Nothing
context("get.tau.permute")
test_that("get.tau.permute returns appropriate values for test case 1 (equilateral triangle)" ,{
x <- rbind(c(1,0,0), c(1,1,0),c(2,.5,sqrt(.75)))
colnames(x) <-c("type","x","y")
test <- function(a,b) {
if (a[1] != 1) return(3)
if (b[1] == 2) return(1)
return(2)
}
###REPRESENTATIVE
#should return 1 for every permutation
res <- get.tau.permute(x, test, 1.5, 0, 500)[,-(1:2)]
res2 <- get.tau.typed.permute(x, 1, 2, 1.5, 0, 500)[,-(1:2)]
expect_that(as.numeric(res), equals(rep(1,500)))
expect_that(as.numeric(res2), equals(rep(1,500)))
###INDEPENDENT
#should return 1 for every permutation
res <- get.tau.permute(x, test, 1.5, 0, 500,
comparison.type="independent")[,-(1:2)]
res2 <- get.tau.typed.permute(x, 1, 2, 1.5, 0, 500,
comparison.type="independent")[,-(1:2)]
expect_that(as.numeric(res), equals(rep(1,500)))
expect_that(as.numeric(res2), equals(rep(1,500)))
})
test_that("get.tau.permute returns appropriate values for test case 2 (points on a line)" ,{
x<-rbind(c(1,0,0), c(2,1,0), c(2,-1,0), c(3,2,0),
c(2,-2,0), c(3,3,0),c(3,-3,0))
colnames(x) <-c("type","x","y")
test <- function(a,b) {
if (a[1] != 1) return(3)
if (b[1] == 2) return(1)
return(2)
}
####REPRESENTATIVE
#the mean of the null distribution should be 1
#the 95% CI equals 0,2 with windows
res <- get.tau.permute(x, test, c(1.5,2.5,3.5), c(0,1.5,2.5), 500)[,-(1:2)]
res2 <- get.tau.typed.permute(x, 1, 2, c(1.5,2.5,3.5), c(0,1.5,2.5), 500)[,-(1:2)]
expect_that(rowMeans(res, na.rm=T), equals(rep(1,3), tolerance=0.1))
expect_that(rowMeans(res2, na.rm=T), equals(rep(1,3), tolerance=0.1))
for (i in 1:3) {
expect_that(as.numeric(quantile(as.numeric(res[i,]), probs=c(.025,.975))),
equals(c(0,2)))
expect_that(as.numeric(quantile(as.numeric(res2[i,]), probs=c(.025,.975))),
equals(c(0,2)))
}
####INDEPENDENT
#the mean of the null distribution should be 1
#the 95% CI equals 0,Inf with windows
res <- get.tau.permute(x, test, c(1.5,2.5,3.5), c(0,1.5,2.5), 500,
comparison.type="independent")[,-(1:2)]
res2 <- get.tau.typed.permute(x, 1, 2, c(1.5,2.5,3.5), c(0,1.5,2.5), 500,
comparison.type="independent")[,-(1:2)]
for (i in 1:3) {
expect_that(as.numeric(quantile(as.numeric(res[i,]), probs=c(.025,.5,.975))),
equals(c(0,1,Inf)))
expect_that(as.numeric(quantile(as.numeric(res2[i,]), probs=c(.025,.5,.975))),
equals(c(0,1,Inf)))
}
})
test_that ("fails nicely if x and y column names are not provided", {
x<-cbind(rep(c(1,2),500), a=runif(1000,0,100), b=runif(1000,0,100))
test <- function(a,b) {
if (a[1] != 2) return(3)
if (b[1] == 3) return(1)
return(2)
}
expect_that(get.tau.permute(x,test,seq(10,50,10), seq(0,40,10),100),
throws_error("unique x and y columns must be defined"))
})
##################DEPRECATED TESTS...TAKE TO LONG...NOW USING SMALLER CANONICAL
##################TESTS THAT HAVE VALUES THAT CAN BE WORKED OUT BY HAND
## test_that("get.tau.permute cis enclose get.tau when no clustering exists",
## {
## set.seed(788)
## x<-cbind(rep(c(1,2),250), x=runif(500,0,100), y=runif(500,0,100))
## colnames(x) <-c("type","x","y")
## test <- function(a,b) {
## if (a[1] != 1) return(3)
## if (b[1] == 1) return(1)
## return(2)
## }
## #plot(x[,"x"],x[,"y"], col=x[,"type"])
## res <- get.tau.permute(x, test, seq(10,100,10), seq(0,90,10), 200)
## res2 <- get.tau(x, test, seq(10,100,10), seq(0,90,10))
## for (i in 1:10) {
## tmp <- quantile(res[,i], probs=c(0.025, .975), na.rm=T)
## #print(res2[i])
## #print(tmp)
## expect_that(res2[i]>=tmp[1], is_true())
## expect_that(res2[i]<=tmp[2], is_true())
## }
## })
## test_that("get.tau.permute cis do not enclose get.tau at extremes when no clustering exists",
## {
## set.seed(788)
## #first generate 200 random uniform points
## x<-cbind(1, x=runif(300,0,100), y=runif(300,0,100))
## colnames(x) <-c("type","x","y")
## #add a seed point
## x<-rbind(x,c(2,50,50))
## #generate 200 normally distibuted points around this
## x<-rbind(x,cbind(3,rnorm(300,50,20),rnorm(300,50,20)))
## test <- function(a,b) {
## if (a[1] != 2) return(3)
## if (b[1] == 3) return(1)
## return(2)
## }
## ## res <- get.tau.permute(x,test,seq(10,50,10), seq(0,40,10), 200)
## res2 <- get.tau(x,test,seq(10,50,10), seq(0,40,10))
## for (i in c(1,5)) {
## tmp <- quantile(res[,i], probs=c(0.025, .975), na.rm=T)
## #print(tmp)
## #print(res2[i])
## expect_that((res2[i]>=tmp[1]) & (res2[i]<=tmp[2]) ,
## is_false())
## }
## res <- get.tau.typed.permute(x,2,3,seq(10,50,10),
## seq(0,40,10), 100)
## for (i in c(1,5)) {
## tmp <- quantile(res[,i], probs=c(0.025, .975), na.rm=T)
## #print(res2[i])
## #print(tmp)
## expect_that((res2[i]>=tmp[1]) & (res2[i]<=tmp[2]) ,
## is_false())
## }
## })
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