tests/testthat/test-corrp.R

In meantrix/corrP: corrp (Compute correlations type analysis in parallel)

test_that("Tests on corrp and cor_fun functions", {

  #data to tests
  df = iris


  #class test
  corr = corrp(df,comp = 'g', alternative = 't')
  expect_s3_class(corr,'clist')

  #parallel test
  corr2 = corrp(df,comp = 'g', alternative = 't',parallel = F)
  expect_s3_class(corr2,'clist')
  expect_equal(corr,corr2)

  #P-Value test
  data = corr$data
  data.p1 = subset(data,stat.value < 0.05 )
  expect_true( all(data.p1$isig) )


  #Inferences test 1: corrp
  data.pearson = subset(data,infer == "Pearson Correlation")
  data.cramer = subset(data,infer == "Cramer's V")
  data.linear = subset(data,infer == "Linear Model")

  #Pearson Correlation
  for(i in 1:NROW(data.pearson)){

  i.test = stats::cor.test(df[[ as.character(data.pearson$varx[i]) ]], df[[ as.character(data.pearson$vary[i]) ]], alternative = 't')

  expect_equal( i.test$p.value,data.pearson$stat.value[i] )
  expect_equal( as.numeric(i.test$'estimate'), data.pearson$infer.value[i])


  }

  #Cramer's V
  for(i in 1:NROW(data.cramer)){

    i.pv = stats::chisq.test(df[[ as.character(data.cramer$varx[i]) ]], df[[ as.character(data.cramer$vary[i]) ]],
                             simulate.p.value=TRUE)$p.value
    i.test = lsr::cramersV(iris[[ as.character(data.cramer$varx[i]) ]], iris[[ as.character(data.cramer$vary[i]) ]])

    expect_equal( i.pv,data.cramer$stat.value[i] )
    expect_equal( i.test, data.pearson$infer.value[i])


  }

  #Linear Model
  for(i in 1:NROW(data.linear)){

    x = df[[ as.character(data.linear$varx[i]) ]]
    y = df[[ as.character(data.linear$vary[i]) ]]
    clx = class(x)
    cly = class(y)

    cond.cn = ( cly %in% c("factor", "character") && clx %in% c("integer", "numeric") )

    if( cond.cn){

      z = x
      clz = clx

      x = y
      clx = cly
      y = z
      cly = clz

    }



    i.test = summary(stats::lm( y ~ as.factor(x) ))

    expect_equal( as.numeric(stats::pf (i.test$fstatistic[1],i.test$fstatistic[2],
                             i.test$fstatistic[3],lower.tail=F)), data.linear$stat.value[i] )
    expect_equal( sqrt(i.test[["r.squared"]]), data.linear$infer.value[i] )


  }

  #Inferences test 2: cor_fun

  #sample numerical correlations to test
  data.num = data.pearson[sample(1:NROW(data.pearson),3),]

  #Numerical
  #MIC
  for(i in 1:NROW(data.num)){

    i.test = minerva::mine(df[[ as.character(data.num$varx[i]) ]], df[[ as.character(data.num$vary[i]) ]] )$MIC
    i.pv = ptest(df[[ as.character(data.num$varx[i]) ]], df[[ as.character(data.num$vary[i]) ]],
                 FUN =function(...) {z = minerva::mine(...); return(z$MIC) }, alternative = 't')

    i.fun = corr_fun(df,nx = as.character(data.num$varx[i]) , ny = as.character(data.num$vary[i]), alternative = 't', cor.nn = 'mic',
                     verbose = F)

    expect_equal( i.test, i.fun$infer.value )
    expect_equal( i.pv, i.fun$stat.value)


  }

  #Dcor
  for(i in 1:NROW(data.num)){

    i.test = dcorT_test(df[[ as.character(data.num$varx[i]) ]], df[[ as.character(data.num$vary[i]) ]])

    i.fun = corr_fun(df,nx = as.character(data.num$varx[i]) , ny = as.character(data.num$vary[i]), alternative = 't', cor.nn = 'dcor',
                     verbose = F)

    expect_equal(as.numeric(i.test$estimate) , i.fun$infer.value )
    expect_equal(i.test$p.value, i.fun$stat.value)


  }

  #PPS
  for(i in 1:NROW(data.num)){

    i.test = ppsr::score( df,x = as.character(data.num$varx[i]) , y = as.character(data.num$vary[i]) )

    i.fun = corr_fun( df,nx = as.character(data.num$varx[i]) , ny = as.character(data.num$vary[i]), alternative = 't', cor.nn = 'pps',
                     verbose = F)



    expect_equal( i.test$pps , i.fun$infer.value )
    expect_equal( i.test$metric , i.fun$stat )
    expect_equal( i.test$model_score , i.fun$stat.value )

  }

  #Categorical

  #PPS
  for(i in 1:NROW(data.cramer)){

    i.test = ppsr::score( df,x = as.character(data.cramer$varx[i]) , y = as.character(data.cramer$vary[i]) )

    i.fun = corr_fun( df,nx = as.character(data.cramer$varx[i]) , ny = as.character(data.cramer$vary[i]), alternative = 't', cor.cc = 'pps',
                      verbose = F)



    expect_equal( i.test$pps , i.fun$infer.value )
    expect_equal( i.test$metric , i.fun$stat )
    expect_equal( i.test$model_score , i.fun$stat.value )

  }

  #Uncoef
  for(i in 1:NROW(data.cramer)){

    i.test = DescTools::UncertCoef(df[[as.character(data.cramer$varx[i])]], df[[as.character(data.cramer$vary[i])]])
    i.pv = ptest(df[[as.character(data.cramer$varx[i]) ]], df[[as.character(data.cramer$vary[i])]],
                 FUN = DescTools::UncertCoef, alternative = 't', rk = TRUE)

    i.fun = corr_fun(df, nx = as.character(data.cramer$varx[i]), ny = as.character(data.cramer$vary[i]), alternative = 't', cor.cc = 'uncoef',
                     verbose = F)

    expect_equal(i.test, i.fun$infer.value)
    expect_equal(i.pv, i.fun$stat.value)


  }

})