disptest<-matrix(rnorm(200),nrow=20)
disptest.means<-rowMeans(disptest)
row.order<-order(disptest.means)
se.disptest<-unlist(apply(disptest,1,std.error))
plot(disptest.means[row.order],main="Dispersion as error bars",
ylim=c(min(disptest.means-se.disptest),max(disptest.means+se.disptest)),
xlab="Occasion",ylab="Value")
dispersion(1:20,disptest.means[row.order],se.disptest[row.order])
plot(disptest.means[row.order],main="Dispersion as confidence band",
ylim=c(min(disptest.means-se.disptest),max(disptest.means+se.disptest)),
xlab="Occasion",ylab="Value")
dispersion(1:20,disptest.means[row.order],se.disptest[row.order],type="l",
fill="#eeccee",lty=2,pch=1)
disptest2<-matrix(sample(c(TRUE,FALSE),200,TRUE),nrow=10)
disptest.prop<-rowMeans(disptest2)
disptest.ulim<-disptest.llim<-rep(NA,10)
for(i in 1:10) {
disptest.ulim[i]<-binciWu(disptest2[i,],20)
disptest.llim[i]<-binciWl(disptest2[i,],20)
}
plot(disptest.prop,main="Dispersion as binomial confidence intervals",
ylim=c(min(disptest.llim),max(disptest.ulim)),
xlab="Sample",ylab="Proportion")
dispersion(1:10,disptest.prop,disptest.ulim,disptest.llim,
interval=FALSE,lty=2,pch=1)
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