tests/testthat/test_gg_rfsrc.R

In ehrlinger/ggRFVignette: Vignettes for ggRandomForests package

# testthat for gg_rfsrc function
context("gg_rfsrc tests")

test_that("gg_rfsrc classifications",{
  ## Load the cached forest
  data(rfsrc_iris, package="ggRandomForests")
  
  # Test the cached forest type
  expect_is(rfsrc_iris, "rfsrc")
  
  # Test the forest family
  expect_is(rfsrc_iris, "class")
  
  ## Create the correct gg_error object
  gg_dta <- gg_rfsrc(rfsrc_iris)
  
  # Test object type
  expect_is(gg_dta, "gg_rfsrc")
  
  # Test classification dimensions
  expect_equal(nrow(gg_dta), nrow(rfsrc_iris$predicted.oob))
  expect_equal(ncol(gg_dta), ncol(rfsrc_iris$predicted.oob) + 1)
  
  # Test data is correctly pulled from randomForest obect.
  expect_equivalent(as.matrix(gg_dta[, -which(colnames(gg_dta) == "y")]),
                    rfsrc_iris$predicted.oob)
  
  ## Test plotting the gg_error object
  gg_plt <- plot.gg_rfsrc(gg_dta)
  
  # Test return is s ggplot object
  expect_is(gg_plt, "ggplot")
  
  
  ## Create the correct gg_error object
  gg_dta <- gg_rfsrc(rfsrc_iris, oob=FALSE)
  
  # Test object type
  expect_is(gg_dta, "gg_rfsrc")
  
  # Test classification dimensions
  expect_equal(nrow(gg_dta), nrow(rfsrc_iris$predicted))
  expect_equal(ncol(gg_dta), ncol(rfsrc_iris$predicted) + 1)
  
  # Test data is correctly pulled from randomForest obect.
  expect_equivalent(as.matrix(gg_dta[, -which(colnames(gg_dta) == "y")]), 
                    rfsrc_iris$predicted) 
})


test_that("gg_rfsrc survival",{
  ## Load the cached forest
  data(rfsrc_pbc, package="ggRandomForests")
  
  # Test the cached forest type
  expect_is(rfsrc_pbc, "rfsrc")
  
  # Test the forest family
  expect_match(rfsrc_pbc$family, "surv")
  
  ## Create the correct gg_error object
  gg_dta <- gg_rfsrc(rfsrc_pbc)
  
  # Test object type
  expect_is(gg_dta, "gg_rfsrc")
  expect_is(gg_dta, "surv")
  
  # Test classification dimensions
  ## Test plotting the gg_error object
  gg_plt <- plot.gg_rfsrc(gg_dta)
  
  # Test return is s ggplot object
  expect_is(gg_plt, "ggplot")
  
  ## Create the correct gg_error object
  gg_dta <- gg_rfsrc(rfsrc_pbc, oob=FALSE)
  
  # Test object type
  expect_is(gg_dta, "gg_rfsrc")
  expect_is(gg_dta, "surv")
  # Test classification dimensions
  ## Test plotting the gg_error object
  gg_plt <- plot.gg_rfsrc(gg_dta, alpha=.4)
  
  # Test return is s ggplot object
  expect_is(gg_plt, "ggplot")
  
  # Test classification dimensions
  
  gg_dta <- gg_rfsrc(rfsrc_pbc, by="treatment")
  
  # Test object type
  expect_is(gg_dta, "gg_rfsrc")
  expect_is(gg_dta, "surv")
  
  ## Create the correct gg_error object
  gg_plt <- plot(gg_dta)
  
  # Test return is s ggplot object
  expect_is(gg_plt, "ggplot")
  
  
  gg_dta <- gg_rfsrc(rfsrc_pbc,conf.int=.68)
  
  # Test object type
  expect_is(gg_dta, "gg_rfsrc")
  expect_is(gg_dta, "surv")
  
  # Test multiple conf intervals
  gg_dta <- gg_rfsrc(rfsrc_pbc,conf.int=c(.025, .975), bs.sample=100)
  
  # Test object type
  expect_is(gg_dta, "gg_rfsrc")
  expect_is(gg_dta, "surv")
  
  ## Create the correct gg_error object
  gg_plt <- plot(gg_dta)
  
  # Test return is s ggplot object
  expect_is(gg_plt, "ggplot")
  
  # Test prediction
  ## Load the cached forest
  # Predict survival for 106 patients not in randomized trial
  data(rfsrc_pbc_test, package="ggRandomForests")
  # Print prediction summary  
  expect_is(gg_dta <- gg_rfsrc(rfsrc_pbc_test), "gg_rfsrc")
  
  # Test for group "by" name exists
  expect_error(gg_rfsrc(rfsrc_pbc, by="trt"))
  # And it's a vector or factor (not a number)
  expect_error(gg_rfsrc(rfsrc_pbc, by=3))
  
  # Test confidence intervals
  
})

test_that("gg_rfsrc regression",{
  ## Load the cached forest
  data(rfsrc_Boston, package="ggRandomForests")
  
  # Test the cached forest type
  expect_is(rfsrc_Boston, "rfsrc")
  
  # Test the forest family
  expect_match(rfsrc_Boston$family, "regr")
  
  ## Create the correct gg_error object
  gg_dta <- gg_rfsrc(rfsrc_Boston)
  
  # Test object type
  expect_is(gg_dta, "gg_rfsrc")
  expect_is(gg_dta, "regr")
  
  ## Test plotting the gg_error object
  gg_plt <- plot.gg_rfsrc(gg_dta)
  
  # Test return is s ggplot object
  expect_is(gg_plt, "ggplot")
  
  ## Create the correct gg_error object
  gg_dta <- gg_rfsrc(rfsrc_Boston, oob=FALSE)
  
  # Test object type
  expect_is(gg_dta, "gg_rfsrc")
  
  # Test classification dimensions
  ## Create the correct gg_error object
  gg_dta <- gg_rfsrc(rfsrc_Boston, by="chas")
  
  # Test object type
  expect_is(gg_dta, "gg_rfsrc")
  expect_is(gg_dta, "regr")
  
  ## Test plotting the gg_error object
  gg_plt <- plot.gg_rfsrc(gg_dta)
  
  # Test data is correctly pulled from randomForest obect.
  # Predicted values
  rfsrc_Boston$family <- "test"
  expect_error(gg_rfsrc(rfsrc_Boston))
  
  # Test exceptions
  # Is it an rfsrc object?
  expect_error(gg_rfsrc(gg_plt))
  
  # Does it contain the forest?
  rfsrc_Boston$forest <- NULL
  expect_error(gg_rfsrc(rfsrc_Boston))
  
})