inst/unitTests/test_ccrepe.R

library(testthat)
find_expr <- function(name, env = parent.frame()) {
      subs <- do.call("substitute", list(as.name(name), env))
        paste0(deparse(subs, width.cutoff = 500), collapse = "\n")
  }

is_approximately <- function(expected,tol=10e-7,label=NULL)
{
      if (is.null(label)) {
              label <- find_expr("expected")
          } else if (!is.character(label) || length(label) != 1) {
                  label <- deparse(label)
              }
      
    function(actual)
        {
            same <- all.equal.numeric(as.vector(actual),as.vector(expected),tol=tol)

            expectation(
                 "success",
                identical(same,TRUE),
                paste0("not equal to ", label, " within tolerance ",tol,"\n", same)
                )
        }
}

testdata<-matrix(c(0.29787234, 0.2978723, 0.2553191, 0.1489362,
		0.17073171, 0.3170732, 0.2682927, 0.2439024,
		0.09302326, 0.3255814, 0.2558140, 0.3255814,
		0.32352941, 0.3235294, 0.1470588, 0.2058824,
		0.17241379, 0.1724138, 0.4137931, 0.2413793,
		0.29729730, 0.2162162, 0.2702703, 0.2162162,
		0.22500000, 0.3250000, 0.2000000, 0.2500000,
		0.12820513, 0.3589744, 0.2307692, 0.2820513,
		0.20000000, 0.2250000, 0.2250000, 0.3500000,
		0.10256410, 0.3076923, 0.1794872, 0.4102564
		),nrow=10,ncol=4,byrow = TRUE)
dimnames(testdata) = list(
            c("Subject 1", "Subject 2","Subject 3","Subject 4","Subject 5","Subject 6","Subject 7","Subject 8","Subject 9","Subject 10"),
            c("bug 1", "bug 2", "bug 3","bug 4")) # column names 
		

                                        #***********************************************************
                                        #* ccrepe tests against testdata                           *
                                        #***********************************************************
context("CCREPE")
ccrepe.results <-  ccrepe  (x=testdata,   min.subj=10)
tol = 0.20
p.values.results <- matrix(c(NA, 0.6337192, 0.4547206, 0.06927885,
							0.63371917,        NA, 0.2432229, 0.19897879,
							0.45472060, 0.2432229,        NA, 0.77606014,
							0.06927885, 0.1989788, 0.7760601 ,        NA),
                             nrow=4,ncol=4,byrow = TRUE)
     
q.values.results <- matrix(c(  NA, 1.801518, 1.615833, 0.9847193,
				1.8015179  ,     NA ,1.152378, 1.4141274,
				1.6158327, 1.152378,       NA ,1.8384672,
				0.9847193 ,1.414127, 1.838467,        NA),
                             nrow=4,ncol=4,byrow = TRUE)

sim.score.results <- matrix(c(   NA, -0.20691521 ,-0.1639831, -0.77208214,
				-0.2069152  ,        NA, -0.6739868 , 0.06805391,
				-0.1639831 ,-0.67398679  ,       NA ,-0.22701786,
				-0.7720821  ,0.06805391 ,-0.2270179  ,        NA),
                            nrow=4,ncol=4,byrow = TRUE)
							
z.stat.results <-matrix(c( NA,  0.4352228,  0.7337243, -1.8029587,
				0.4352228 ,        NA ,-1.1905054,  1.2389936,
				 0.7337243, -1.1905054,         NA , 0.2843813,
				-1.8029587 , 1.2389936,  0.2843813 ,        NA ),
                        nrow=4,ncol=4,byrow = TRUE)
   


 

expect_that(p.values.results,is_approximately(ccrepe.results$p.values, tol))
expect_that(q.values.results,is_approximately(ccrepe.results$q.values,tol))
expect_that(sim.score.results,is_approximately(ccrepe.results$sim.score, tol))
expect_that(z.stat.results,is_approximately(ccrepe.results$z.stat, tol))



context("Permutation p-values")

## test_that("Renormalization gives normalized data without missing", {
##     data <- matrix(1:10,nrow=2,byrow=TRUE)
##     data.norm <- ccrepe_norm(data)

##     expect_that( is.matrix(data.norm), is_true())
##     expect_that( apply(data.norm,1,sum), equals(c(1,1)) )
##     expect_that( data.norm, equals( matrix(c(1/15,2/15,3/15,4/15,5/15,6/40,7/40,8/40,9/40,0.25),
##                                            byrow=TRUE,
##                                            nrow=2) ) )
## })

## test_that("Renormalization works with missing data", {
##     data <- matrix(1:10,nrow=2,byrow=TRUE)
##     data[1,2] = NA
##     data.norm <- ccrepe_norm(data)

##     expect_that( data.norm[1,], equals(rep(0,ncol(data))) )
## })


## test_that("Permutation of one matrix permutes the data by columns", {
##     data <- matrix(1:12, ncol=3)
##     permute.id.matrix <- matrix(c(4,3,2,1,2,1,4,3,2,3,1,4),ncol=3)
##     data.permute <- permute(data,permute.id.matrix)

##     expect_that( is.matrix(data.permute), is_true() )
##     expect_that( data.permute, equals(matrix(c(4,3,2,1,6,5,8,7,10,11,9,12),ncol=3)) )
## })

## v_dist.na   <- c(3.4,2,NA,2,4.6,0,10,2,8,NA,-2,0,NA)
## v_dist      <- c(3.4,2,2,4.6,0,10,2,8,-2,0)
## v_dist.null <- NA
## obs.value1 <- 3.4             # high p-value, in dist
## obs.value2 <- 10              # low p-value, in dist
## obs.value3 <- 4               # high p-value, not in dist
## obs.value4 <- -5              # low p-value, not in dist
## dist.value1 <- 2              # repeated value
## dist.value2 <- 0              # repeated value

## test_that("get_count returns a correct value with no missing", {

##     count1 <- get_count(dist.value1, v_dist)
##     count2 <- get_count(dist.value2, v_dist)
##     count3 <- get_count(obs.value3, v_dist)
##     count4 <- get_count(obs.value1, v_dist)

##     expect_that( count1, equals(3) )
##     expect_that( count2, equals(2) )
##     expect_that( count3, equals(0) )
##     expect_that( count4, equals(1) )
## })

## test_that("get_perm_p.value works with no missing", {

##     p.value1 <- get_perm_p.value(v_dist,obs.value1)
##     p.value2 <- get_perm_p.value(v_dist,obs.value2)
##     p.value3 <- get_perm_p.value(v_dist,obs.value3)
##     p.value4 <- get_perm_p.value(v_dist,obs.value4)

##     expect_that( p.value1, equals(1) )
##     expect_that( p.value2, equals(.1) )
##     expect_that( p.value3, equals(.9) )
##     expect_that( p.value4, equals(0) )
## })

## test_that("get_perm_p.value works with missing", {

##     p.value1 <- get_perm_p.value(v_dist.na,obs.value1)
##     p.value2 <- get_perm_p.value(v_dist.na,obs.value2)
##     p.value3 <- get_perm_p.value(v_dist.na,obs.value3)
##     p.value4 <- get_perm_p.value(v_dist.na,obs.value4)

##     expect_that( p.value1, equals(1) )
##     expect_that( p.value2, equals(.1) )
##     expect_that( p.value3, equals(.9) )
##     expect_that( p.value4, equals(0) )
## })

## test_that("get_perm_p.value returns missing if only missing in v_dist", {

##     p.value <- get_perm_p.value(v_dist.null,obs.value1)

##     expect_that( is.na(p.value), is_true() )
## })

## test_that("get_perm_p.value returns missing if obs.value is missing", {

##     p.value <- get_perm_p.value(v_dist, NA)

##     expect_that( is.na(p.value), is_true() )
## })

 
Bioconductor-mirror/ccrepe documentation built on June 1, 2017, 5:27 a.m.