skip_on_cran() skip_on_os(c("mac", "solaris")) skip_if_not_installed("datawizard") skip_if_not_installed("sjlabelled") test_that("ggpredict, format", { data(efc, package = "ggeffects") efc$c172code <- datawizard::to_factor(efc$c172code) efc$e42dep <- datawizard::to_factor(efc$e42dep) efc$c82cop1 <- as.numeric(efc$c82cop1) fit <- lm(barthtot ~ c12hour + neg_c_7 + c82cop1 + e42dep + c161sex + c172code, data = efc) pr <- ggpredict(fit, terms = "neg_c_7 [quart2]") out <- format(pr) expect_identical(dim(out), c(3L, 3L)) expect_identical(out[["95% CI"]], c("85.95, 95.93", "83.95, 93.93", "80.75, 91.12")) pr <- ggpredict(fit, terms = "c161sex") out <- format(pr, value_labels = TRUE) expect_identical(out$c161sex, c("[1] Male ", "[2] Female")) out <- format(pr, variable_labels = TRUE) expect_identical(colnames(out)[2], "Predicted values of Total score BARTHEL INDEX") fit <- lm(barthtot ~ c161sex * c172code, data = efc) pr <- ggpredict(fit, c("c161sex", "c172code")) out <- format(pr) expect_identical( out$groups, c( "low level of education", "low level of education", "intermediate level of education", "intermediate level of education", "high level of education", "high level of education" ) ) out <- format(pr, group_name = TRUE) expect_identical( out$groups, c( "c172code: low level of education", "c172code: low level of education", "c172code: intermediate level of education", "c172code: intermediate level of education", "c172code: high level of education", "c172code: high level of education" ) ) pr <- ggpredict(fit, terms = "c161sex") out <- format(pr) expect_identical(out[["95% CI"]], c("55.21, 73.33", "54.65, 64.67")) out <- format(pr, ci_brackets = c("[", "]")) expect_identical(out[["95% CI"]], c("[55.21, 73.33]", "[54.65, 64.67]")) }) test_that("format, collapse tables", { data(iris) m <- lm(Sepal.Length ~ Species * Petal.Length, data = iris) pr <- ggpredict(m, c("Petal.Length", "Species")) out <- format(pr, collapse_tables = TRUE, n = 3) expect_named(out, c("Petal.Length", "Species", "Predicted", "95% CI")) expect_identical(out$Species, c("setosa", "", "", "versicolor", "", "", "virginica", "", "")) }) skip_if_not_installed("withr") withr::with_options( list(ggeffects_ci_brackets = c("(", ")")), test_that("ggpredict, parenthesis-option", { data(efc, package = "ggeffects") fit <- lm(barthtot ~ c161sex, data = efc) pr <- ggpredict(fit, terms = "c161sex") out <- format(pr) expect_identical(out[["95% CI"]], c("(62.86, 70.86)", "(61.54, 66.02)")) }) )