Nothing
tests/testthat/test-00-CRAN.R
In RoBMA: Robust Bayesian Meta-Analyses
context("(0) Basic tests for CRAN")
# These are just a very rudimentary tests that don't require time or saved files.
# The full range of tests is run locally.
test_that("Basic functionality works", {
RoBMA:::.load_RoBMA_module()
# fit a default model
d <- c(0.1, 0.2, 0.3)
n <- c(20, 15, 10)
fit_default <- suppressWarnings(RoBMA(d = d, n = n, chains = 1, burnin = 100, sample = 200, autofit = FALSE, seed = 1, algorithm = "ss"))
expect_equal(TRUE, is.RoBMA(fit_default))
expect_equal(
capture_output_lines(fit_default, print = TRUE, width = 150),
c("Call:" ,
"RoBMA(d = d, n = n, algorithm = \"ss\", chains = 1, sample = 200, " ,
" burnin = 100, autofit = FALSE, seed = 1)" ,
"" ,
"Estimates:" ,
" mu tau omega[0,0.025] omega[0.025,0.05] omega[0.05,0.5] omega[0.5,0.95] omega[0.95,0.975] omega[0.975,1] ",
" -0.005022785 0.093402625 1.000000000 0.947454681 0.853829443 0.708481230 0.708800846 0.720853861 ",
" PET PEESE " ,
" 0.025472293 0.027265737 " )
)
expect_equal(
capture_output_lines(summary(fit_default), print = TRUE, width = 150),
c("Call:" ,
"RoBMA(d = d, n = n, algorithm = \"ss\", chains = 1, sample = 200, " ,
" burnin = 100, autofit = FALSE, seed = 1)" ,
"" ,
"Robust Bayesian meta-analysis" ,
"Components summary:" ,
" Prior prob. Post. prob. Inclusion BF" ,
"Effect 0.500 0.285 0.399" ,
"Heterogeneity 0.500 0.490 0.961" ,
"Bias 0.500 0.465 0.869" ,
"" ,
"Model-averaged estimates:" ,
" Mean Median 0.025 0.975" ,
"mu -0.005 0.000 -0.586 0.474" ,
"tau 0.093 0.000 0.000 0.451" ,
"omega[0,0.025] 1.000 1.000 1.000 1.000" ,
"omega[0.025,0.05] 0.947 1.000 0.534 1.000" ,
"omega[0.05,0.5] 0.854 1.000 0.269 1.000" ,
"omega[0.5,0.95] 0.708 1.000 0.017 1.000" ,
"omega[0.95,0.975] 0.709 1.000 0.017 1.000" ,
"omega[0.975,1] 0.721 1.000 0.017 1.000" ,
"PET 0.025 0.000 0.000 0.379" ,
"PEESE 0.027 0.000 0.000 0.016" ,
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).",
"(Estimated publication weights omega correspond to one-sided p-values.)" ,
"\033[0;31mESS 122 is lower than the set target (500).\033[0m" )
)
df_reg <- data.frame(
d = d,
se = se_d(d, n),
mod_con = scale(1:3),
mod_cat = c("A", "A", "B")
)
fit_reg <- suppressWarnings(RoBMA.reg(~ mod_cat + mod_con, data = df_reg, priors_bias = NULL, chains = 1, burnin = 100, sample = 500, autofit = FALSE, seed = 1, algorithm = "ss"))
expect_equal(
capture_output_lines(summary(fit_reg), print = TRUE, width = 150),
c("Call:" ,
"RoBMA.reg(formula = ~mod_cat + mod_con, data = df_reg, priors_bias = NULL, " ,
" algorithm = \"ss\", chains = 1, sample = 500, burnin = 100, " ,
" autofit = FALSE, seed = 1)" ,
"" ,
"Robust Bayesian meta-regression" ,
"Components summary:" ,
" Prior prob. Post. prob. Inclusion BF" ,
"Effect 0.500 0.282 0.393" ,
"Heterogeneity 0.500 0.402 0.672" ,
"" ,
"Meta-regression components summary:" ,
" Prior prob. Post. prob. Inclusion BF" ,
"mod_cat 0.500 0.472 0.894" ,
"mod_con 0.500 0.510 1.041" ,
"" ,
"Model-averaged estimates:" ,
" Mean Median 0.025 0.975" ,
"mu 0.052 0.000 -0.268 0.677" ,
"tau 0.090 0.000 0.000 0.476" ,
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).",
"(Estimated publication weights omega correspond to one-sided p-values.)" ,
"\033[0;31mESS 193 is lower than the set target (500).\033[0m" ,
"" ,
"Model-averaged meta-regression estimates:" ,
" Mean Median 0.025 0.975" ,
"intercept 0.052 0.000 -0.268 0.677" ,
"mod_cat [dif: A] -0.024 0.000 -0.356 0.284" ,
"mod_cat [dif: B] 0.024 0.000 -0.284 0.356" ,
"mod_con 0.009 0.000 -0.327 0.364" ,
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).",
"\033[0;31mESS 193 is lower than the set target (500).\033[0m"
))
})
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context("(0) Basic tests for CRAN")
# These are just a very rudimentary tests that don't require time or saved files.
# The full range of tests is run locally.
test_that("Basic functionality works", {
RoBMA:::.load_RoBMA_module()
# fit a default model
d <- c(0.1, 0.2, 0.3)
n <- c(20, 15, 10)
fit_default <- suppressWarnings(RoBMA(d = d, n = n, chains = 1, burnin = 100, sample = 200, autofit = FALSE, seed = 1, algorithm = "ss"))
expect_equal(TRUE, is.RoBMA(fit_default))
expect_equal(
capture_output_lines(fit_default, print = TRUE, width = 150),
c("Call:" ,
"RoBMA(d = d, n = n, algorithm = \"ss\", chains = 1, sample = 200, " ,
" burnin = 100, autofit = FALSE, seed = 1)" ,
"" ,
"Estimates:" ,
" mu tau omega[0,0.025] omega[0.025,0.05] omega[0.05,0.5] omega[0.5,0.95] omega[0.95,0.975] omega[0.975,1] ",
" -0.005022785 0.093402625 1.000000000 0.947454681 0.853829443 0.708481230 0.708800846 0.720853861 ",
" PET PEESE " ,
" 0.025472293 0.027265737 " )
)
expect_equal(
capture_output_lines(summary(fit_default), print = TRUE, width = 150),
c("Call:" ,
"RoBMA(d = d, n = n, algorithm = \"ss\", chains = 1, sample = 200, " ,
" burnin = 100, autofit = FALSE, seed = 1)" ,
"" ,
"Robust Bayesian meta-analysis" ,
"Components summary:" ,
" Prior prob. Post. prob. Inclusion BF" ,
"Effect 0.500 0.285 0.399" ,
"Heterogeneity 0.500 0.490 0.961" ,
"Bias 0.500 0.465 0.869" ,
"" ,
"Model-averaged estimates:" ,
" Mean Median 0.025 0.975" ,
"mu -0.005 0.000 -0.586 0.474" ,
"tau 0.093 0.000 0.000 0.451" ,
"omega[0,0.025] 1.000 1.000 1.000 1.000" ,
"omega[0.025,0.05] 0.947 1.000 0.534 1.000" ,
"omega[0.05,0.5] 0.854 1.000 0.269 1.000" ,
"omega[0.5,0.95] 0.708 1.000 0.017 1.000" ,
"omega[0.95,0.975] 0.709 1.000 0.017 1.000" ,
"omega[0.975,1] 0.721 1.000 0.017 1.000" ,
"PET 0.025 0.000 0.000 0.379" ,
"PEESE 0.027 0.000 0.000 0.016" ,
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).",
"(Estimated publication weights omega correspond to one-sided p-values.)" ,
"\033[0;31mESS 122 is lower than the set target (500).\033[0m" )
)
df_reg <- data.frame(
d = d,
se = se_d(d, n),
mod_con = scale(1:3),
mod_cat = c("A", "A", "B")
)
fit_reg <- suppressWarnings(RoBMA.reg(~ mod_cat + mod_con, data = df_reg, priors_bias = NULL, chains = 1, burnin = 100, sample = 500, autofit = FALSE, seed = 1, algorithm = "ss"))
expect_equal(
capture_output_lines(summary(fit_reg), print = TRUE, width = 150),
c("Call:" ,
"RoBMA.reg(formula = ~mod_cat + mod_con, data = df_reg, priors_bias = NULL, " ,
" algorithm = \"ss\", chains = 1, sample = 500, burnin = 100, " ,
" autofit = FALSE, seed = 1)" ,
"" ,
"Robust Bayesian meta-regression" ,
"Components summary:" ,
" Prior prob. Post. prob. Inclusion BF" ,
"Effect 0.500 0.282 0.393" ,
"Heterogeneity 0.500 0.402 0.672" ,
"" ,
"Meta-regression components summary:" ,
" Prior prob. Post. prob. Inclusion BF" ,
"mod_cat 0.500 0.472 0.894" ,
"mod_con 0.500 0.510 1.041" ,
"" ,
"Model-averaged estimates:" ,
" Mean Median 0.025 0.975" ,
"mu 0.052 0.000 -0.268 0.677" ,
"tau 0.090 0.000 0.000 0.476" ,
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).",
"(Estimated publication weights omega correspond to one-sided p-values.)" ,
"\033[0;31mESS 193 is lower than the set target (500).\033[0m" ,
"" ,
"Model-averaged meta-regression estimates:" ,
" Mean Median 0.025 0.975" ,
"intercept 0.052 0.000 -0.268 0.677" ,
"mod_cat [dif: A] -0.024 0.000 -0.356 0.284" ,
"mod_cat [dif: B] 0.024 0.000 -0.284 0.356" ,
"mod_con 0.009 0.000 -0.327 0.364" ,
"The estimates are summarized on the Cohen's d scale (priors were specified on the Cohen's d scale).",
"\033[0;31mESS 193 is lower than the set target (500).\033[0m"
))
})
Try the RoBMA package in your browser
Any scripts or data that you put into this service are public.
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.
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