library(animint2)
data(prior)
prior$accuracy$percent <- prior$accuracy$accuracy.mean * 100
prior$accuracy$percent.se <- prior$accuracy$accuracy.se * 100
sqLab <- "squared error of the prior estimate"
priorBands <-
list(set=ggplot()+
geom_abline()+
geom_text(aes(positive, negative, label=set), data=prior$data)+
geom_point(aes(positive, negative, size=dimension),
clickSelects="set",
data=prior$data)+
scale_size_continuous(range=c(3,20),breaks=prior$data$dim),
error=ggplot()+
make_text(prior$accuracy, 86, 0.3, "prior")+
make_text(prior$accuracy, 86, 0.32, "samples")+
geom_point(aes(percent, sqErr.mean, fill=method, colour=classifier),
showSelected=c("prior", "samples"),
clickSelects="set",
data=prior$accuracy, size=4)+
scale_colour_manual(values=c("Kernel logistic regression"="black",
"Least squares probabalistic classifier"="white"))+
ylab(sqLab)+
xlab("percent classification accuracy"),
samples=ggplot()+
make_tallrect(prior$accuracy, "samples")+
make_text(prior$accuracy, 175, 97.5, "prior")+
make_text(prior$accuracy, 175, 95, "set")+
geom_ribbon(aes(samples,
ymin=percent-percent.se,
ymax=percent+percent.se,
group=interaction(method, classifier),
fill=method),
showSelected=c("prior", "set"),
data=prior$accuracy, alpha=1/4)+
geom_line(aes(samples, percent, group=interaction(method, classifier),
colour=method, linetype=classifier),
showSelected=c("prior", "set"),
data=prior$accuracy)+
guides(colour="none",linetype="none",fill="none")+
xlab("number of points sampled")+
ylab("percent classification accuracy"),
prior=ggplot()+
make_tallrect(prior$accuracy, "prior")+
make_text(prior$accuracy, 0.5, 97.5, "samples")+
make_text(prior$accuracy, 0.5, 95, "set")+
geom_ribbon(aes(prior, ymin=percent-percent.se, ymax=percent+percent.se,
group=interaction(method, classifier),
fill=method),
showSelected=c("samples", "set"),
data=prior$accuracy, alpha=1/4)+
geom_line(aes(prior, percent, group=interaction(method, classifier),
colour=method, linetype=classifier),
showSelected=c("samples", "set"),
data=prior$accuracy)+
xlab("class prior")+
ylab("percent classification accuracy"),
samplessqErr=ggplot()+
make_tallrect(prior$accuracy, "samples")+
geom_ribbon(aes(samples,
ymin=sqErr.mean-sqErr.se,
ymax=sqErr.mean+sqErr.se,
group=interaction(method, classifier),
fill=method),
showSelected=c("prior", "set"),
data=prior$accuracy, alpha=1/4)+
geom_line(aes(samples, sqErr.mean, group=interaction(method, classifier),
colour=method, linetype=classifier),
showSelected=c("prior", "set"),
data=prior$accuracy)+
guides(colour="none",linetype="none",fill="none")+
xlab("number of points sampled")+
ylab(sqLab),
priorsqErr=ggplot()+
make_tallrect(prior$accuracy, "prior")+
geom_ribbon(aes(prior,
ymin=sqErr.mean-sqErr.se,
ymax=sqErr.mean+sqErr.se,
group=interaction(method, classifier),
fill=method),
showSelected=c("samples", "set"),
data=prior$accuracy, alpha=1/4)+
geom_line(aes(prior, sqErr.mean, group=interaction(method, classifier),
colour=method, linetype=classifier),
showSelected=c("samples", "set"),
data=prior$accuracy)+
xlab("class prior")+
ylab(sqLab))
animint2dir(priorBands, "prior")
## are the exported files the same?
## csv.files <- Sys.glob("/tmp/RtmpVIt99h/filee8b6b741ce7/*.csv")
## for(i in 1:(length(csv.files)-1)){
## for(j in (i+1):length(csv.files)){
## cmd <- sprintf("diff %s %s|head -1",csv.files[i],csv.files[j])
## out <- system(cmd, intern=TRUE)
## if(length(out)==0){
## print(cmd)
## }
## }
## }
## Answer: 3 pairs are the same: (12,20), (14,9), (17,7). So actually
## there is not so much repetition that can be easily avoided.
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