusType*Congruency, random=~1|Subject, method="ML", data=subset(df_dprime_long_mm, FeedbackCond=="NoFeedback"))
dprime_nofb_model4_r <- lme(dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + StimulusType + Congruency + StimulusType*Congruency + ASRS_A*StimulusType*Congruency + ASRS_B*StimulusType*Congruency + Diagnosis*StimulusType*Congruency + COHS*StimulusType*Congruency, random=~1|Subject, method="ML", data=subset(df_dprime_long_mm, FeedbackCond=="NoFeedback"))
#Check for outliers, beyond -3.3<x<3.3. No output means no outliers.
which(abs(residuals(dprime_nofb_model1_r, type="normalized")) > 3.3)
which(abs(residuals(dprime_nofb_model2_r, type="normalized")) > 3.3)
which(abs(residuals(dprime_nofb_model3_r, type="normalized")) > 3.3)
which(abs(residuals(dprime_nofb_model4_r, type="normalized")) > 3.3)
#Diagnostics. plot() checks for homoscedasticity violation, qqplot() checks for normality, vif() checks for multicollinearity
qqnorm(resid(dprime_nofb_model1_r))
qqnorm(resid(dprime_nofb_model2_r))
qqnorm(resid(dprime_nofb_model3_r))
qqnorm(resid(dprime_nofb_model4_r))
plot(dprime_nofb_model1_r)
plot(dprime_nofb_model2_r)
plot(dprime_nofb_model3_r)
plot(dprime_nofb_model4_r)
vif(dprime_nofb_model1_r)
vif(dprime_nofb_model2_r)
vif(dprime_nofb_model3_r)
vif(dprime_nofb_model4_r)
#Use beta from reghelper, otherwise beta coefs won't be standardized
beta(dprime_nofb_model1_r)
beta(dprime_nofb_model2_r)
beta(dprime_nofb_model3_r)
beta(dprime_nofb_model4_r)
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 1053.502 1077.686 -520.7511 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.716217 0.6858888 Fixed effects: dprime.z ~ Gender.z + Cong_Order.z + Drive.z Value Std.Error DF t-value p-value (Intercept) 0.00000000 0.07824394 312 0.0000000 1.0000 Gender.z 0.07082378 0.07891434 100 0.8974767 0.3716 Cong_Order.z 0.00163141 0.07889070 100 0.0206794 0.9835 Drive.z 0.09229977 0.07848831 100 1.1759684 0.2424 Correlation: (Intr) Gndr.z Cng_O. Gender.z 0.000 Cong_Order.z 0.000 -0.110 Drive.z 0.000 -0.045 -0.037 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.40116512 -0.60916418 0.06488038 0.61371615 2.24711491 Number of Observations: 416 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 1060.535 1100.841 -520.2673 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.7121285 0.6858888 Fixed effects: dprime.z ~ Gender.z + Cong_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + Diagnosis.z + COHS.z Value Std.Error DF t-value p-value (Intercept) 0.00000000 0.07826162 312 0.0000000 1.0000 Gender.z 0.06406711 0.08141145 96 0.7869546 0.4332 Cong_Order.z 0.01012538 0.07995611 96 0.1266367 0.8995 Drive.z 0.08634509 0.08947758 96 0.9649913 0.3370 ASRS_A.z 0.02202359 0.09962766 96 0.2210590 0.8255 ASRS_B.z -0.08612770 0.10234983 96 -0.8415031 0.4022 Diagnosis.z -0.02888103 0.08951545 96 -0.3226374 0.7477 COHS.z 0.02867349 0.08078436 96 0.3549386 0.7234 Correlation: (Intr) Gndr.z Cng_O. Driv.z ASRS_A ASRS_B Dgnss. Gender.z 0.000 Cong_Order.z 0.000 -0.118 Drive.z 0.000 -0.112 -0.073 ASRS_A.z 0.000 -0.178 0.114 0.011 ASRS_B.z 0.000 0.096 -0.152 0.165 -0.596 Diagnosis.z 0.000 0.187 0.025 -0.422 -0.154 0.107 COHS.z 0.000 -0.055 0.041 -0.062 0.159 -0.216 0.066 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.37763800 -0.61796068 0.06366376 0.62082250 2.25492182 Number of Observations: 416 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 1033.054 1085.453 -503.5269 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.7204819 0.6500573 Fixed effects: dprime.z ~ Gender.z + Cong_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + Diagnosis.z + COHS.z + StimulusTypeNovel.z + CongruencyIncongruent.z + StimulusTypeNovel.z * CongruencyIncongruent.z Value Std.Error DF (Intercept) 0.00000000 0.07855094 309 Gender.z 0.06406711 0.08171242 96 Cong_Order.z 0.01012538 0.08025170 96 Drive.z 0.08634509 0.08980837 96 ASRS_A.z 0.02202359 0.09999597 96 ASRS_B.z -0.08612770 0.10272821 96 Diagnosis.z -0.02888103 0.08984637 96 COHS.z 0.02867349 0.08108300 96 StimulusTypeNovel.z -0.01863938 0.03234050 309 CongruencyIncongruent.z -0.10020647 0.03234050 309 StimulusTypeNovel.z:CongruencyIncongruent.z 0.16019098 0.03237944 309 t-value p-value (Intercept) 0.000000 1.0000 Gender.z 0.784056 0.4349 Cong_Order.z 0.126170 0.8999 Drive.z 0.961437 0.3387 ASRS_A.z 0.220245 0.8261 ASRS_B.z -0.838404 0.4039 Diagnosis.z -0.321449 0.7486 COHS.z 0.353631 0.7244 StimulusTypeNovel.z -0.576348 0.5648 CongruencyIncongruent.z -3.098482 0.0021 StimulusTypeNovel.z:CongruencyIncongruent.z 4.947306 0.0000 Correlation: (Intr) Gndr.z Cng_O. Driv.z ASRS_A Gender.z 0.000 Cong_Order.z 0.000 -0.118 Drive.z 0.000 -0.112 -0.073 ASRS_A.z 0.000 -0.178 0.114 0.011 ASRS_B.z 0.000 0.096 -0.152 0.165 -0.596 Diagnosis.z 0.000 0.187 0.025 -0.422 -0.154 COHS.z 0.000 -0.055 0.041 -0.062 0.159 StimulusTypeNovel.z 0.000 0.000 0.000 0.000 0.000 CongruencyIncongruent.z 0.000 0.000 0.000 0.000 0.000 StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 0.000 0.000 ASRS_B Dgnss. COHS.z StmTN. CngrI. Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z 0.107 COHS.z -0.216 0.066 StimulusTypeNovel.z 0.000 0.000 0.000 CongruencyIncongruent.z 0.000 0.000 0.000 0.000 StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 0.000 0.000 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.55479336 -0.58456294 0.07278563 0.63535710 2.34778924 Number of Observations: 416 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 1041.383 1142.15 -495.6914 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.7240645 0.633935 Fixed effects: dprime.z ~ Gender.z + Cong_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + Diagnosis.z + COHS.z + StimulusTypeNovel.z + CongruencyIncongruent.z + StimulusTypeNovel.z * CongruencyIncongruent.z + ASRS_A.z * StimulusTypeNovel.z * CongruencyIncongruent.z + ASRS_B.z * StimulusTypeNovel.z * CongruencyIncongruent.z + Diagnosis.z * StimulusTypeNovel.z * CongruencyIncongruent.z + COHS.z * StimulusTypeNovel.z * CongruencyIncongruent.z Value Std.Error (Intercept) 0.00000000 0.07974117 Gender.z 0.06406711 0.08295055 Cong_Order.z 0.01012538 0.08146770 Drive.z 0.08634509 0.09116918 ASRS_A.z 0.02202359 0.10151115 ASRS_B.z -0.08612770 0.10428478 Diagnosis.z -0.02888103 0.09120776 COHS.z 0.02867349 0.08231160 StimulusTypeNovel.z -0.01863938 0.03201630 CongruencyIncongruent.z -0.10020647 0.03201630 StimulusTypeNovel.z:CongruencyIncongruent.z 0.16019098 0.03205485 ASRS_A.z:StimulusTypeNovel.z -0.08492669 0.03992174 ASRS_A.z:CongruencyIncongruent.z -0.01424861 0.03992174 ASRS_B.z:StimulusTypeNovel.z 0.08288701 0.04066376 ASRS_B.z:CongruencyIncongruent.z 0.00924979 0.04066376 Diagnosis.z:StimulusTypeNovel.z 0.00072237 0.03280470 Diagnosis.z:CongruencyIncongruent.z 0.03795031 0.03280470 COHS.z:StimulusTypeNovel.z -0.01453741 0.03290563 COHS.z:CongruencyIncongruent.z -0.02605947 0.03290563 ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z -0.03275842 0.03996981 ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z -0.01032024 0.04071272 Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.05515102 0.03284420 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.06098196 0.03294525 DF t-value p-value (Intercept) 297 0.000000 1.0000 Gender.z 96 0.772353 0.4418 Cong_Order.z 96 0.124287 0.9013 Drive.z 96 0.947086 0.3460 ASRS_A.z 96 0.216957 0.8287 ASRS_B.z 96 -0.825889 0.4109 Diagnosis.z 96 -0.316651 0.7522 COHS.z 96 0.348353 0.7283 StimulusTypeNovel.z 297 -0.582184 0.5609 CongruencyIncongruent.z 297 -3.129858 0.0019 StimulusTypeNovel.z:CongruencyIncongruent.z 297 4.997403 0.0000 ASRS_A.z:StimulusTypeNovel.z 297 -2.127329 0.0342 ASRS_A.z:CongruencyIncongruent.z 297 -0.356914 0.7214 ASRS_B.z:StimulusTypeNovel.z 297 2.038351 0.0424 ASRS_B.z:CongruencyIncongruent.z 297 0.227470 0.8202 Diagnosis.z:StimulusTypeNovel.z 297 0.022020 0.9824 Diagnosis.z:CongruencyIncongruent.z 297 1.156856 0.2483 COHS.z:StimulusTypeNovel.z 297 -0.441791 0.6590 COHS.z:CongruencyIncongruent.z 297 -0.791946 0.4290 ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z 297 -0.819579 0.4131 ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z 297 -0.253489 0.8001 Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z 297 1.679171 0.0942 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z 297 1.851009 0.0652 Correlation: (Intr) Gndr.z Cng_O. Gender.z 0.000 Cong_Order.z 0.000 -0.118 Drive.z 0.000 -0.112 -0.073 ASRS_A.z 0.000 -0.178 0.114 ASRS_B.z 0.000 0.096 -0.152 Diagnosis.z 0.000 0.187 0.025 COHS.z 0.000 -0.055 0.041 StimulusTypeNovel.z 0.000 0.000 0.000 CongruencyIncongruent.z 0.000 0.000 0.000 StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z 0.000 0.000 0.000 ASRS_A.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z 0.000 0.000 0.000 ASRS_B.z:CongruencyIncongruent.z 0.000 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z 0.000 0.000 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 0.000 0.000 COHS.z:StimulusTypeNovel.z 0.000 0.000 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 Driv.z ASRS_A.z Gender.z Cong_Order.z Drive.z ASRS_A.z 0.011 ASRS_B.z 0.165 -0.596 Diagnosis.z -0.422 -0.154 COHS.z -0.062 0.159 StimulusTypeNovel.z 0.000 0.000 CongruencyIncongruent.z 0.000 0.000 StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z 0.000 0.000 ASRS_A.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z 0.000 0.000 ASRS_B.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z 0.000 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:StimulusTypeNovel.z 0.000 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z Dgnss. COHS.z Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z 0.107 COHS.z -0.216 0.066 StimulusTypeNovel.z 0.000 0.000 0.000 CongruencyIncongruent.z 0.000 0.000 0.000 StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z 0.000 0.000 0.000 ASRS_A.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z 0.000 0.000 0.000 ASRS_B.z:CongruencyIncongruent.z 0.000 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z 0.000 0.000 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 0.000 0.000 COHS.z:StimulusTypeNovel.z 0.000 0.000 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 StmTN. CngrI. STN.:C Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z StimulusTypeNovel.z CongruencyIncongruent.z 0.000 StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z 0.000 0.000 0.000 ASRS_A.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z 0.000 0.000 0.000 ASRS_B.z:CongruencyIncongruent.z 0.000 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z 0.000 0.000 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 0.000 0.000 COHS.z:StimulusTypeNovel.z 0.000 0.000 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 0.000 ASRS_A.z:STN. ASRS_A.:C Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z StimulusTypeNovel.z CongruencyIncongruent.z StimulusTypeNovel.z:CongruencyIncongruent.z ASRS_A.z:StimulusTypeNovel.z ASRS_A.z:CongruencyIncongruent.z 0.000 ASRS_B.z:StimulusTypeNovel.z -0.594 0.000 ASRS_B.z:CongruencyIncongruent.z 0.000 -0.594 Diagnosis.z:StimulusTypeNovel.z -0.143 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 -0.143 COHS.z:StimulusTypeNovel.z 0.149 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.149 ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:STN. ASRS_B.:C Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z StimulusTypeNovel.z CongruencyIncongruent.z StimulusTypeNovel.z:CongruencyIncongruent.z ASRS_A.z:StimulusTypeNovel.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:StimulusTypeNovel.z ASRS_B.z:CongruencyIncongruent.z 0.000 Diagnosis.z:StimulusTypeNovel.z 0.186 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 0.186 COHS.z:StimulusTypeNovel.z -0.202 0.000 COHS.z:CongruencyIncongruent.z 0.000 -0.202 ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 Dg.:STN. D.:CI. Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z StimulusTypeNovel.z CongruencyIncongruent.z StimulusTypeNovel.z:CongruencyIncongruent.z ASRS_A.z:StimulusTypeNovel.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:StimulusTypeNovel.z ASRS_B.z:CongruencyIncongruent.z Diagnosis.z:StimulusTypeNovel.z Diagnosis.z:CongruencyIncongruent.z 0.000 COHS.z:StimulusTypeNovel.z 0.054 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.054 ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:STN. COHS.:C Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z StimulusTypeNovel.z CongruencyIncongruent.z StimulusTypeNovel.z:CongruencyIncongruent.z ASRS_A.z:StimulusTypeNovel.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:StimulusTypeNovel.z ASRS_B.z:CongruencyIncongruent.z Diagnosis.z:StimulusTypeNovel.z Diagnosis.z:CongruencyIncongruent.z COHS.z:StimulusTypeNovel.z COHS.z:CongruencyIncongruent.z 0.000 ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.:STN.: Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z StimulusTypeNovel.z CongruencyIncongruent.z StimulusTypeNovel.z:CongruencyIncongruent.z ASRS_A.z:StimulusTypeNovel.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:StimulusTypeNovel.z ASRS_B.z:CongruencyIncongruent.z Diagnosis.z:StimulusTypeNovel.z Diagnosis.z:CongruencyIncongruent.z COHS.z:StimulusTypeNovel.z COHS.z:CongruencyIncongruent.z ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z -0.594 Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z -0.143 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.149 ASRS_B.:STN.: D.:STN.: Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z StimulusTypeNovel.z CongruencyIncongruent.z StimulusTypeNovel.z:CongruencyIncongruent.z ASRS_A.z:StimulusTypeNovel.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:StimulusTypeNovel.z ASRS_B.z:CongruencyIncongruent.z Diagnosis.z:StimulusTypeNovel.z Diagnosis.z:CongruencyIncongruent.z COHS.z:StimulusTypeNovel.z COHS.z:CongruencyIncongruent.z ASRS_A.z:StimulusTypeNovel.z:CongruencyIncongruent.z ASRS_B.z:StimulusTypeNovel.z:CongruencyIncongruent.z Diagnosis.z:StimulusTypeNovel.z:CongruencyIncongruent.z 0.186 COHS.z:StimulusTypeNovel.z:CongruencyIncongruent.z -0.202 0.054 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.27145310 -0.62629495 0.08271762 0.59802681 2.56527704 Number of Observations: 416 Number of Groups: 104
#Extract the R^2 value of each model
r.squaredGLMM(dprime_nofb_model1_r)
r.squaredGLMM(dprime_nofb_model2_r)
r.squaredGLMM(dprime_nofb_model3_r)
r.squaredGLMM(dprime_nofb_model4_r)
Warning message: "'r.squaredGLMM' now calculates a revised statistic. See the help page."
R2m | R2c |
---|---|
0.01425388 | 0.5284392 |
R2m | R2c |
---|---|
0.02012145 | 0.5284458 |
R2m | R2c |
---|---|
0.05618978 | 0.5764645 |
R2m | R2c |
---|---|
0.07178549 | 0.5972275 |
#Subtract from each other to derive delta R^2. First will be 2-1, next 3-2.
r.squaredGLMM(dprime_nofb_model2_r) - r.squaredGLMM(dprime_nofb_model1_r)
r.squaredGLMM(dprime_nofb_model3_r) - r.squaredGLMM(dprime_nofb_model2_r)
r.squaredGLMM(dprime_nofb_model4_r) - r.squaredGLMM(dprime_nofb_model3_r)
R2m | R2c |
---|---|
0.005867572 | 6.651851e-06 |
R2m | R2c |
---|---|
0.03606833 | 0.04801871 |
R2m | R2c |
---|---|
0.01559571 | 0.02076297 |
#Compare the models to each other to extract log likelihood ratio Chi^2 values and the associated p-values.
#Df is however many new variables are added to next model.
anova(dprime_nofb_model1_r, dprime_nofb_model2_r, dprime_nofb_model3_r, dprime_nofb_model4_r)
call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
---|---|---|---|---|---|---|---|---|---|
dprime_nofb_model1_r | lme.formula(fixed = dprime ~ Gender + Cong_Order + Drive, data = subset(df_dprime_long_mm, FeedbackCond == "NoFeedback"), random = ~1 | Subject, method = "ML") | 1 | 6 | -159.5025 | -135.31841 | 85.75126 | NA | NA | |
dprime_nofb_model2_r | lme.formula(fixed = dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS, data = subset(df_dprime_long_mm, FeedbackCond == "NoFeedback"), random = ~1 | Subject, method = "ML") | 2 | 10 | -152.4702 | -112.16332 | 86.23508 | 1 vs 2 | 0.967648 | 9.146613e-01 |
dprime_nofb_model3_r | lme.formula(fixed = dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + StimulusType + Congruency + StimulusType * Congruency, data = subset(df_dprime_long_mm, FeedbackCond == "NoFeedback"), random = ~1 | Subject, method = "ML") | 3 | 13 | -179.9509 | -127.55199 | 102.97545 | 2 vs 3 | 33.480729 | 2.549906e-07 |
dprime_nofb_model4_r | lme.formula(fixed = dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + StimulusType + Congruency + StimulusType * Congruency + ASRS_A * StimulusType * Congruency + ASRS_B * StimulusType * Congruency + Diagnosis * StimulusType * Congruency + COHS * StimulusType * Congruency, data = subset(df_dprime_long_mm, FeedbackCond == "NoFeedback"), random = ~1 | Subject, method = "ML") | 4 | 25 | -171.6220 | -70.85487 | 110.81100 | 3 vs 4 | 15.671104 | 2.067687e-01 |
#Day 1 model with dprime
dprime_fb_model1_r <- lme(dprime ~ Gender + Cong_Order + Drive, random=~1|Subject, method="ML", data=subset(df_dprime_long_mm, StimulusType=="Familiar"))
dprime_fb_model2_r <- lme(dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS, random=~1|Subject, method="ML", data=subset(df_dprime_long_mm, StimulusType=="Familiar"))
dprime_fb_model3_r <- lme(dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + FeedbackCond + Congruency + FeedbackCond*Congruency, random=~1|Subject, method="ML", data=subset(df_dprime_long_mm, StimulusType=="Familiar"))
dprime_fb_model4_r <- lme(dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + FeedbackCond + Congruency + FeedbackCond*Congruency + ASRS_A*FeedbackCond*Congruency + ASRS_B*FeedbackCond*Congruency + Diagnosis*FeedbackCond*Congruency + COHS*FeedbackCond*Congruency, random=~1|Subject, method="ML", data=subset(df_dprime_long_mm, StimulusType=="Familiar"))
#Check for outliers, beyond -3.3<x<3.3. No output means no outliers.
which(abs(residuals(dprime_fb_model1_r, type="normalized")) > 3.3)
which(abs(residuals(dprime_fb_model2_r, type="normalized")) > 3.3)
which(abs(residuals(dprime_fb_model3_r, type="normalized")) > 3.3)
which(abs(residuals(dprime_fb_model4_r, type="normalized")) > 3.3)
#Diagnostics. plot() checks for homoscedasticity violation, qqplot() checks for normality, vif() checks for multicollinearity
qqnorm(resid(dprime_fb_model1_r))
qqnorm(resid(dprime_fb_model2_r))
qqnorm(resid(dprime_fb_model3_r))
qqnorm(resid(dprime_fb_model4_r))
plot(dprime_fb_model1_r)
plot(dprime_fb_model2_r)
plot(dprime_fb_model3_r)
plot(dprime_fb_model4_r)
vif(dprime_fb_model1_r)
vif(dprime_fb_model2_r)
vif(dprime_fb_model3_r)
vif(dprime_fb_model4_r)
#Use beta from reghelper, otherwise beta coefs won't be standardized
beta(dprime_fb_model1_r)
beta(dprime_fb_model2_r)
beta(dprime_fb_model3_r)
beta(dprime_fb_model4_r)
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 1049.229 1073.413 -518.6146 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.7142287 0.6819645 Fixed effects: dprime.z ~ Gender.z + Cong_Order.z + Drive.z Value Std.Error DF t-value p-value (Intercept) 0.00000000 0.07798379 312 0.0000000 1.0000 Gender.z 0.07076684 0.07865197 100 0.8997466 0.3704 Cong_Order.z -0.06899935 0.07862840 100 -0.8775372 0.3823 Drive.z 0.11684500 0.07822735 100 1.4936592 0.1384 Correlation: (Intr) Gndr.z Cng_O. Gender.z 0.000 Cong_Order.z 0.000 -0.110 Drive.z 0.000 -0.045 -0.037 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.46791949 -0.57861226 0.08083738 0.60171354 2.39627185 Number of Observations: 416 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 1054.13 1094.437 -517.0649 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.7012353 0.6819645 Fixed effects: dprime.z ~ Gender.z + Cong_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + Diagnosis.z + COHS.z Value Std.Error DF t-value p-value (Intercept) 0.00000000 0.07720611 312 0.0000000 1.0000 Gender.z 0.05393971 0.08031346 96 0.6716149 0.5034 Cong_Order.z -0.05569006 0.07887775 96 -0.7060300 0.4819 Drive.z 0.11977716 0.08827080 96 1.3569284 0.1780 ASRS_A.z 0.04441637 0.09828399 96 0.4519187 0.6523 ASRS_B.z -0.14323894 0.10096945 96 -1.4186365 0.1592 Diagnosis.z -0.07559333 0.08830816 96 -0.8560176 0.3941 COHS.z 0.05526106 0.07969482 96 0.6934085 0.4897 Correlation: (Intr) Gndr.z Cng_O. Driv.z ASRS_A ASRS_B Dgnss. Gender.z 0.000 Cong_Order.z 0.000 -0.118 Drive.z 0.000 -0.112 -0.073 ASRS_A.z 0.000 -0.178 0.114 0.011 ASRS_B.z 0.000 0.096 -0.152 0.165 -0.596 Diagnosis.z 0.000 0.187 0.025 -0.422 -0.154 0.107 COHS.z 0.000 -0.055 0.041 -0.062 0.159 -0.216 0.066 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.5514504 -0.5802645 0.0734819 0.6077699 2.4273641 Number of Observations: 416 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 1025.957 1078.356 -499.9783 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.7097839 0.6456212 Fixed effects: dprime.z ~ Gender.z + Cong_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + Diagnosis.z + COHS.z + FeedbackCondNoFeedback.z + CongruencyIncongruent.z + FeedbackCondNoFeedback.z * CongruencyIncongruent.z Value Std.Error DF (Intercept) 0.00000000 0.07749153 309 Gender.z 0.05393971 0.08061037 96 Cong_Order.z -0.05569006 0.07916935 96 Drive.z 0.11977716 0.08859712 96 ASRS_A.z 0.04441637 0.09864733 96 ASRS_B.z -0.14323894 0.10134272 96 Diagnosis.z -0.07559333 0.08863462 96 COHS.z 0.05526106 0.07998944 96 FeedbackCondNoFeedback.z -0.07649367 0.03211980 309 CongruencyIncongruent.z -0.10955155 0.03211980 309 FeedbackCondNoFeedback.z:CongruencyIncongruent.z -0.13588764 0.03215848 309 t-value p-value (Intercept) 0.000000 1.0000 Gender.z 0.669141 0.5050 Cong_Order.z -0.703430 0.4835 Drive.z 1.351931 0.1796 ASRS_A.z 0.450254 0.6535 ASRS_B.z -1.413411 0.1608 Diagnosis.z -0.852865 0.3959 COHS.z 0.690854 0.4913 FeedbackCondNoFeedback.z -2.381511 0.0178 CongruencyIncongruent.z -3.410717 0.0007 FeedbackCondNoFeedback.z:CongruencyIncongruent.z -4.225562 0.0000 Correlation: (Intr) Gndr.z Cng_O. Driv.z Gender.z 0.000 Cong_Order.z 0.000 -0.118 Drive.z 0.000 -0.112 -0.073 ASRS_A.z 0.000 -0.178 0.114 0.011 ASRS_B.z 0.000 0.096 -0.152 0.165 Diagnosis.z 0.000 0.187 0.025 -0.422 COHS.z 0.000 -0.055 0.041 -0.062 FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 CongruencyIncongruent.z 0.000 0.000 0.000 0.000 FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 0.000 0.000 ASRS_A ASRS_B Dgnss. COHS.z Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z -0.596 Diagnosis.z -0.154 0.107 COHS.z 0.159 -0.216 0.066 FeedbackCondNoFeedback.z 0.000 0.000 0.000 0.000 CongruencyIncongruent.z 0.000 0.000 0.000 0.000 FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 0.000 0.000 FdCNF. CngrI. Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z 0.000 FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.49611536 -0.58180706 0.06750361 0.62914623 2.41376678 Number of Observations: 416 Number of Groups: 104
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 1040.391 1141.158 -495.1953 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev: 0.711997 0.6357991 Fixed effects: dprime.z ~ Gender.z + Cong_Order.z + Drive.z + ASRS_A.z + ASRS_B.z + Diagnosis.z + COHS.z + FeedbackCondNoFeedback.z + CongruencyIncongruent.z + FeedbackCondNoFeedback.z * CongruencyIncongruent.z + ASRS_A.z * FeedbackCondNoFeedback.z * CongruencyIncongruent.z + ASRS_B.z * FeedbackCondNoFeedback.z * CongruencyIncongruent.z + Diagnosis.z * FeedbackCondNoFeedback.z * CongruencyIncongruent.z + COHS.z * FeedbackCondNoFeedback.z * CongruencyIncongruent.z Value (Intercept) 0.00000000 Gender.z 0.05393971 Cong_Order.z -0.05569006 Drive.z 0.11977716 ASRS_A.z 0.04441637 ASRS_B.z -0.14323894 Diagnosis.z -0.07559333 COHS.z 0.05526106 FeedbackCondNoFeedback.z -0.07649367 CongruencyIncongruent.z -0.10955155 FeedbackCondNoFeedback.z:CongruencyIncongruent.z -0.13588764 ASRS_A.z:FeedbackCondNoFeedback.z 0.04996994 ASRS_A.z:CongruencyIncongruent.z -0.01633354 ASRS_B.z:FeedbackCondNoFeedback.z 0.00125566 ASRS_B.z:CongruencyIncongruent.z 0.03036929 Diagnosis.z:FeedbackCondNoFeedback.z 0.03029550 Diagnosis.z:CongruencyIncongruent.z -0.02351840 COHS.z:FeedbackCondNoFeedback.z -0.01844219 COHS.z:CongruencyIncongruent.z -0.05071679 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.03378486 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z -0.01194815 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.00737595 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z -0.03129923 Std.Error DF (Intercept) 0.07866571 297 Gender.z 0.08183180 96 Cong_Order.z 0.08036895 96 Drive.z 0.08993958 96 ASRS_A.z 0.10014208 96 ASRS_B.z 0.10287830 96 Diagnosis.z 0.08997765 96 COHS.z 0.08120147 96 FeedbackCondNoFeedback.z 0.03211044 297 CongruencyIncongruent.z 0.03211044 297 FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.03214910 297 ASRS_A.z:FeedbackCondNoFeedback.z 0.04003913 297 ASRS_A.z:CongruencyIncongruent.z 0.04003913 297 ASRS_B.z:FeedbackCondNoFeedback.z 0.04078333 297 ASRS_B.z:CongruencyIncongruent.z 0.04078333 297 Diagnosis.z:FeedbackCondNoFeedback.z 0.03290116 297 Diagnosis.z:CongruencyIncongruent.z 0.03290116 297 COHS.z:FeedbackCondNoFeedback.z 0.03300239 297 COHS.z:CongruencyIncongruent.z 0.03300239 297 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.04008734 297 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.04083243 297 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.03294078 297 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.03304212 297 t-value p-value (Intercept) 0.000000 1.0000 Gender.z 0.659153 0.5114 Cong_Order.z -0.692930 0.4900 Drive.z 1.331751 0.1861 ASRS_A.z 0.443534 0.6584 ASRS_B.z -1.392314 0.1670 Diagnosis.z -0.840135 0.4029 COHS.z 0.680543 0.4978 FeedbackCondNoFeedback.z -2.382206 0.0178 CongruencyIncongruent.z -3.411711 0.0007 FeedbackCondNoFeedback.z:CongruencyIncongruent.z -4.226794 0.0000 ASRS_A.z:FeedbackCondNoFeedback.z 1.248028 0.2130 ASRS_A.z:CongruencyIncongruent.z -0.407939 0.6836 ASRS_B.z:FeedbackCondNoFeedback.z 0.030789 0.9755 ASRS_B.z:CongruencyIncongruent.z 0.744650 0.4571 Diagnosis.z:FeedbackCondNoFeedback.z 0.920803 0.3579 Diagnosis.z:CongruencyIncongruent.z -0.714820 0.4753 COHS.z:FeedbackCondNoFeedback.z -0.558814 0.5767 COHS.z:CongruencyIncongruent.z -1.536761 0.1254 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.842781 0.4000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z -0.292614 0.7700 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.223916 0.8230 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z -0.947252 0.3443 Correlation: (Intr) Gndr.z Gender.z 0.000 Cong_Order.z 0.000 -0.118 Drive.z 0.000 -0.112 ASRS_A.z 0.000 -0.178 ASRS_B.z 0.000 0.096 Diagnosis.z 0.000 0.187 COHS.z 0.000 -0.055 FeedbackCondNoFeedback.z 0.000 0.000 CongruencyIncongruent.z 0.000 0.000 FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z 0.000 0.000 ASRS_A.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 0.000 ASRS_B.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z 0.000 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 Cng_O. Driv.z Gender.z Cong_Order.z Drive.z -0.073 ASRS_A.z 0.114 0.011 ASRS_B.z -0.152 0.165 Diagnosis.z 0.025 -0.422 COHS.z 0.041 -0.062 FeedbackCondNoFeedback.z 0.000 0.000 CongruencyIncongruent.z 0.000 0.000 FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z 0.000 0.000 ASRS_A.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 0.000 ASRS_B.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z 0.000 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z ASRS_B.z Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z -0.596 Diagnosis.z -0.154 0.107 COHS.z 0.159 -0.216 FeedbackCondNoFeedback.z 0.000 0.000 CongruencyIncongruent.z 0.000 0.000 FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z 0.000 0.000 ASRS_A.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 0.000 ASRS_B.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z 0.000 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 Dgnss. COHS.z Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z 0.066 FeedbackCondNoFeedback.z 0.000 0.000 CongruencyIncongruent.z 0.000 0.000 FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z 0.000 0.000 ASRS_A.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 0.000 ASRS_B.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z 0.000 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 FdCNF. CngrI. Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z 0.000 FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z 0.000 0.000 ASRS_A.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 0.000 ASRS_B.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z 0.000 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.000 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 FCNF.: Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z 0.000 ASRS_A.z:CongruencyIncongruent.z 0.000 ASRS_B.z:FeedbackCondNoFeedback.z 0.000 ASRS_B.z:CongruencyIncongruent.z 0.000 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 Diagnosis.z:CongruencyIncongruent.z 0.000 COHS.z:FeedbackCondNoFeedback.z 0.000 COHS.z:CongruencyIncongruent.z 0.000 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_A.z:FCNF. Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z ASRS_A.z:CongruencyIncongruent.z 0.000 ASRS_B.z:FeedbackCondNoFeedback.z -0.594 ASRS_B.z:CongruencyIncongruent.z 0.000 Diagnosis.z:FeedbackCondNoFeedback.z -0.143 Diagnosis.z:CongruencyIncongruent.z 0.000 COHS.z:FeedbackCondNoFeedback.z 0.149 COHS.z:CongruencyIncongruent.z 0.000 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_A.:C Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z 0.000 ASRS_B.z:CongruencyIncongruent.z -0.594 Diagnosis.z:FeedbackCondNoFeedback.z 0.000 Diagnosis.z:CongruencyIncongruent.z -0.143 COHS.z:FeedbackCondNoFeedback.z 0.000 COHS.z:CongruencyIncongruent.z 0.149 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_B.z:FCNF. Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z ASRS_B.z:CongruencyIncongruent.z 0.000 Diagnosis.z:FeedbackCondNoFeedback.z 0.186 Diagnosis.z:CongruencyIncongruent.z 0.000 COHS.z:FeedbackCondNoFeedback.z -0.202 COHS.z:CongruencyIncongruent.z 0.000 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_B.:C Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z ASRS_B.z:CongruencyIncongruent.z Diagnosis.z:FeedbackCondNoFeedback.z 0.000 Diagnosis.z:CongruencyIncongruent.z 0.186 COHS.z:FeedbackCondNoFeedback.z 0.000 COHS.z:CongruencyIncongruent.z -0.202 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 Dg.:FCNF. D.:CI. Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z ASRS_B.z:CongruencyIncongruent.z Diagnosis.z:FeedbackCondNoFeedback.z Diagnosis.z:CongruencyIncongruent.z 0.000 COHS.z:FeedbackCondNoFeedback.z 0.054 0.000 COHS.z:CongruencyIncongruent.z 0.000 0.054 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 0.000 COHS.z:FCNF. Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z ASRS_B.z:CongruencyIncongruent.z Diagnosis.z:FeedbackCondNoFeedback.z Diagnosis.z:CongruencyIncongruent.z COHS.z:FeedbackCondNoFeedback.z COHS.z:CongruencyIncongruent.z 0.000 ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 COHS.:C Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z ASRS_B.z:CongruencyIncongruent.z Diagnosis.z:FeedbackCondNoFeedback.z Diagnosis.z:CongruencyIncongruent.z COHS.z:FeedbackCondNoFeedback.z COHS.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.000 ASRS_A.:FCNF.: Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z ASRS_B.z:CongruencyIncongruent.z Diagnosis.z:FeedbackCondNoFeedback.z Diagnosis.z:CongruencyIncongruent.z COHS.z:FeedbackCondNoFeedback.z COHS.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z -0.594 Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z -0.143 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.149 ASRS_B.:FCNF.: Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z ASRS_B.z:CongruencyIncongruent.z Diagnosis.z:FeedbackCondNoFeedback.z Diagnosis.z:CongruencyIncongruent.z COHS.z:FeedbackCondNoFeedback.z COHS.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.186 COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z -0.202 D.:FCNF.: Gender.z Cong_Order.z Drive.z ASRS_A.z ASRS_B.z Diagnosis.z COHS.z FeedbackCondNoFeedback.z CongruencyIncongruent.z FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z ASRS_A.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z ASRS_B.z:CongruencyIncongruent.z Diagnosis.z:FeedbackCondNoFeedback.z Diagnosis.z:CongruencyIncongruent.z COHS.z:FeedbackCondNoFeedback.z COHS.z:CongruencyIncongruent.z ASRS_A.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z ASRS_B.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z Diagnosis.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z COHS.z:FeedbackCondNoFeedback.z:CongruencyIncongruent.z 0.054 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -2.43316782 -0.59970244 0.06578658 0.62969155 2.41758883 Number of Observations: 416 Number of Groups: 104
#Extract the R^2 value of each model
r.squaredGLMM(dprime_fb_model1_r)
r.squaredGLMM(dprime_fb_model2_r)
r.squaredGLMM(dprime_fb_model3_r)
r.squaredGLMM(dprime_fb_model4_r)
R2m | R2c |
---|---|
0.02250472 | 0.533829 |
R2m | R2c |
---|---|
0.04098239 | 0.5338497 |
R2m | R2c |
---|---|
0.07733334 | 0.5822466 |
R2m | R2c |
---|---|
0.08681478 | 0.59487 |
#Subtract from each other to derive delta R^2. First will be 2-1, next 3-2.
r.squaredGLMM(dprime_fb_model2_r) - r.squaredGLMM(dprime_fb_model1_r)
r.squaredGLMM(dprime_fb_model3_r) - r.squaredGLMM(dprime_fb_model2_r)
r.squaredGLMM(dprime_fb_model4_r) - r.squaredGLMM(dprime_fb_model3_r)
R2m | R2c |
---|---|
0.01847766 | 2.070854e-05 |
R2m | R2c |
---|---|
0.03635095 | 0.04839692 |
R2m | R2c |
---|---|
0.009481447 | 0.01262341 |
#Compare the models to each other to extract log likelihood ratio Chi^2 values and the associated p-values.
#Df is however many new variables are added to next model.
anova(dprime_fb_model1_r, dprime_fb_model2_r, dprime_fb_model3_r, dprime_fb_model4_r)
call | Model | df | AIC | BIC | logLik | Test | L.Ratio | p-value | |
---|---|---|---|---|---|---|---|---|---|
dprime_fb_model1_r | lme.formula(fixed = dprime ~ Gender + Cong_Order + Drive, data = subset(df_dprime_long_mm, StimulusType == "Familiar"), random = ~1 | Subject, method = "ML") | 1 | 6 | -114.6162 | -90.43208 | 63.30810 | NA | NA | |
dprime_fb_model2_r | lme.formula(fixed = dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS, data = subset(df_dprime_long_mm, StimulusType == "Familiar"), random = ~1 | Subject, method = "ML") | 2 | 10 | -109.7155 | -69.40866 | 64.85775 | 1 vs 2 | 3.099313 | 5.413453e-01 |
dprime_fb_model3_r | lme.formula(fixed = dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + FeedbackCond + Congruency + FeedbackCond * Congruency, data = subset(df_dprime_long_mm, StimulusType == "Familiar"), random = ~1 | Subject, method = "ML") | 3 | 13 | -137.8887 | -85.48980 | 81.94435 | 2 vs 3 | 34.173195 | 1.821212e-07 |
dprime_fb_model4_r | lme.formula(fixed = dprime ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + FeedbackCond + Congruency + FeedbackCond * Congruency + ASRS_A * FeedbackCond * Congruency + ASRS_B * FeedbackCond * Congruency + Diagnosis * FeedbackCond * Congruency + COHS * FeedbackCond * Congruency, data = subset(df_dprime_long_mm, StimulusType == "Familiar"), random = ~1 | Subject, method = "ML") | 4 | 25 | -123.4548 | -22.68766 | 86.72739 | 3 vs 4 | 9.566084 | 6.539687e-01 |