?theme
p <- ggCaterpillar(ranef(fit, postVar=TRUE))
p
p + theme_bw()
ggCaterpillar <- function(re, QQ=TRUE, likeDotplot=TRUE) {
## http://stackoverflow.com/questions/13847936/in-r-plotting-random-effects-from-lmer-lme4-package-using-qqmath-or-dotplot
## re = object of class ranef.mer
require(ggplot2)
f <- function(x) {
pv   <- attr(x, "postVar")
cols <- 1:(dim(pv)[1])
se   <- unlist(lapply(cols, function(i) sqrt(pv[i, i, ])))
ord  <- unlist(lapply(x, order)) + rep((0:(ncol(x) - 1)) * nrow(x), each=nrow(x))
pDf  <- data.frame(y=unlist(x)[ord],
ci=1.96*se[ord],
nQQ=rep(qnorm(ppoints(nrow(x))), ncol(x)),
ID=factor(rep(rownames(x), ncol(x))[ord], levels=rownames(x)[ord]),
ind=gl(ncol(x), nrow(x), labels=names(x)))
if(QQ) {  ## normal QQ-plot
p <- ggplot(pDf, aes(nQQ, y))
p <- p + facet_wrap(~ ind, scales="free")
p <- p + xlab("Standard normal quantiles") + ylab("Random effect quantiles")
} else {  ## caterpillar dotplot
p <- ggplot(pDf, aes(ID, y)) + coord_flip()
if(likeDotplot) {  ## imitate dotplot() -> same scales for random effects
p <- p + facet_wrap(~ ind)
} else {           ## different scales for random effects
p <- p + facet_grid(ind ~ ., scales="free_y")
}
p <- p + xlab("Levels") + ylab("Random effects")
}
p <- p + theme_bw(legend.position="none")
p <- p + geom_hline(yintercept=0)
p <- p + geom_errorbar(aes(ymin=y-ci, ymax=y+ci), width=0, colour="black")
p <- p + geom_point(aes(size=1.2), colour="blue")
return(p)
}
lapply(re, f)
}
# Example
# fit <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
# ggCaterpillar(ranef(fit, postVar=TRUE))  ## using ggplot2
# qqmath(ranef(fit, postVar=TRUE))         ## for comparison
ggCaterpillar(ranef(fit, postVar=TRUE))
ggCaterpillar <- function(re, QQ=TRUE, likeDotplot=TRUE) {
## http://stackoverflow.com/questions/13847936/in-r-plotting-random-effects-from-lmer-lme4-package-using-qqmath-or-dotplot
## re = object of class ranef.mer
require(ggplot2)
f <- function(x) {
pv   <- attr(x, "postVar")
cols <- 1:(dim(pv)[1])
se   <- unlist(lapply(cols, function(i) sqrt(pv[i, i, ])))
ord  <- unlist(lapply(x, order)) + rep((0:(ncol(x) - 1)) * nrow(x), each=nrow(x))
pDf  <- data.frame(y=unlist(x)[ord],
ci=1.96*se[ord],
nQQ=rep(qnorm(ppoints(nrow(x))), ncol(x)),
ID=factor(rep(rownames(x), ncol(x))[ord], levels=rownames(x)[ord]),
ind=gl(ncol(x), nrow(x), labels=names(x)))
if(QQ) {  ## normal QQ-plot
p <- ggplot(pDf, aes(nQQ, y))
p <- p + facet_wrap(~ ind, scales="free")
p <- p + xlab("Standard normal quantiles") + ylab("Random effect quantiles")
} else {  ## caterpillar dotplot
p <- ggplot(pDf, aes(ID, y)) + coord_flip()
if(likeDotplot) {  ## imitate dotplot() -> same scales for random effects
p <- p + facet_wrap(~ ind)
} else {           ## different scales for random effects
p <- p + facet_grid(ind ~ ., scales="free_y")
}
p <- p + xlab("Levels") + ylab("Random effects")
}
p <- p + theme(legend.position="none")
p <- p + geom_hline(yintercept=0)
p <- p + geom_errorbar(aes(ymin=y-ci, ymax=y+ci), width=0, colour="black")
p <- p + geom_point(aes(size=1.2), colour="blue")
p <- p + theme_bw()
return(p)
}
lapply(re, f)
}
# Example
# fit <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
# ggCaterpillar(ranef(fit, postVar=TRUE))  ## using ggplot2
# qqmath(ranef(fit, postVar=TRUE))         ## for comparison
ggCaterpillar(ranef(fit, postVar=TRUE))
ggCaterpillar <- function(re, QQ=TRUE, likeDotplot=TRUE) {
## http://stackoverflow.com/questions/13847936/in-r-plotting-random-effects-from-lmer-lme4-package-using-qqmath-or-dotplot
## re = object of class ranef.mer
require(ggplot2)
f <- function(x) {
pv   <- attr(x, "postVar")
cols <- 1:(dim(pv)[1])
se   <- unlist(lapply(cols, function(i) sqrt(pv[i, i, ])))
ord  <- unlist(lapply(x, order)) + rep((0:(ncol(x) - 1)) * nrow(x), each=nrow(x))
pDf  <- data.frame(y=unlist(x)[ord],
ci=1.96*se[ord],
nQQ=rep(qnorm(ppoints(nrow(x))), ncol(x)),
ID=factor(rep(rownames(x), ncol(x))[ord], levels=rownames(x)[ord]),
ind=gl(ncol(x), nrow(x), labels=names(x)))
if(QQ) {  ## normal QQ-plot
p <- ggplot(pDf, aes(nQQ, y))
p <- p + facet_wrap(~ ind, scales="free")
p <- p + xlab("Standard normal quantiles") + ylab("Random effect quantiles")
} else {  ## caterpillar dotplot
p <- ggplot(pDf, aes(ID, y)) + coord_flip()
if(likeDotplot) {  ## imitate dotplot() -> same scales for random effects
p <- p + facet_wrap(~ ind)
} else {           ## different scales for random effects
p <- p + facet_grid(ind ~ ., scales="free_y")
}
p <- p + xlab("Levels") + ylab("Random effects")
}
p <- p + them_bw + theme(legend.position="none")
p <- p + geom_hline(yintercept=0)
p <- p + geom_errorbar(aes(ymin=y-ci, ymax=y+ci), width=0, colour="black")
p <- p + geom_point(aes(size=1.2), colour="blue")
return(p)
}
lapply(re, f)
}
# Example
# fit <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
# ggCaterpillar(ranef(fit, postVar=TRUE))  ## using ggplot2
# qqmath(ranef(fit, postVar=TRUE))         ## for comparison
ggCaterpillar(ranef(fit, postVar=TRUE))
ggCaterpillar <- function(re, QQ=TRUE, likeDotplot=TRUE) {
## http://stackoverflow.com/questions/13847936/in-r-plotting-random-effects-from-lmer-lme4-package-using-qqmath-or-dotplot
## re = object of class ranef.mer
require(ggplot2)
f <- function(x) {
pv   <- attr(x, "postVar")
cols <- 1:(dim(pv)[1])
se   <- unlist(lapply(cols, function(i) sqrt(pv[i, i, ])))
ord  <- unlist(lapply(x, order)) + rep((0:(ncol(x) - 1)) * nrow(x), each=nrow(x))
pDf  <- data.frame(y=unlist(x)[ord],
ci=1.96*se[ord],
nQQ=rep(qnorm(ppoints(nrow(x))), ncol(x)),
ID=factor(rep(rownames(x), ncol(x))[ord], levels=rownames(x)[ord]),
ind=gl(ncol(x), nrow(x), labels=names(x)))
if(QQ) {  ## normal QQ-plot
p <- ggplot(pDf, aes(nQQ, y))
p <- p + facet_wrap(~ ind, scales="free")
p <- p + xlab("Standard normal quantiles") + ylab("Random effect quantiles")
} else {  ## caterpillar dotplot
p <- ggplot(pDf, aes(ID, y)) + coord_flip()
if(likeDotplot) {  ## imitate dotplot() -> same scales for random effects
p <- p + facet_wrap(~ ind)
} else {           ## different scales for random effects
p <- p + facet_grid(ind ~ ., scales="free_y")
}
p <- p + xlab("Levels") + ylab("Random effects")
}
p <- p + theme_bw + theme(legend.position="none")
p <- p + geom_hline(yintercept=0)
p <- p + geom_errorbar(aes(ymin=y-ci, ymax=y+ci), width=0, colour="black")
p <- p + geom_point(aes(size=1.2), colour="blue")
return(p)
}
lapply(re, f)
}
# Example
# fit <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
# ggCaterpillar(ranef(fit, postVar=TRUE))  ## using ggplot2
# qqmath(ranef(fit, postVar=TRUE))         ## for comparison
ggCaterpillar(ranef(fit, postVar=TRUE))
ggCaterpillar <- function(re, QQ=TRUE, likeDotplot=TRUE) {
## http://stackoverflow.com/questions/13847936/in-r-plotting-random-effects-from-lmer-lme4-package-using-qqmath-or-dotplot
## re = object of class ranef.mer
require(ggplot2)
f <- function(x) {
pv   <- attr(x, "postVar")
cols <- 1:(dim(pv)[1])
se   <- unlist(lapply(cols, function(i) sqrt(pv[i, i, ])))
ord  <- unlist(lapply(x, order)) + rep((0:(ncol(x) - 1)) * nrow(x), each=nrow(x))
pDf  <- data.frame(y=unlist(x)[ord],
ci=1.96*se[ord],
nQQ=rep(qnorm(ppoints(nrow(x))), ncol(x)),
ID=factor(rep(rownames(x), ncol(x))[ord], levels=rownames(x)[ord]),
ind=gl(ncol(x), nrow(x), labels=names(x)))
if(QQ) {  ## normal QQ-plot
p <- ggplot(pDf, aes(nQQ, y))
p <- p + facet_wrap(~ ind, scales="free")
p <- p + xlab("Standard normal quantiles") + ylab("Random effect quantiles")
} else {  ## caterpillar dotplot
p <- ggplot(pDf, aes(ID, y)) + coord_flip()
if(likeDotplot) {  ## imitate dotplot() -> same scales for random effects
p <- p + facet_wrap(~ ind)
} else {           ## different scales for random effects
p <- p + facet_grid(ind ~ ., scales="free_y")
}
p <- p + xlab("Levels") + ylab("Random effects")
}
p <- p + theme_bw() + theme(legend.position="none")
p <- p + geom_hline(yintercept=0)
p <- p + geom_errorbar(aes(ymin=y-ci, ymax=y+ci), width=0, colour="black")
p <- p + geom_point(aes(size=1.2), colour="blue")
return(p)
}
lapply(re, f)
}
# Example
# fit <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
# ggCaterpillar(ranef(fit, postVar=TRUE))  ## using ggplot2
# qqmath(ranef(fit, postVar=TRUE))         ## for comparison
ggCaterpillar(ranef(fit, postVar=TRUE))
ggCaterpillar <- function(re, QQ=TRUE, likeDotplot=TRUE) {
## http://stackoverflow.com/questions/13847936/in-r-plotting-random-effects-from-lmer-lme4-package-using-qqmath-or-dotplot
## re = object of class ranef.mer
require(ggplot2)
f <- function(x) {
pv   <- attr(x, "postVar")
cols <- 1:(dim(pv)[1])
se   <- unlist(lapply(cols, function(i) sqrt(pv[i, i, ])))
ord  <- unlist(lapply(x, order)) + rep((0:(ncol(x) - 1)) * nrow(x), each=nrow(x))
pDf  <- data.frame(y=unlist(x)[ord],
ci=1.96*se[ord],
nQQ=rep(qnorm(ppoints(nrow(x))), ncol(x)),
ID=factor(rep(rownames(x), ncol(x))[ord], levels=rownames(x)[ord]),
ind=gl(ncol(x), nrow(x), labels=names(x)))
if(QQ) {  ## normal QQ-plot
p <- ggplot(pDf, aes(nQQ, y))
p <- p + facet_wrap(~ ind, scales="free")
p <- p + xlab("Standard normal quantiles") + ylab("Random effect quantiles")
} else {  ## caterpillar dotplot
p <- ggplot(pDf, aes(ID, y)) + coord_flip()
if(likeDotplot) {  ## imitate dotplot() -> same scales for random effects
p <- p + facet_wrap(~ ind)
} else {           ## different scales for random effects
p <- p + facet_grid(ind ~ ., scales="free_y")
}
p <- p + xlab("Levels") + ylab("Random effects")
}
p <- p + theme_bw() + theme(legend.position="none", aspect=1)
p <- p + geom_hline(yintercept=0)
p <- p + geom_errorbar(aes(ymin=y-ci, ymax=y+ci), width=0, colour="black")
p <- p + geom_point(aes(size=1.2), colour="blue")
return(p)
}
lapply(re, f)
}
# Example
# fit <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
# ggCaterpillar(ranef(fit, postVar=TRUE))  ## using ggplot2
# qqmath(ranef(fit, postVar=TRUE))         ## for comparison
ggCaterpillar(ranef(fit, postVar=TRUE))
library(lme4)
fit <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
library(ggplot2)
ggCaterpillar(ranef(fit, postVar=TRUE))  ## using ggplot2
lapply(names(ranef(fit)),
function(x) cbind(ranef(fit)[[x]], table(model.frame(fit)[[x]])))
library(fortunes)
fortune("impossibility")
install.packages("rJava")
a = 1
a
a < - 1
a < -1
rm(a)
a < - 1
library(gmodels)
?gmodels
?ci
library(lme4)
library(ggplot2)
library(memisc)
rm(list=ls())
source("~/documents/projects/R_functions/remef.v0.6.7.R")
source("~/documents/projects/R_functions/mtable-ext.R")  # requires package memisc
source("~/documents/projects/R_functions/glmm_funs.R")
setwd("~/documents/projects/COOPMasson/MK.JEPLMC.2013/Masson_Kliegl.JEPLMC.2013")
# Combine data
load("Exp1.rda")
d1 <- d
d1$Exp <- 1
d1$id <- factor(paste(d1$id, "1", sep="_"))
load("Exp2.rda")
d2 <- d
names(d2)[c(8, 13)] <- c("l", "L")
d2$Exp <- 2
d2$id <- factor(paste(d2$id, "2", sep="_"))
d <- rbind(d1, d2)
d$Exp <- factor(d$Exp)
contrasts(d$Exp) <- MASS::contr.sdif(2)
rm(list=ls()[2:5])
d$ltt <- factor(d$ltt, labels=list("RelW", "UnrW", "NW"))
ls()
# Combine data
load("Exp1.rda")
d1 <- d
d1$Exp <- 1
d1$id <- factor(paste(d1$id, "1", sep="_"))
load("Exp2.rda")
d2 <- d
names(d2)[c(8, 13)] <- c("l", "L")
d2$Exp <- 2
d2$id <- factor(paste(d2$id, "2", sep="_"))
d <- rbind(d1, d2)
d$Exp <- factor(d$Exp)
contrasts(d$Exp) <- MASS::contr.sdif(2)
ls()
d1$ltt <- factor(d1$ltt, labels=list("RelW", "UnrW", "NW"))
d2$ltt <- factor(d2$ltt, labels=list("RelW", "UnrW", "NW"))
d <- rbind(d1, d2)
d$Exp <- factor(d$Exp)
contrasts(d$Exp) <- MASS::contr.sdif(2)
(Plot.rrt <- ggplot(d, aes(x=trial, y=rrt) ) + xlab("Trial") + facet_grid(. ~ Exp) +
geom_smooth( aes(group = ltt, colour=ltt), method="lm", formula=y~poly(x, 1), size=1) +
scale_colour_manual("N-1 Target", values=c("red", "blue", "black")) +
scale_y_continuous("Response Time  [-1/s]", breaks=seq(from=-1.80, to=-1.50, by=.05)) +
coord_cartesian(ylim=c(-1.80, -1.50) ) ) + theme_bw()
print(m0 <- lmer(rrt ~ 1 + Q*P*F*Exp*poly(trial.c,2) + (1 | id) + (1 | st) + (1|lst), data=d.NW), cor=F)
rm(list=ls())
source("~/documents/projects/R_functions/remef.v0.6.7.R")
source("~/documents/projects/R_functions/mtable-ext.R")  # requires package memisc
source("~/documents/projects/R_functions/glmm_funs.R")
setwd("~/documents/projects/COOPMasson/MK.JEPLMC.2013/Masson_Kliegl.JEPLMC.2013")
# Combine data
load("Exp1.rda")
d1 <- d
d1$Exp <- 1
d1$id <- factor(paste(d1$id, "1", sep="_"))
d1$ltt <- factor(d1$ltt, labels=list("RelW", "UnrW", "NW"))
contrasts(d$ltt) <- MASS::contr.sdif(3)
d1$LP <- ifelse(d1$ltt == "RelW", -2/3, 1/3)
d1$LW <- ifelse(d1$ltt == "UnrNW", 2/3, -1/3)
load("Exp2.rda")
d2 <- d
names(d2)[c(8, 13)] <- c("l", "L")
d2$Exp <- 2
d2$id <- factor(paste(d2$id, "2", sep="_"))
d2$ltt <- factor(d2$ltt, labels=list("RelW", "UnrW", "NW"))
contrasts(d$ltt) <- MASS::contr.sdif(3)
d2$LP <- ifelse(d2$ltt == "RelW", -2/3, 1/3)
d2$LW <- ifelse(d2$ltt == "UnrNW", 2/3, -1/3)
d <- rbind(d1, d2)
d$Exp <- factor(d$Exp)
contrasts(d$Exp) <- MASS::contr.sdif(2)
names(d)
print(m0 <- lmer(rrt ~ 1 + (LP+LW)*Q*P*F*Exp*poly(trial.c,2) + (1 | id) + (1 | st) + (1|lst), data=d.NW), cor=F)
print(m0 <- lmer(rrt ~ 1 + (LP+LW)*Q*P*F*Exp*poly(trial.c,2) + (1 | id) + (1 | st) + (1|lst), data=d), cor=F)
print(m1 <- lmer(rrt ~ 1 + (LP+LW)*(Q+P+F+Exp+trial.c)^2 + (1 | id) + (1 | st) + (1|lst), data=d), cor=F)
print(m0.1 <- lmer(rrt ~ 1 + (LP+LW)*Q*P*F*poly(trial.c,2) + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp==1), cor=F)
d.NW <- subset(d, w=="NW")
(Plot.rrt <- ggplot(d.NW, aes(x=trial, y=rrt) ) + xlab("Trial") + facet_grid(. ~ Exp) +
geom_smooth( aes(group = ltt, colour=ltt), method="lm", formula=y~poly(x, 1), size=1) +
scale_colour_manual("N-1 NW-Target", values=c("black")) +
scale_y_continuous("Response Time  [-1/s]", breaks=seq(from=-1.80, to=-1.50, by=.05)) +
coord_cartesian(ylim=c(-1.80, -1.50) ) ) + theme_bw()
print(m0.1 <- lmer(rrt ~ 1 + (LP+LW)*Q*P*F*poly(trial.c,2) + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1"), cor=F)
head(d)
str(d)
nrow(d)
print(m <- lmer(rrt ~ 1 + Q*P*F*poly(trial.c,2) + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1"), cor=F)
print(m <- lmer(rrt ~ 1 + (LP+LW)*Q*P*F+poly(trial.c,2) + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1"), cor=F)
print(m0.1 <- lmer(rrt ~ 1 + (LP+LW)*Q*P*F + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1"), cor=F)
print(m0.1 <- lmer(rrt ~ 1 + (LP+LW)*Q + P*F + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1"), cor=F)
print(m0.1 <- lmer(rrt ~ 1 + (LP+LW)*P*F + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1"), cor=F)
print(m0.1 <- lmer(rrt ~ 1 + ltt*Q*P*F + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1"), cor=F)
contrasts(d$ltt)
contrasts(d$ltt) <- MASS::contr.sdif(3)
print(m0.1 <- lmer(rrt ~ 1 + ltt*Q*P*F + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1"), cor=F)
print(m0.1 <- lmer(rrt ~ 1 + Q*P*F*ltt + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1"), cor=F)
print(m0.1 <- lmer(rrt ~ 1 + Q*P*F*L*ltt + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1"), cor=F)
print(m0.2 <- lmer(rrt ~ 1 + Q*P*F*L*ltt + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
mtable(m0.1, m0.2, coef.style="horizontal")
print(m0.1.nw <- lmer(rrt ~ 1 + Q*P*F*L*ltt + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1" & w=="NW"), cor=F)
print(m0.2.nw <- lmer(rrt ~ 1 + Q*P*F*L*ltt + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2" & w == "NW"), cor=F)
print(m0.1.nw <- lmer(rrt ~ 1 + Q*P*F*L*LP + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1" & w=="NW"), cor=F)
print(m0.1.nw <- lmer(rrt ~ 1 + Q*P*F*L*lpr + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1" & w=="NW"), cor=F)
levels(d$lpr)
table(d$LP)
table(d$LW)
table(d$ltt)
d1$LW <- ifelse(d1$ltt == "NW", 2/3, -1/3)
d2$LW <- ifelse(d2$ltt == "NW", 2/3, -1/3)
d <- rbind(d1, d2)
d$Exp <- factor(d$Exp)
contrasts(d$Exp) <- MASS::contr.sdif(2)
contrasts(d$ltt) <- MASS::contr.sdif(3)
table(d$LW)
table(d$LP)
table(d$ltt)
print(m0.1.nw <- lmer(rrt ~ 1 + Q*P*F*L*LP + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1" & w=="NW"), cor=F)
d2$LP <- ifelse(d2$ltt == "RelW", -2/3, 1/3)
d2$LW <- ifelse(d2$ltt == "NW", 2/3, -1/3)
d1$LP <- ifelse(d1$ltt == "RelW", -2/3, 1/3)
d1$LW <- ifelse(d1$ltt == "NW", 2/3, -1/3)
d <- rbind(d1, d2)
d$Exp <- factor(d$Exp)
contrasts(d$Exp) <- MASS::contr.sdif(2)
contrasts(d$ltt) <- MASS::contr.sdif(3)
print(m0.1.nw <- lmer(rrt ~ 1 + Q*P*F*L*LP + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1" & w=="NW"), cor=F)
table(d$w)
table(d$LP)
table(d$lpr)
print(m0.1.nw <- lmer(rrt ~ 1 + Q*P*F*L*LW + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1" & w=="NW"), cor=F)
table(d$LW)
table(d$lpr, d$LW)
table(d$lpr, d$w)
table(d$LP, d$w)
print(m0.1.nw <- lmer(rrt ~ 1 + Q*P*F*L*LP + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1" & w=="NW"), cor=F)
print(m0.1.nw <- lmer(rrt ~ 1 + Q*P*F*L + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1" & w=="NW"), cor=F)
print(m0.2.nw <- lmer(rrt ~ 1 + Q*P*F*L + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2" & w == "NW"), cor=F)
print(m0.1 <- lmer(rrt ~ 1 + Q*P*F*L + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1"), cor=F)
print(m0.1.nw <- lmer(rrt ~ 1 + Q*P*F*L + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1" & w=="NW"), cor=F)
print(m0.1.w <- lmer(rrt ~ 1 + Q*P*F*L + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="1" & w=="W"), cor=F)
print(m0.2 <- lmer(rrt ~ 1 + Q*P*F*L + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m0.2.nw <- lmer(rrt ~ 1 + Q*P*F*L + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2" & w == "NW"), cor=F)
print(m0.2.w <- lmer(rrt ~ 1 + Q*P*F*L + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2" & w == "W"), cor=F)
mtable(m0.1, m0.1.nw, m01.w, m0.2, m0.2.nw, m02.w, coef.style="horizontal")
mtable(m0.1, m0.1nw, m0.1w, m0.2, m0.2nw, m02w, coef.style="horizontal")
mtable(m0.1, m0.1.nw, m0.1.w, m0.2, m0.2.nw, m02.w, coef.style="horizontal")
mtable(m0.1, m0.1.nw, m0.1.w, m0.2, m0.2.nw, m0.2.w, coef.style="horizontal")
print(m0.2 <- lmer(rrt ~ 1 + Q*P*F*L*poly(trial,2) + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m0.2 <- lmer(rrt ~ 1 + (Q+P+F+L+poly(trial,2))^2 + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m0.2 <- lmer(rrt ~ 1 + (Q+P+F+L+ltt+poly(trial,2))^2 + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+L+ltt+(Q+L+ltt)*poly(trial,2) +  + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+L+ltt+Q*poly(trial,2) + ltt*poly(trial,1) + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+L+ltt+Q*poly(trial.c,2) + ltt*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+L+ ltt*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m0.2 <- lmer(rrt ~ 1 + (Q+P+F+L+ltt+poly(trial,2))^2 + (1 | id) + (1 | st) + (1|lst), data=d2), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+L+ ltt*trial.c + (1 | id) + (1 | st) + (1|lst), data=d2), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+L+ ltt*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+L+Q+ltt*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+L+(Q+ltt)*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+(Q+ltt)*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m2.2 <- lmer(rrt ~ 1 + P*F+(Q+ltt)*poly(trial.c,2) + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
anova(m1.2, m2.2, m0.2)
print(m0.2 <- lmer(rrt ~ 1 + (Q+P+F+L+ltt+poly(trial,2))^2 + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
anova(m1.2, m2.2, m0.2)
print(m0.2 <- lmer(rrt ~ 1 + (Q+P+F+ltt+poly(trial,2))^2 + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m0.2 <- lmer(rrt ~ 1 + (Q+P+F+ltt+poly(trial,2))^2 + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+(Q+ltt)*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
anova(m1.2, m0.2)
print(m1.2 <- lmer(rrt ~ 1 + P*F+(Q+LW+LP)*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m0.2 <- lmer(rrt ~ 1 +(P+F+ Q+LW+LP +poly(trial,2))^2 + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m0.2 <- lmer(rrt ~ 1 + (P+F+Q+LW+LP +trial.c)^2 - LW:LP + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+(Q+LW+LP)*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
anova(m1.2, m0.2)
print(m0.2 <- lmer(rrt ~ 1 + (P+F+Q+LW+LP +trial.c)^2 - LW:LP + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m1.2 <- lmer(rrt ~ 1 + P*F+(Q+LW+LP)*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
anova(m1.2, m0.2)
print(m1.2 <- lmer(rrt ~ 1 + P*F+Q+(LW+LP)*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
anova(m1.2, m0.2)
print(m1.2 <- lmer(rrt ~ 1 + P*F+Q+LP+LW*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
anova(m1.2, m0.2)
print(m2.2 <- lmer(rrt ~ 1 + P*F+Q+LP+LW*trial.c + (1 + p + trial.c | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m2.2 <- lmer(rrt ~ 1 + P*F+Q+LP+LW*trial.c +
(1+P | id) + (0+LW | id) + (0+Q | id) + (0+trial.c | id) + (0+F | id) + (1 | st) + (0+P | st)
, data=d, subset=Exp=="2"), cor=F)
print(m3.2 <- lmer(rrt ~ 1 + P*F+Q+LP+LW*trial.c + (1+P | id) + (0+F | id) + (0+Q | id) + (0+LW | id) + (0+trial.c | id) +
(0+LW:trial.c | id) + (1 | st) + (0+P | st), data=d, subset=Exp=="2"), cor=F)
anova(m2.2, m3.2)
anova(m1.2, m2.2, m3.2)
print(m1.2 <- lmer(rrt ~ 1 + P*F+Q+LW*trial.c + (1 | id) + (1 | st) + (1|lst), data=d, subset=Exp=="2"), cor=F)
print(m2.2 <- lmer(rrt ~ 1 + P*F+Q+LW*trial.c + (1+P | id) + (0+F | id) + (0+Q | id) + (0+LW | id) + (0+trial.c | id) +
(0+LW:trial.c | id) + (1 | st) + (0+P | st), data=d, subset=Exp=="2"), cor=F)
print(m2.2 <- lmer(rrt ~ 1 + P*F+Q+LW*trial.c + (1+P | id) + (0+F | id) + (0+Q | id) + (0+LW | id) + (0+trial.c | id) +
(1 | st) + (0+P | st), data=d, subset=Exp=="2"), cor=F)
print(m3.2 <- lmer(rrt ~ 1 + P*F+Q+LW*trial.c +
(1+P | id) + (0+F | id) + (0+Q | id) + (0+LW | id) + (0+trial.c | id) + (0+LW:trial.c | id) +
(1 | st) + (0+P | st) + (0+LW | st) + (0+trial.c | st) + (0+LW:trial.c | st), data=d, subset=Exp=="2"), cor=F)
anova(m1.2, m2.2, m3.2)
ggCaterpillar(ranef(m3.2, postVar=TRUE))
source("~/documents/projects/R_functions/ggCaterpillar.R")
ggCaterpillar(ranef(m3.2, postVar=TRUE))
ls()
printvc(m3.2)
print(m4.2 <- lmer(rrt ~ 1 + P*F+Q+LW*trial.c +
(1+P | id) + (0+F | id) + (0+Q | id) + (0+LW | id) + (0+trial.c | id) + (0+LW:trial.c | id) +
(1 | st) + (0+P | st) + (0+LW | st), data=d, subset=Exp=="2"), cor=F)
anova(m1.2, m2.2, m3.2, m4.2)
drop1(m3.2)
?drop1
?methods.mer
?lmer
(fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy))
drop1(fm2)
(fm1 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy))
drop1(fm1)
drop1(fm2)
drop1(m3.2)
print(m2.2 <- lmer(rrt ~ 1 + P*F+Q+LW*trial.c +
(1+P | id) + (0+F | id) + (0+Q | id) + (0+LW | id) + (0+trial.c | id) + (0+LW:trial.c | id) +
(1 | st) + (0+P | st), data=d, subset=Exp=="2"), cor=F)
anova(m1.2, m2.2, m3.2, m4.2)
print(m2.2nw <- lmer(rrt ~ 1 + P*F*Q*trial.c +
(1+P | id) + (0+F | id) + (0+Q | id) + (0+trial.c | id) +
(1 | st) + (0+P | st), data=d, subset=Exp=="2" & w == "NW"), cor=F)
print(m2.2nw <- lmer(rrt ~ 1 + P*F+Q+trial.c +
(1+P | id) + (0+F | id) + (0+Q | id) + (0+trial.c | id) +
(1 | st) + (0+P | st), data=d, subset=Exp=="2" & w == "NW"), cor=F)
print(m2.2w <- lmer(rrt ~ 1 + P*F+Q+trial.c +
(1+P | id) + (0+F | id) + (0+Q | id) + (0+trial.c | id) +
(1 | st) + (0+P | st), data=d, subset=Exp=="2" & w == "W"), cor=F)
d.NW <- subset(d, w=="NW")
print(m1 <- lmer(rrt ~ 1 + Q*P*F*Exp*trial.c +  (1 | id) + (1 | st) + (1|lst), data=d.NW), cor=F)
print(m2 <- lmer(rrt ~ 1 + (Q+P+F+Exp+trial.c)^2 +  (1 | id) + (1 | st) + (1|lst), data=d.NW), cor=F)
anova(m2, m1)
print(m3 <- lmer(rrt ~ 1 + (Q+P+F+Exp+trial.c)^2 + (1 + trial.c | id) + (1 | st) + (1|lst), data=d.NW), cor=F)
ggCaterpillar(ranef(m3, postVar=TRUE))
m3.RE <- lapply(names(ranef(m3)), function(x) cbind(ranef(m3)[[x]], table(model.frame(m3)[[x]])))
# ... preserve nonword info
RE_lst <- cbind(lst=row.names(m3.RE[[1]][1]), m3.RE[[1]][1])
names(RE_lst)[2] <- "M.lst"
RE_lst <- arrange(RE_lst, M.lst)
library(plyr)
RE_lst <- arrange(RE_lst, M.lst)
d.NW <- merge(d.NW, RE_lst, by="lst")
# ... restore trial order within subjects after merge
d.NW <- arrange(d.NW, id, trial)  # d.NW <- d.NW[order(d.NW$id, d.NW$trial), ]
