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)
#Read csv with NoGo information (Days 1 and 2)
myNoGoData <- read.csv('Exp8_NoGo_Full.csv')
myNoGoDay1Data <- subset(myNoGoData, FeedbackCond=="NoFeedback")
#Use ML instead of REML becuase we're concerned with comparing fixed effects between models.
nogo_nofb_model1_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive, random=~1|Subject, method="ML", data=myNoGoDay1Data)
nogo_nofb_model2_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS, random=~1|Subject, method="ML", data=myNoGoDay1Data)
nogo_nofb_model3_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + StimulusType, random=~1|Subject, method="ML", data=myNoGoDay1Data)
nogo_nofb_model4_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + StimulusType + ASRS_A*StimulusType + ASRS_B*StimulusType + Diagnosis*StimulusType + COHS*StimulusType, random=~1|Subject, method="ML", data=myNoGoDay1Data)
#Check for outliers, beyond -3.3<x<3.3. No output means no outliers.
which(abs(residuals(nogo_nofb_model1_r, type="normalized")) > 3.3)
which(abs(residuals(nogo_nofb_model2_r, type="normalized")) > 3.3)
which(abs(residuals(nogo_nofb_model3_r, type="normalized")) > 3.3)
which(abs(residuals(nogo_nofb_model4_r, type="normalized")) > 3.3)
#Diagnostics. plot() checks for homoscedasticity violation, qqplot() checks for normality, vif() checks for multicollinearity
qqnorm(resid(nogo_nofb_model1_r))
qqnorm(resid(nogo_nofb_model2_r))
qqnorm(resid(nogo_nofb_model3_r))
qqnorm(resid(nogo_nofb_model4_r))
plot(nogo_nofb_model1_r)
plot(nogo_nofb_model2_r)
plot(nogo_nofb_model3_r)
plot(nogo_nofb_model4_r)
vif(nogo_nofb_model1_r)
vif(nogo_nofb_model2_r)
vif(nogo_nofb_model3_r)
vif(nogo_nofb_model4_r)
#Use beta from reghelper, otherwise beta coefs won't be standardized
beta(nogo_nofb_model1_r)
beta(nogo_nofb_model2_r)
beta(nogo_nofb_model3_r)
beta(nogo_nofb_model4_r)
if(!require(devtools)) install.packages("devtools")
devtools::install_github("aloy/lmeresampler")
packageVersion("lmeresampler")
install.packages("bootstrap")
library(bootstrap)
library(lmeresampler)
boot_nogo_nofb_model1_r<-bootstrap(model=nogo_nofb_model1_r,
fn=fixef,type="parametric",
B=1000)
boot_nogo_nofb_model2_r<-bootstrap(model=nogo_nofb_model2_r,
fn=fixef,type="parametric",
B=1000)
boot_nogo_nofb_model3_r<-bootstrap(model=nogo_nofb_model3_r,
fn=fixef,type="parametric",
B=1000)
boot_nogo_nofb_model4_r<-bootstrap(model=nogo_nofb_model4_r,
fn=fixef,type="parametric",
B=1000)
confint(boot_nogo_nofb_model1_r, level=0.95)
confint(boot_nogo_nofb_model2_r, level=0.95)
confint(boot_nogo_nofb_model3_r, level=0.95)
confint(boot_nogo_nofb_model4_r, level=0.95)
summary(nogo_nofb_model1_r)
#Extract the R^2 value of each model
r.squaredGLMM(nogo_nofb_model1_r)
r.squaredGLMM(nogo_nofb_model2_r)
r.squaredGLMM(nogo_nofb_model3_r)
r.squaredGLMM(nogo_nofb_model4_r)
#Subtract from each other to derive delta R^2. First will be 2-1, next 3-2.
r.squaredGLMM(nogo_nofb_model2_r) - r.squaredGLMM(nogo_nofb_model1_r)
r.squaredGLMM(nogo_nofb_model3_r) - r.squaredGLMM(nogo_nofb_model2_r)
r.squaredGLMM(nogo_nofb_model4_r) - r.squaredGLMM(nogo_nofb_model3_r)
#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(nogo_nofb_model1_r, nogo_nofb_model2_r, nogo_nofb_model3_r, nogo_nofb_model4_r)
myNoGoDay2Data <- subset(myNoGoData, StimulusType=="Familiar")
nogo_fb_model1_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive, random=~1|Subject, method="ML", data=myNoGoDay2Data)
nogo_fb_model2_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS, random=~1|Subject, method="ML", data=myNoGoDay2Data)
nogo_fb_model3_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + FeedbackCond, random=~1|Subject, method="ML", data=myNoGoDay2Data)
nogo_fb_model4_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + FeedbackCond + ASRS_A*FeedbackCond + ASRS_B*FeedbackCond + Diagnosis*FeedbackCond + COHS*FeedbackCond, random=~1|Subject, method="ML", data=myNoGoDay2Data)
#Check for outliers, beyond -3.3<x<3.3. No output means no outliers.
which(abs(residuals(nogo_fb_model1_r, type="normalized")) > 3.3)
which(abs(residuals(nogo_fb_model2_r, type="normalized")) > 3.3)
which(abs(residuals(nogo_fb_model3_r, type="normalized")) > 3.3)
which(abs(residuals(nogo_fb_model4_r, type="normalized")) > 3.3)
#Diagnostics. plot() checks for homoscedasticity violation, qqplot() checks for normality, vif() checks for multicollinearity
qqnorm(resid(nogo_fb_model1_r))
qqnorm(resid(nogo_fb_model2_r))
qqnorm(resid(nogo_fb_model3_r))
qqnorm(resid(nogo_fb_model4_r))
plot(nogo_fb_model1_r)
plot(nogo_fb_model2_r)
plot(nogo_fb_model3_r)
plot(nogo_fb_model4_r)
vif(nogo_fb_model1_r)
vif(nogo_fb_model2_r)
vif(nogo_fb_model3_r)
vif(nogo_fb_model4_r)
beta(nogo_fb_model1_r)
beta(nogo_fb_model2_r)
beta(nogo_fb_model3_r)
beta(nogo_fb_model4_r)
#Extract the R^2 value of each model
r.squaredGLMM(nogo_fb_model1_r)
r.squaredGLMM(nogo_fb_model2_r)
r.squaredGLMM(nogo_fb_model3_r)
r.squaredGLMM(nogo_fb_model4_r)
#Subtract from each other to derive delta R^2. First will be 2-1, next 3-2.
r.squaredGLMM(nogo_fb_model2_r) - r.squaredGLMM(nogo_fb_model1_r)
r.squaredGLMM(nogo_fb_model3_r) - r.squaredGLMM(nogo_fb_model2_r)
r.squaredGLMM(nogo_fb_model4_r) - r.squaredGLMM(nogo_fb_model3_r)
#Compare models
anova(nogo_fb_model1_r, nogo_fb_model2_r, nogo_fb_model3_r, nogo_fb_model4_r)
myGoData <- read.csv('Exp8_Go_Full.csv')
myGoDay1Data <- subset(myGoData, FeedbackCond=="NoFeedback")
go_nofb_model1_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive, random=~1|Subject, method="ML", data=myGoDay1Data)
go_nofb_model2_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS, random=~1|Subject, method="ML", data=myGoDay1Data)
go_nofb_model3_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + StimulusType, random=~1|Subject, method="ML", data=myGoDay1Data)
go_nofb_model4_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + StimulusType + ASRS_A*StimulusType + ASRS_B*StimulusType + Diagnosis*StimulusType + COHS*StimulusType, random=~1|Subject, method="ML", data=myGoDay1Data)
#Check for outliers, beyond -3.3<x<3.3. No output means no outliers.
which(abs(residuals(go_nofb_model1_r, type="normalized")) > 3.3)
which(abs(residuals(go_nofb_model2_r, type="normalized")) > 3.3)
which(abs(residuals(go_nofb_model3_r, type="normalized")) > 3.3)
which(abs(residuals(go_nofb_model4_r, type="normalized")) > 3.3)
#Rerun model without outliers for pub tables
myGoDay1Data_r_outrem <- subset(myGoDay1Data, Subject!=79 & Subject!=106)
go_nofb_model1_r_outrem <- lme(Acc_Diff ~ Gender + Cong_Order + Drive, random=~1|Subject, method="ML", data=myGoDay1Data_r_outrem)
go_nofb_model2_r_outrem <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS, random=~1|Subject, method="ML", data=myGoDay1Data_r_outrem)
go_nofb_model3_r_outrem <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + StimulusType, random=~1|Subject, method="ML", data=myGoDay1Data_r_outrem)
go_nofb_model4_r_outrem <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + StimulusType + ASRS_A*StimulusType + ASRS_B*StimulusType + Diagnosis*StimulusType + COHS*StimulusType, random=~1|Subject, method="ML", data=myGoDay1Data_r_outrem)
#Diagnostics. plot() checks for homoscedasticity violation, qqplot() checks for normality, vif() checks for multicollinearity
qqnorm(resid(go_nofb_model1_r_outrem))
qqnorm(resid(go_nofb_model2_r_outrem))
qqnorm(resid(go_nofb_model3_r_outrem))
qqnorm(resid(go_nofb_model4_r_outrem))
plot(go_nofb_model1_r_outrem)
plot(go_nofb_model2_r_outrem)
plot(go_nofb_model3_r_outrem)
plot(go_nofb_model4_r_outrem)
vif(go_nofb_model1_r_outrem)
vif(go_nofb_model2_r_outrem)
vif(go_nofb_model3_r_outrem)
vif(go_nofb_model4_r_outrem)
beta(go_nofb_model1_r_outrem)
beta(go_nofb_model2_r_outrem)
beta(go_nofb_model3_r_outrem)
beta(go_nofb_model4_r_outrem)
#Extract the R^2 value of each model
r.squaredGLMM(go_nofb_model1_r_outrem)
r.squaredGLMM(go_nofb_model2_r_outrem)
r.squaredGLMM(go_nofb_model3_r_outrem)
r.squaredGLMM(go_nofb_model4_r_outrem)
#Subtract from each other to derive delta R^2. First will be 2-1, next 3-2.
r.squaredGLMM(go_nofb_model2_r_outrem) - r.squaredGLMM(go_nofb_model1_r_outrem)
r.squaredGLMM(go_nofb_model3_r_outrem) - r.squaredGLMM(go_nofb_model2_r_outrem)
r.squaredGLMM(go_nofb_model4_r_outrem) - r.squaredGLMM(go_nofb_model3_r_outrem)
#Compare models
anova(go_nofb_model1_r_outrem, go_nofb_model2_r_outrem, go_nofb_model3_r_outrem, go_nofb_model4_r_outrem)
#Post-hoc t-tests. Compare Go Accuracy across phases (Congruency) in both Conditions
#Subject!=79 & Subject!=106 (outliers)
print("Familiar Go t-test")
myGoAccData <- read.csv("gostatssheet_full.csv")
myDay1CompareFam_outrem <- subset(myGoAccData, StimulusType=="Familiar" & FeedbackCond=="NoFeedback" & Subject!=79 & Subject!=106)
pairedSamplesTTest(formula=Accuracy~Congruency, data=myDay1CompareFam_outrem, id="Subject")
print("Novel Go t-test")
myDay1CompareNov_outrem <- subset(myGoAccData, StimulusType=="Novel" & Subject!=79 & Subject!=106)
pairedSamplesTTest(formula=Accuracy~Congruency, data=myDay1CompareNov_outrem, id="Subject")
myGoDay2Data <- subset(myGoData, StimulusType=="Familiar")
go_fb_model1_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive, random=~1|Subject, method="ML", data=myGoDay2Data)
go_fb_model2_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS, random=~1|Subject, method="ML", data=myGoDay2Data)
go_fb_model3_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + FeedbackCond, random=~1|Subject, method="ML", data=myGoDay2Data)
go_fb_model4_r <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + FeedbackCond + ASRS_A*FeedbackCond + ASRS_B*FeedbackCond + Diagnosis*FeedbackCond + COHS*FeedbackCond, random=~1|Subject, method="ML", data=myGoDay2Data)
#Check for outliers
which(abs(residuals(go_fb_model1_r, type="normalized"))>3.3)
which(abs(residuals(go_fb_model2_r, type="normalized"))>3.3)
which(abs(residuals(go_fb_model3_r, type="normalized"))>3.3)
which(abs(residuals(go_fb_model4_r, type="normalized"))>3.3)
#run with outliers excluded
myGoDay2Data_r_outrem <- subset(myGoDay2Data, Subject!=72 & Subject!=79)
go_fb_model1_r_outrem <- lme(Acc_Diff ~ Gender + Cong_Order + Drive, random=~1|Subject, method="ML", data=myGoDay2Data_r_outrem)
go_fb_model2_r_outrem <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS, random=~1|Subject, method="ML", data=myGoDay2Data_r_outrem)
go_fb_model3_r_outrem <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + FeedbackCond, random=~1|Subject, method="ML", data=myGoDay2Data_r_outrem)
go_fb_model4_r_outrem <- lme(Acc_Diff ~ Gender + Cong_Order + Drive + ASRS_A + ASRS_B + Diagnosis + COHS + FeedbackCond + ASRS_A*FeedbackCond + ASRS_B*FeedbackCond + Diagnosis*FeedbackCond + COHS*FeedbackCond, random=~1|Subject, method="ML", data=myGoDay2Data_r_outrem)
#Diagnostics. plot() checks for homoscedasticity violation, qqplot() checks for normality, vif() checks for multicollinearity
qqnorm(resid(go_fb_model1_r_outrem))
qqnorm(resid(go_fb_model2_r_outrem))
qqnorm(resid(go_fb_model3_r_outrem))
qqnorm(resid(go_fb_model4_r_outrem))
plot(go_fb_model1_r_outrem)
plot(go_fb_model2_r_outrem)
plot(go_fb_model3_r_outrem)
plot(go_fb_model4_r_outrem)
vif(go_nofb_model1_r_outrem)
vif(go_nofb_model2_r_outrem)
vif(go_nofb_model3_r_outrem)
vif(go_nofb_model4_r_outrem)
beta(go_fb_model1_r_outrem)
beta(go_fb_model2_r_outrem)
beta(go_fb_model3_r_outrem)
beta(go_fb_model4_r_outrem)
#Extract the R^2 value of each model
r.squaredGLMM(go_fb_model1_r_outrem)
r.squaredGLMM(go_fb_model2_r_outrem)
r.squaredGLMM(go_fb_model3_r_outrem)
r.squaredGLMM(go_fb_model4_r_outrem)
#Subtract from each other to derive delta R^2. First will be 2-1, next 3-2.
r.squaredGLMM(go_fb_model2_r_outrem) - r.squaredGLMM(go_fb_model1_r_outrem)
r.squaredGLMM(go_fb_model3_r_outrem) - r.squaredGLMM(go_fb_model2_r_outrem)
r.squaredGLMM(go_fb_model4_r_outrem) - r.squaredGLMM(go_fb_model3_r_outrem)
#Compare models
anova(go_fb_model1_r_outrem, go_fb_model2_r_outrem, go_fb_model3_r_outrem, go_fb_model4_r_outrem)
#install.packages("corrplot")
library(corrplot)
#Transfer the individual difference columns from the RT dataframe with match()
myGoRTLongData <- read.csv("gostatssheetRT_full.csv")
myGoRTData <- read.csv('Exp8_GoRT_Full.csv')
myGoRTDay1Data <- subset(myGoRTData, FeedbackCond=="NoFeedback")
myDay1FamCongData <- subset(myGoRTLongData, StimulusType=="Familiar" & FeedbackCond=="NoFeedback" & Congruency=="Congruent")
myDay1FamCongData$ASRS_A = myGoRTDay1Data[match(myDay1FamCongData$Subject, myGoRTDay1Data$Subject),"ASRS_A"]
myDay1FamCongData$ASRS_B = myGoRTDay1Data[match(myDay1FamCongData$Subject, myGoRTDay1Data$Subject),"ASRS_B"]
myDay1FamCongData$ASRS_Total = myGoRTDay1Data[match(myDay1FamCongData$Subject, myGoRTDay1Data$Subject),"ASRS_Total"]
myDay1FamCongData$COHS = myGoRTDay1Data[match(myDay1FamCongData$Subject, myGoRTDay1Data$Subject),"COHS"]
head(myDay1FamCongData)
#Generate correlation matrix with RT and survey measures
day1RTcorrcols <- myDay1FamCongData[,6:length(myDay1FamCongData)]
#day1RTcorrcols
#myDay1FamCongData
#corr.test automatically adjusts for multiple comparisons using Holm's method, unless you adjust="none"
corr.test(day1RTcorrcols$RT, day1RTcorrcols[,c("ASRS_A", "ASRS_B", "COHS")])[[4]]
#plot(day1RTcorrcols$ASRS_B, day1RTcorrcols$RT, main="Hyperactivity and Go RT correlation",
# xlab="ASRS_Hyperactivity ", ylab="Go RT in ms", pch=19)
#png(filename="RT_ASRSB_scatterplot.png", res=100)
ggplot2.scatterplot(data=day1RTcorrcols, xName='ASRS_B',yName='RT', backgroundColor="white", axisLine=c(1, "solid", "black"),
addRegLine=TRUE, regLineColor="darkgreen", regLineSize=1, linetype="dashed", removePanelGrid=TRUE, removePanelBorder=TRUE, addConfidenceInterval=TRUE, xtitle="ASRS_Hyperactivity",
ytitle="RT in ms") + ggtitle("Higher hyperactivity scores predict quicker Green-Go RT") +
theme(plot.title = element_text(hjust = 0, size=rel(1.55))) + theme(axis.title.x = element_text(angle=0, hjust=0.5))
#dev.off()
#Transfer the individual difference columns from the RT dataframe with match()
myDay1NovCongData <- subset(myGoRTLongData, StimulusType=="Novel" & Congruency=="Incongruent")
myDay1NovCongData$ASRS_A = myGoRTDay1Data[match(myDay1NovCongData$Subject, myGoRTDay1Data$Subject),"ASRS_A"]
myDay1NovCongData$ASRS_B = myGoRTDay1Data[match(myDay1NovCongData$Subject, myGoRTDay1Data$Subject),"ASRS_B"]
myDay1NovCongData$ASRS_Total = myGoRTDay1Data[match(myDay1NovCongData$Subject, myGoRTDay1Data$Subject),"ASRS_Total"]
myDay1NovCongData$COHS = myGoRTDay1Data[match(myDay1NovCongData$Subject, myGoRTDay1Data$Subject),"COHS"]
head(myDay1NovCongData)
panel.cor <- function(x, y, digits=2, cex.cor)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- (cor(x, y))
p <- round(corr.test(x,y)[[4]],3)
txt <- format(c(r, 0.123456789), digits=digits)[1]
# test <- correlate(x,y, p.adjust.method="holm")
p <- ifelse(p<0.001,"p<0.001",paste("p=",p))
text(0.5, 0.35, paste("r = ", txt), cex = 1.5)
text(.5, .55, p, cex = 1.5)
}
panel.hist <- function(x, ...)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col="gray", ...)
}
colors <- c("black", "black", "red", "black")
labelnames=c("RT", "Inattentiveness", "Hyperactivity", "COHS")
pairs(~RT+ASRS_A+ASRS_B+COHS, labels=labelnames, lower.panel=panel.cor, diag.panel=panel.hist, data=day1RTcorrcols,
main="Individual differences in Go RT")
boot_nogo_nofb_model1_r<-bootstrap(model=nogo_nofb_model1_r, fn=fixef,type="parametric", B=1000)
boot_nogo_nofb_model2_r<-bootstrap(model=nogo_nofb_model2_r, fn=fixef,type="parametric", B=1000)
boot_nogo_nofb_model3_r<-bootstrap(model=nogo_nofb_model3_r, fn=fixef,type="parametric", B=1000)
boot_nogo_nofb_model4_r<-bootstrap(model=nogo_nofb_model4_r, fn=fixef,type="parametric", B=1000)
confint(boot_nogo_nofb_model1_r, level=0.95)
confint(boot_nogo_nofb_model2_r, level=0.95)
confint(boot_nogo_nofb_model3_r, level=0.95)
confint(boot_nogo_nofb_model4_r, level=0.95)
summary(nogo_nofb_model1_r)$coefficients[1]
confint(boot_nogo_nofb_model1_r, level=0.95)
summary(nogo_nofb_model2_r)$coefficients[1]
confint(boot_nogo_nofb_model2_r, level=0.95)
summary(nogo_nofb_model3_r)$coefficients[1]
confint(boot_nogo_nofb_model3_r, level=0.95)
summary(nogo_nofb_model4_r)$coefficients[1]
confint(boot_nogo_nofb_model4_r, level=0.95)
boot_nogo_fb_model1_r<-bootstrap(model=nogo_fb_model1_r, fn=fixef,type="parametric", B=1000)
boot_nogo_fb_model2_r<-bootstrap(model=nogo_fb_model2_r, fn=fixef,type="parametric", B=1000)
boot_nogo_fb_model3_r<-bootstrap(model=nogo_fb_model3_r, fn=fixef,type="parametric", B=1000)
boot_nogo_fb_model4_r<-bootstrap(model=nogo_fb_model4_r, fn=fixef,type="parametric", B=1000)
summary(nogo_fb_model1_r)$coefficients[1]
confint(boot_nogo_fb_model1_r, level=0.95)
summary(nogo_fb_model2_r)$coefficients[1]
confint(boot_nogo_fb_model2_r, level=0.95)
summary(nogo_fb_model3_r)$coefficients[1]
confint(boot_nogo_fb_model3_r, level=0.95)
summary(nogo_fb_model4_r)$coefficients[1]
confint(boot_nogo_fb_model4_r, level=0.95)
boot_go_nofb_model1_r<-bootstrap(model=go_nofb_model1_r, fn=fixef,type="parametric", B=1000)
boot_go_nofb_model2_r<-bootstrap(model=go_nofb_model2_r, fn=fixef,type="parametric", B=1000)
boot_go_nofb_model3_r<-bootstrap(model=go_nofb_model3_r, fn=fixef,type="parametric", B=1000)
boot_go_nofb_model4_r<-bootstrap(model=go_nofb_model4_r, fn=fixef,type="parametric", B=1000)
summary(go_nofb_model1_r)$coefficients[1]
confint(boot_go_nofb_model1_r, level=0.95)
summary(go_nofb_model2_r)$coefficients[1]
confint(boot_go_nofb_model2_r, level=0.95)
summary(go_nofb_model3_r)$coefficients[1]
confint(boot_go_nofb_model3_r, level=0.95)
summary(go_nofb_model4_r)$coefficients[1]
confint(boot_go_nofb_model4_r, level=0.95)
boot_go_fb_model1_r<-bootstrap(model=go_fb_model1_r, fn=fixef,type="parametric", B=1000)
boot_go_fb_model2_r<-bootstrap(model=go_fb_model2_r, fn=fixef,type="parametric", B=1000)
boot_go_fb_model3_r<-bootstrap(model=go_fb_model3_r, fn=fixef,type="parametric", B=1000)
boot_go_fb_model4_r<-bootstrap(model=go_fb_model4_r, fn=fixef,type="parametric", B=1000)
summary(go_fb_model1_r)$coefficients[1]
confint(boot_go_fb_model1_r, level=0.95)
summary(go_fb_model2_r)$coefficients[1]
confint(boot_go_fb_model2_r, level=0.95)
summary(go_fb_model3_r)$coefficients[1]
confint(boot_go_fb_model3_r, level=0.95)
summary(go_fb_model4_r)$coefficients[1]
confint(boot_go_fb_model4_r, level=0.95)
#install.packages("metafor")
#library(metafor)
options(scipen=999)
"print"(confint(boot_go_fb_model4_r, level=0.95), digits=2)
confint(boot_go_fb_model4_r, level=0.95)
#round(summary(go_fb_model4_r)$coefficients[1], 2)
#round(confint(boot_go_fb_model4_r, level=0.95), 2)