meanmethodfactor <- as.numeric(as.character(unlist(meanmethodfactor))) # make it numeric as well
Cmethodmeans <- as.numeric(Cmethodmeans)
meansbcombined <- as.data.frame(cbind(Ctraitmeans,meanmethodfactor,Cmethodmeans)) #2.
View(meansbcombined)
meansbcombined$methodvariation <- rep(methodvariation)
#View(meansbcombined$methodvariation)
meansbcombined$MTMMmeans <- rep(MTMMmethodmeans,c(35))
#class(loadingscombined)
# now add some handy columns for creating the plots
meansbcombined$latent <- rep(1:3)
loadingscombined$latent <- rep(1:9)
meansbcombined$wave <-   rep(1:35,c(3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3))
loadingscombined$wave <- rep(1:35,c(9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9))
# now, add one variable that reflects the mean in the change in quality (no longer used)
#loadingsmean <- tapply(loadingscombined$Cquality, (seq_along(loadingscombined$Cquality)-1) %/% 9, mean)
#loadingscombined$means <- rep(loadingsmean,c(9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9))
# add up differences in trait and method means
# for this, need to ceate a product matrix - 3 x3.
######################################################################
# I want moving averages to make the plot look nicer. Here is a function I stole
# http://druedin.com/2012/08/11/moving-averages-in-r/
mav <- function(x,n=3){filter(x,rep(1/n,n), sides=2)}
# ah crap. I need to change the order of the matrix, so that it is ordered on latent.
# use the order variable to order on latent variable
meansbcombined <- meansbcombined[order(meansbcombined$latent),]
#now, add this moving average to the data frame
meansbcombined$mavtraitmeans <- as.numeric(mav(meansbcombined$Ctraitmeans))
# impose a 0 for the first 2 waves (they get an NA in the mav-function)
meansbcombined$mavtraitmeans[meansbcombined$wave <3] <- 0
# now do the same for the method means
meansbcombined <- meansbcombined[order(meansbcombined$latent),]
meansbcombined$mavmethodmeans <- as.numeric(mav(meansbcombined$meanmethodfactor))
meansbcombined$mavmethodmeans[meansbcombined$wave <3] <- 0
#now do the same for method variation
meansbcombined <- meansbcombined[order(meansbcombined$latent),]
meansbcombined$mavmethodvariation <- as.numeric(mav(meansbcombined$methodvariation))
meansbcombined$mavmethodvariation[meansbcombined$wave <3] <- 0
# create a total bias coefficient
meansbcombined$totalbias <- meansbcombined$Ctraitmeans + meansbcombined$meanmethodfactor
# later plot these in 9 interaction-tables
# Ctraitmeans *
# meanmethodfactor
# totalbias
# or plot these in 3 interaction tables
# Ctraitmeans
# methodvariation
# totalbias
# now do the same for the quality coefficient
loadingscombined <- loadingscombined[order(loadingscombined$latent),]
#loadingscombined$mavquality[loadingscombined$wave<4]  <- as.numeric(loadingscombined$quality[loadingscombined$wave<4])
#loadingscombined$mavquality[loadingscombined$wave>3]  <- as.numeric(mav(loadingscombined$quality[loadingscombined$wave>3]))
#loadingscombined$mavCquality[loadingscombined$wave<4] <- as.numeric(loadingscombined$Cquality[loadingscombined$wave<4])
#loadingscombined$mavCquality[loadingscombined$wave>3] <- as.numeric(mav(loadingscombined$Cquality[loadingscombined$wave>3]))
loadingscombined$mavreliability[loadingscombined$wave<4]    <- as.numeric(loadingscombined$reliability[loadingscombined$wave<4])
loadingscombined$mavreliability[loadingscombined$wave>3]    <- as.numeric(mav(loadingscombined$reliability[loadingscombined$wave>3]))
loadingscombined$mavvalidity[loadingscombined$wave<4] <- as.numeric(loadingscombined$validity[loadingscombined$wave<4])
loadingscombined$mavvalidity[loadingscombined$wave>3] <- as.numeric(mav(loadingscombined$validity[loadingscombined$wave>3]))
loadingscombined$question <- factor(loadingscombined$latent,
levels=c("1","2","3","4","5","6","7","8","9"),
labels=c("parliament, 0-10 battery",
"legal system, 0-10 battery",
"police, 0-10 battery",
"parliament, 0-5  battery",
"legal system, 0-5  battery",
"police, 0-5  battery",
"parliament, 0-10 score",
"legal system, 0-10 score",
"police, 0-10 score"))
meansbcombined$question <- factor(meansbcombined$latent,
levels=c("1","2","3","4","5","6","7","8","9"),
labels=c("parliament, 0-10 battery",
"legal system, 0-10 battery",
"police, 0-10 battery",
"parliament, 0-5  battery",
"legal system, 0-5  battery",
"police, 0-5  battery",
"parliament, 0-10 score",
"legal system, 0-10 score",
"police, 0-10 score"))
######################################################################
# make a plot
#install.packages("ggplot2")
require(ggplot2)
library(ggplot2)
#meantraits <- # not in paper, later combied with method means
plot1<-
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ggtitle("means of trait factors")+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
labs(colour="Variable")
#methodtraits <- # not in paper, later combined with trait means
plot2<-
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ggtitle("means of method factors")+
ylab("difference in latent method means")+
xlab("waves")+
plot2<-
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ggtitle("means of method factors")+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
labs(colour="Variable")
plot2<-
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ggtitle("means of method factors")+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
labs(colour="Variable")
meansplot <- ggarrange(plot1,plot2, labels=c("Latent Traits","Latent Methods"),ncol=2,nrow=1)
meansplot <- ggarrange(plot1,plot2+rremove("x.text"),
labels=c("Latent Traits","Latent Methods"),ncol=2,nrow=1)
plot1<-
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
labs(colour="Variable")
#methodtraits <- # not in paper, later combined with trait means
plot2<-
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
labs(colour="Variable")
meansplot <- ggarrange(plot1,plot2+rremove("x.text"),
labels=c("Latent Traits","Latent Methods"),ncol=2,nrow=1)
meansplot <- ggarrange(plot1,plot2+rremove("x.text"),
labels=c("Latent Traits","Latent Methods"),ncol=2,nrow=1)
plot2
plot1
require(cowplot)
meansplot <- plot_grid(plot1,plot2+rremove("x.text"),
labels=c("Latent Traits","Latent Methods"),ncol=2,nrow=1)
meansplot <- plot_grid(plot1,plot2+rremove("x.text"),
labels=c("Latent Traits","Latent Methods"),ncol=2,nrow=1)
meansplot <- plot_grid(plot1,plot2+rremove("x.text"),
ncol=2,nrow=1)
plot2
meansplot <- plot_grid(plot1,plot2,
labels=c("Latent Traits","Latent Methods"),ncol=2,nrow=1)
meansplot <- grid.arrange(plot1,plot2+rremove("x.text"),
labels=c("Latent Traits","Latent Methods"),ncol=2,nrow=1)
require(gridExtra)
meansplot <- grid.arrange(plot1,plot2+rremove("x.text"),
labels=c("Latent Traits","Latent Methods"),ncol=2,nrow=1)
meansplot <- grid.arrange(plot1,plot2+rremove("x.text"),
labels=c("Latent Traits","Latent Methods"),ncol=2,nrow=1)
meansplot <- grid.arrange(plot1,plot2+rremove("x.text"),
labels=c("Latent Traits","Latent Methods"),ncol=2)
meansplot <- grid.arrange(plot1,plot2+rremove("x.text"),
ncol=2)
View(meansbcombined)
plot1<-
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
scale_fill_discrete(name="Latent Traint Mean differences",
breaks=c("partliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
plot2<-
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_fill_discrete(name="Relative Method Mean differences ",
breaks=c("partliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
meansplot <- grid.arrange(plot1,plot2+rremove("x.text"),
ncol=2)
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_fill_discrete(name="Relative Method Mean differences ",
breaks=c("partliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
plot2
plot2
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_fill_discrete(name="Relative Method Mean differences ",
breaks=c("partliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_fill_discrete(name="Relative Method Mean differences ",
breaks=c("partliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
plot2
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_fill_discrete(name="Relative Method Mean differences ",
breaks=c("partliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))+
labs(colour="Variable")
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_fill_discrete(name="Relative Method Mean differences ",
breaks=c("partliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
plot2
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Relative Method Mean differences ",
breaks=c("partliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Latent Traint Mean differences",
breaks=c("partliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Latent Traint Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Relative Method Mean differences ",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
plot1 <-
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Latent Traint Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
plot2 <-
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Relative Method Mean differences ",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
meansplot <- grid.arrange(plot1,plot2+rremove("x.text"),
ncol=2)
meansplot <- grid.arrange(plot1,plot2+rremove("x.text"),
ncol=1,nrow=2)
plot1 <-
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Latent Trait Mean differences   ",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
plot2 <-
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Relative Method Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
meansplot <- grid.arrange(plot1,plot2+rremove("x.text"),
ncol=1,nrow=2)
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
coord_fixed(ratio = 0.5)+
scale_colour_discrete(name="Latent Trait Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
coord_fixed(ratio = 0.2)+
scale_colour_discrete(name="Latent Trait Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
coord_fixed(ratio = 1)+
scale_colour_discrete(name="Latent Trait Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Latent Trait Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Latent Trait /n Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Latent Trait \n Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
plot2 <-
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Relative Method \n Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
plot2 <-
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Relative Method \n Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Relative Method \n Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
plot1 <-
ggplot(meansbcombined, aes(y=mavtraitmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent trait means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Latent Trait \n Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("parliament", "legal system", "police"))
plot2 <-
ggplot(meansbcombined, aes(y=Cmethodmeans, x=wave, group=question,colour=question))+
geom_point()+
geom_line()+
ylab("difference in latent method means")+
xlab("waves")+
theme_bw()+
scale_colour_discrete(name="Relative Method \n Mean differences",
breaks=c("parliament, 0-10 battery","legal system, 0-10 battery", "police, 0-10 battery"),
labels=c("0-10 battery", "0-5 battery", "0-10 score"))
meansplot <- grid.arrange(plot1,plot2+rremove("x.text"),
ncol=1,nrow=2)
ggsave(meansplot, file = "D:/SURFdrive/Onderzoek/common metric/paper/combined means.png", width = 10, height = 5)
ggsave(meansplot, file = "D:/SURFdrive/Onderzoek/common metric/paper/combined means.png", width = 20, height = 12)
ggsave(meansplot, file = "D:/SURFdrive/Onderzoek/common metric/paper/combined means.png", width = 10, height = 6)
meansplot <- grid.arrange(plot1,plot2,
ncol=1,nrow=2)
ggsave(meansplot, file = "D:/SURFdrive/Onderzoek/common metric/paper/combined means.png", width = 10, height = 6)
combinedloadingsfigure <-
ggplot(combined[c(1:630),], aes(y=value,x=wave, colour=question,shape=question))+
geom_point()+
geom_line()+
ggtitle("Reliability and validity  loadings over time")+
ylab("size of coefficient")+
xlab("waves")+
theme_bw()+
facet_wrap( ~ variable)
combined <- melt(combined, id = c("question","wave"))
combined <- loadingscombined[,c(6,9,1,3,7,8)]
require(reshape)
combined <- melt(combined, id = c("question","wave"))
combined$variable <- factor(combined$variable, levels= c("reliability","validity", "mavreliability","mavvalidity"),
labels =c("reliability", "validity","mavreliability","mavvalidity"))
combinedloadingsfigure <-
ggplot(combined[c(1:630),], aes(y=value,x=wave, colour=question,shape=question))+
geom_point()+
geom_line()+
ggtitle("Reliability and validity  loadings over time")+
ylab("size of coefficient")+
xlab("waves")+
theme_bw()+
facet_wrap( ~ variable)
ggplot(combined[c(1:630),], aes(y=value,x=wave, colour=question,shape=question))+
geom_point()+
geom_line()+
ggtitle("Reliability and validity  loadings over time")+
ylab("size of coefficient")+
xlab("waves")+
theme_bw()+
facet_wrap( ~ variable)
ggsave(combinedloadingsfigure, file = "D:/SURFdrive/Onderzoek/common metric/paper/combined loadings.png", width = 10, height = 5)
npwerwavet <- 2873 - nperwave # create a figure with sample sizes [figure as in paper]
nperwavefigure <- melt(nperwavet)
nperwavefigure$wave <- 1:35
samplesizes <- ggplot(nperwavefigure, aes(y=value, x=wave))+
geom_point()+
geom_line()+
ggtitle("Sample sizes December 2008-December 2011 in LISS panel")+
ylab("sample size")+
ylim(0,max(nperwavefigure$value))+
theme_bw()+
xlab("waves")
ggsave(samplesizes, file = "D:/SURFdrive/Onderzoek/common metric/paper/sample sizes.png", width = 10, height = 5)
nperwave <- nperwave[nperwave>0];
npwerwavet <- 2873 - nperwave # create a figure with sample sizes [figure as in paper]
View(nperwavet)
nperwavet <- 2873 - nperwave # create a figure with sample sizes [figure as in paper]
View(nperwavet)
nperwavefigure <- melt(nperwavet)
nperwavefigure$wave <- 1:35
samplesizes <- ggplot(nperwavefigure, aes(y=value, x=wave))+
geom_point()+
geom_line()+
ggtitle("Sample sizes December 2008-December 2011 in LISS panel")+
ylab("sample size")+
ylim(0,max(nperwavefigure$value))+
theme_bw()+
xlab("waves")
ggsave(samplesizes, file = "D:/SURFdrive/Onderzoek/common metric/paper/sample sizes.png", width = 10, height = 5)
View(ndata)
ndata <- 3217 - ndata[,c(278:307,309:313)]
nperwave <- as.numeric(apply(ndata,2,FUN=function(x) length(which(x=='selected for at least 1 questionnaire and completed at least one'))))
nperwave <- 3217-nperwave
nperwavefigure <- melt(nperwave)
nperwavefigure$wave <- 1:35
samplesizes <- ggplot(nperwavefigure, aes(y=value, x=wave))+
geom_point()+
geom_line()+
ggtitle("Sample sizes December 2008-December 2011 in LISS panel")+
ylab("sample size")+
ylim(0,max(nperwavefigure$value))+
theme_bw()+
xlab("waves")
ggsave(samplesizes, file = "D:/SURFdrive/Onderzoek/common metric/paper/sample sizes.png", width = 10, height = 5)
