Nothing
test_that("2x2 correlation matrix generated correctly", {
#2 by 2 matrix
dist_rmc_mat_2by2 <- rmcorr_mat(participant = Subject,
variables = c("Blindwalk Away",
"Blindwalk Toward"),
dataset = twedt_dist_measures)
#Dimensions
expect_identical(dim(dist_rmc_mat_2by2$matrix), c(2L,2L))
#Transpose
expect_identical(dist_rmc_mat_2by2$matrix,
t(dist_rmc_mat_2by2$matrix))
})
test_that("5x5 correlation matrix generated correctly", {
dist_rmc_mat <- rmcorr_mat(participant = Subject,
variables = c("Blindwalk Away",
"Blindwalk Toward",
"Triangulated BW",
"Verbal",
"Visual matching"),
dataset = twedt_dist_measures)
#check class
expect_s3_class(dist_rmc_mat, 'rmcmat')
#Check dimensions
identical(dim(dist_rmc_mat$matrix), c(5L,5L))
#Check transpose of output rmcorr matrix too
identical(dist_rmc_mat$matrix,
t(dist_rmc_mat$matrix))
#Expected output for correlation matrix to reasonable number of decimal places
expected <- c(1.00000000, 0.8065821, 0.2382857, 0.7355813, 0.7758245,
0.8065821, 1.0000000, 0.2254866, 0.7160551, 0.7575109,
0.2382857, 0.2254866, 1.0000000, 0.1835838, 0.2537431,
0.7355813, 0.7160551, 0.1835838, 1.0000000, 0.7341831,
0.7758245, 0.7575109, 0.2537431, 0.7341831, 1.0000000)
expected.matrix <- matrix(expected,
byrow = FALSE,
nrow = 5, ncol = 5)
expected.mat.row.col.names <- c("Blindwalk Away",
"Blindwalk Toward",
"Triangulated BW",
"Verbal",
"Visual matching")
rownames(expected.matrix) <- expected.mat.row.col.names
colnames(expected.matrix) <- expected.mat.row.col.names
expect_equal(dist_rmc_mat$matrix, expected.matrix, tolerance = 0.00001)
})
test_that('rmcorr_mat model matches single rmcorr model', {
#rmcorr_mat
dist_rmc_mat <- rmcorr_mat(participant = Subject,
variables = c("Blindwalk Away",
"Blindwalk Toward",
"Triangulated BW",
"Verbal",
"Visual matching"),
dataset = twedt_dist_measures)
#single rmcorr model
expect_warning(bwa.tribw.rmc <- rmcorr(Subject,
"Blindwalk Away",
"Triangulated BW",
twedt_dist_measures),
regexp = "coerced into a factor")
#Expected output for rmcorr_mat model and single rmcorr model, run separately
expect_identical(bwa.tribw.rmc$r, dist_rmc_mat$models[[2]]$r)
expect_identical(bwa.tribw.rmc$df, dist_rmc_mat$models[[2]]$df)
expect_identical(bwa.tribw.rmc$p, dist_rmc_mat$models[[2]]$p)
expect_identical(bwa.tribw.rmc$CI, dist_rmc_mat$models[[2]]$CI)
#check that variable names are the same
expect_identical(bwa.tribw.rmc$vars[2:3], dist_rmc_mat$models[[2]]$vars[2:3])
#dist_rmc_mat uses "Participant" regardless of the input variable
expect_identical(dist_rmc_mat$models[[2]]$vars[1], "Participant")
})
test_that('rmcorr_mat summary matches single rmcorr model', {
#rmcorr_mat
dist_rmc_mat <- rmcorr_mat(participant = Subject,
variables = c("Blindwalk Away",
"Blindwalk Toward",
"Triangulated BW",
"Verbal",
"Visual matching"),
dataset = twedt_dist_measures)
#single rmcorr model
expect_warning(bwt.vis.rmc <- rmcorr(Subject,
"Blindwalk Toward",
"Visual matching",
twedt_dist_measures),
"coerced into a factor")
#Expected output for rmcorr_mat summary and single rmcorr model, run separately
expect_identical(dist_rmc_mat$summary[7,]$rmcorr.r, bwt.vis.rmc$r)
expect_identical(dist_rmc_mat$summary[7,]$df, bwt.vis.rmc$df)
expect_identical(dist_rmc_mat$summary[7,]$p.vals, bwt.vis.rmc$p)
})
test_that("Test class of output",{
dist_rmc_mat <- rmcorr_mat(participant = Subject,
variables = c("Blindwalk Away",
"Blindwalk Toward",
"Triangulated BW",
"Verbal",
"Visual matching"),
dataset = twedt_dist_measures)
expect_match(class(dist_rmc_mat), "rmcmat")
expect_snapshot(print(dist_rmc_mat))
})
test_that("Participant variable can be entered as a string", {
expect_silent(rmcorr_mat(participant = "Subject",
variables = c("Blindwalk Away",
"Blindwalk Toward",
"Triangulated BW",
"Verbal",
"Visual matching"),
dataset = twedt_dist_measures))
})
test_that("CI level input gets checked", {
expect_error(rmcorr_mat(participant = "Subject",
variables = c("Blindwalk Away",
"Blindwalk Toward",
"Triangulated BW",
"Verbal",
"Visual matching"),
CI.level = 95,
dataset = twedt_dist_measures))
})
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