tests/testthat/test-2-methods.R
In FBartos/RoBTT: Robust Bayesian T-Test
context("(2) Print and summary functions")
skip_on_cran()
# the summary tables and print functions are imported from BayesTools and tested henceforth
# test objects - assuming that the fit function worked properly
saved_files <- paste0("fit_", 1:4, ".RDS")
saved_fits <- list()
for(i in seq_along(saved_files)){
saved_fits[[i]] <- readRDS(file = file.path(testthat::test_path("../results/fits"), saved_files[i]))
}
test_that("Print functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
capture_output_lines(saved_fits[[i]], print = TRUE, width = 150),
read.table(file = file.path("../results/print", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
})
test_that("Summary functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
capture_output_lines(summary(saved_fits[[i]]), print = TRUE, width = 150),
read.table(file = file.path("../results/summary", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
# all options
expect_equal(
capture_output_lines(summary(saved_fits[[1]], conditional = TRUE, logBF = TRUE, BF01 = TRUE, group_estimates = TRUE, probs = c(0.10, 0.50, .90)), print = TRUE, width = 150),
c("Call:" ,
"RoBTT(x1 = x1, x2 = x2, parallel = FALSE, seed = 1)" ,
"" ,
"Robust Bayesian t-test" ,
"Components summary:" ,
" Models Prior prob. Post. prob. log(Exclusion BF)" ,
"Effect 4/8 0.500 0.330 0.707" ,
"Heterogeneity 4/8 0.500 0.371 0.526" ,
"Outliers 4/8 0.500 0.548 -0.192" ,
"" ,
"Model-averaged estimates:" ,
" Mean Median 0.1 0.5 0.9" ,
"delta 0.092 0.000 0.000 0.000 0.467" ,
"rho 0.517 0.500 0.414 0.500 0.686" ,
"nu Inf 4.425 2.242 4.425 Inf" ,
"\033[0;31mModel (6): There were 1 divergent transitions.\033[0m",
"" ,
"Model-averaged group parameter estimates:" ,
" Mean Median 0.1 0.5 0.9" ,
"mu[1] 1.691 1.689 0.264 1.689 3.148" ,
"mu[2] 2.035 1.944 0.576 1.944 3.630" ,
"sigma[1] 4.937 3.801 2.479 3.801 7.802" ,
"sigma[2] 5.115 3.946 2.610 3.946 7.920" ,
"\033[0;31mModel (6): There were 1 divergent transitions.\033[0m",
"" ,
"Conditional estimates:" ,
" Mean Median 0.1 0.5 0.9" ,
"delta 0.272 0.233 -0.261 0.233 0.862" ,
"rho 0.549 0.558 0.276 0.558 0.805" ,
"nu 3.054 2.754 2.136 2.754 4.362" ,
"\033[0;31mModel (6): There were 1 divergent transitions.\033[0m"
))
})
test_that("Models summary functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
capture_output_lines(summary(saved_fits[[i]], type = "models"), print = TRUE, width = 150),
read.table(file = file.path("../results/summary.models", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
# test short names & no spikes
expect_equal(
capture_output_lines(summary(saved_fits[[1]], type = "models", short_name = TRUE, remove_spike_0 = TRUE), print = TRUE, width = 150),
c("Call:" ,
"RoBTT(x1 = x1, x2 = x2, parallel = FALSE, seed = 1)" ,
"" ,
"Robust Bayesian t-test" ,
"Models overview:" ,
" Model Distribution Prior delta Prior rho Prior nu Prior prob. log(marglik) Post. prob. Inclusion BF",
" 1 normal S(0.5) 0.125 -24.39 0.175 1.490",
" 2 t S(0.5) E(1) 0.125 -24.07 0.244 2.253",
" 3 normal B(1, 1) 0.125 -24.97 0.099 0.769",
" 4 t B(1, 1) E(1) 0.125 -24.54 0.152 1.253",
" 5 normal C(0, 0.71) S(0.5) 0.125 -24.82 0.115 0.911",
" 6 t C(0, 0.71) S(0.5) E(1) 0.125 -25.01 0.094 0.730",
" 7 normal C(0, 0.71) B(1, 1) 0.125 -25.43 0.062 0.466",
" 8 t C(0, 0.71) B(1, 1) E(1) 0.125 -25.50 0.058 0.432",
"\033[0;31mModel (6): There were 1 divergent transitions.\033[0m"
))
})
test_that("Diagnostics summary functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
capture_output_lines(summary(saved_fits[[i]], type = "diagnostics"), print = TRUE, width = 200),
read.table(file = file.path("../results/summary.diagnostics", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
})
test_that("Individual summary functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
capture_output_lines(summary(saved_fits[[i]], type = "individual"), print = TRUE, width = 150),
read.table(file = file.path("../results/summary.individual", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
})
test_that("Interpret functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
gsub("(.{80})", "\\1\\\n", interpret(saved_fits[[i]])),
read.table(file = file.path("../results/interpret", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
})
#### creating / updating the test settings ####
if(FALSE){
saved_files <- paste0("fit_", 1:4, ".RDS")
saved_fits <- list()
for(i in seq_along(saved_files)){
saved_fits[[i]] <- readRDS(file = file.path("tests/results/fits", saved_files[i]))
}
# generate print files
for(i in seq_along(saved_fits)){
write.table(capture_output_lines(saved_fits[[i]], print = TRUE, width = 150), file = file.path("tests/results/print", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
# generate summary files
for(i in seq_along(saved_fits)){
write.table(capture_output_lines(summary(saved_fits[[i]]), print = TRUE, width = 150), file = file.path("tests/results/summary", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
# generate summary.models files
for(i in seq_along(saved_fits)){
write.table(capture_output_lines(summary(saved_fits[[i]], type = "models"), print = TRUE, width = 150), file = file.path("tests/results/summary.models", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
# generate summary.diagnostics files
for(i in seq_along(saved_fits)){
write.table(capture_output_lines(summary(saved_fits[[i]], type = "diagnostics"), print = TRUE, width = 200), file = file.path("tests/results/summary.diagnostics", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
# generate summary.individual files
for(i in seq_along(saved_fits)){
write.table(capture_output_lines(summary(saved_fits[[i]], type = "individual"), print = TRUE, width = 150), file = file.path("tests/results/summary.individual", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
# generate summary.individual files
for(i in seq_along(saved_fits)){
write.table(gsub("(.{80})", "\\1\\\n", interpret(saved_fits[[i]])), file = file.path("tests/results/interpret", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
}
FBartos/RoBTT documentation built on Nov. 14, 2024, 6:42 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
context("(2) Print and summary functions")
skip_on_cran()
# the summary tables and print functions are imported from BayesTools and tested henceforth
# test objects - assuming that the fit function worked properly
saved_files <- paste0("fit_", 1:4, ".RDS")
saved_fits <- list()
for(i in seq_along(saved_files)){
saved_fits[[i]] <- readRDS(file = file.path(testthat::test_path("../results/fits"), saved_files[i]))
}
test_that("Print functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
capture_output_lines(saved_fits[[i]], print = TRUE, width = 150),
read.table(file = file.path("../results/print", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
})
test_that("Summary functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
capture_output_lines(summary(saved_fits[[i]]), print = TRUE, width = 150),
read.table(file = file.path("../results/summary", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
# all options
expect_equal(
capture_output_lines(summary(saved_fits[[1]], conditional = TRUE, logBF = TRUE, BF01 = TRUE, group_estimates = TRUE, probs = c(0.10, 0.50, .90)), print = TRUE, width = 150),
c("Call:" ,
"RoBTT(x1 = x1, x2 = x2, parallel = FALSE, seed = 1)" ,
"" ,
"Robust Bayesian t-test" ,
"Components summary:" ,
" Models Prior prob. Post. prob. log(Exclusion BF)" ,
"Effect 4/8 0.500 0.330 0.707" ,
"Heterogeneity 4/8 0.500 0.371 0.526" ,
"Outliers 4/8 0.500 0.548 -0.192" ,
"" ,
"Model-averaged estimates:" ,
" Mean Median 0.1 0.5 0.9" ,
"delta 0.092 0.000 0.000 0.000 0.467" ,
"rho 0.517 0.500 0.414 0.500 0.686" ,
"nu Inf 4.425 2.242 4.425 Inf" ,
"\033[0;31mModel (6): There were 1 divergent transitions.\033[0m",
"" ,
"Model-averaged group parameter estimates:" ,
" Mean Median 0.1 0.5 0.9" ,
"mu[1] 1.691 1.689 0.264 1.689 3.148" ,
"mu[2] 2.035 1.944 0.576 1.944 3.630" ,
"sigma[1] 4.937 3.801 2.479 3.801 7.802" ,
"sigma[2] 5.115 3.946 2.610 3.946 7.920" ,
"\033[0;31mModel (6): There were 1 divergent transitions.\033[0m",
"" ,
"Conditional estimates:" ,
" Mean Median 0.1 0.5 0.9" ,
"delta 0.272 0.233 -0.261 0.233 0.862" ,
"rho 0.549 0.558 0.276 0.558 0.805" ,
"nu 3.054 2.754 2.136 2.754 4.362" ,
"\033[0;31mModel (6): There were 1 divergent transitions.\033[0m"
))
})
test_that("Models summary functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
capture_output_lines(summary(saved_fits[[i]], type = "models"), print = TRUE, width = 150),
read.table(file = file.path("../results/summary.models", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
# test short names & no spikes
expect_equal(
capture_output_lines(summary(saved_fits[[1]], type = "models", short_name = TRUE, remove_spike_0 = TRUE), print = TRUE, width = 150),
c("Call:" ,
"RoBTT(x1 = x1, x2 = x2, parallel = FALSE, seed = 1)" ,
"" ,
"Robust Bayesian t-test" ,
"Models overview:" ,
" Model Distribution Prior delta Prior rho Prior nu Prior prob. log(marglik) Post. prob. Inclusion BF",
" 1 normal S(0.5) 0.125 -24.39 0.175 1.490",
" 2 t S(0.5) E(1) 0.125 -24.07 0.244 2.253",
" 3 normal B(1, 1) 0.125 -24.97 0.099 0.769",
" 4 t B(1, 1) E(1) 0.125 -24.54 0.152 1.253",
" 5 normal C(0, 0.71) S(0.5) 0.125 -24.82 0.115 0.911",
" 6 t C(0, 0.71) S(0.5) E(1) 0.125 -25.01 0.094 0.730",
" 7 normal C(0, 0.71) B(1, 1) 0.125 -25.43 0.062 0.466",
" 8 t C(0, 0.71) B(1, 1) E(1) 0.125 -25.50 0.058 0.432",
"\033[0;31mModel (6): There were 1 divergent transitions.\033[0m"
))
})
test_that("Diagnostics summary functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
capture_output_lines(summary(saved_fits[[i]], type = "diagnostics"), print = TRUE, width = 200),
read.table(file = file.path("../results/summary.diagnostics", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
})
test_that("Individual summary functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
capture_output_lines(summary(saved_fits[[i]], type = "individual"), print = TRUE, width = 150),
read.table(file = file.path("../results/summary.individual", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
})
test_that("Interpret functions work", {
# testing consistency across all model specifications
for(i in 1:length(saved_fits)){
expect_equal(
gsub("(.{80})", "\\1\\\n", interpret(saved_fits[[i]])),
read.table(file = file.path("../results/interpret", paste0(i, ".txt")), header = FALSE, blank.lines.skip = FALSE)[,1])
}
})
#### creating / updating the test settings ####
if(FALSE){
saved_files <- paste0("fit_", 1:4, ".RDS")
saved_fits <- list()
for(i in seq_along(saved_files)){
saved_fits[[i]] <- readRDS(file = file.path("tests/results/fits", saved_files[i]))
}
# generate print files
for(i in seq_along(saved_fits)){
write.table(capture_output_lines(saved_fits[[i]], print = TRUE, width = 150), file = file.path("tests/results/print", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
# generate summary files
for(i in seq_along(saved_fits)){
write.table(capture_output_lines(summary(saved_fits[[i]]), print = TRUE, width = 150), file = file.path("tests/results/summary", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
# generate summary.models files
for(i in seq_along(saved_fits)){
write.table(capture_output_lines(summary(saved_fits[[i]], type = "models"), print = TRUE, width = 150), file = file.path("tests/results/summary.models", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
# generate summary.diagnostics files
for(i in seq_along(saved_fits)){
write.table(capture_output_lines(summary(saved_fits[[i]], type = "diagnostics"), print = TRUE, width = 200), file = file.path("tests/results/summary.diagnostics", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
# generate summary.individual files
for(i in seq_along(saved_fits)){
write.table(capture_output_lines(summary(saved_fits[[i]], type = "individual"), print = TRUE, width = 150), file = file.path("tests/results/summary.individual", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
# generate summary.individual files
for(i in seq_along(saved_fits)){
write.table(gsub("(.{80})", "\\1\\\n", interpret(saved_fits[[i]])), file = file.path("tests/results/interpret", paste0(i, ".txt")), row.names = FALSE, col.names = FALSE)
}
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.