skip_on_cran() skip_on_os(c("mac", "solaris")) skip_if_not_installed("haven") skip_if_not_installed("datawizard") test_that("ggpredict, print", { # lm, linear regression ---- 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) expect_message({ junk <- capture.output(print(ggpredict(fit, terms = "c12hour"))) }) expect_silent({ junk <- capture.output(print(ggpredict(fit, terms = "c12hour"), n = Inf)) }) ggpredict(fit, terms = "c172code") ggpredict(fit, terms = "c161sex") ggpredict(fit, terms = c("c12hour", "c172code")) ggpredict(fit, terms = c("c12hour", "c161sex")) ggpredict(fit, terms = c("e42dep", "c161sex")) ggpredict(fit, terms = c("e42dep", "c172code")) ggpredict(fit, terms = c("c12hour", "c172code", "c161sex")) ggpredict(fit, terms = c("e42dep", "c172code", "c161sex")) ggpredict(fit, terms = c("c12hour", "c172code", "e42dep")) ggpredict(fit, terms = c("c161sex", "c172code", "e42dep")) ggpredict(fit, terms = c("c12hour", "neg_c_7")) ggpredict(fit, terms = c("c12hour", "neg_c_7 [all]")) ggpredict(fit, terms = c("c12hour", "neg_c_7 [quart2]")) ggpredict(fit, terms = c("c12hour", "neg_c_7 [quart2]", "c161sex")) ggpredict(fit, terms = c("c12hour", "neg_c_7", "c161sex")) expect_snapshot(print(ggpredict(fit, terms = c("c12hour", "neg_c_7", "c161sex")))) expect_snapshot(print(ggpredict(fit, terms = c("c12hour", "neg_c_7", "c161sex")), n = Inf)) out <- utils::capture.output(ggpredict(fit, terms = c("c12hour", "neg_c_7 [quart2]", "c82cop1"))) expect_equal( out, c( "# Predicted values of Total score BARTHEL INDEX", "", "neg_c_7: 9", "c82cop1: 1", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 93.03 | 85.95, 100.11", " 45 | 89.98 | 82.82, 97.14", " 85 | 87.27 | 79.87, 94.68", " 170 | 81.52 | 73.11, 89.92", "", "neg_c_7: 9", "c82cop1: 2", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 92.44 | 86.73, 98.15", " 45 | 89.40 | 83.62, 95.18", " 85 | 86.69 | 80.63, 92.74", " 170 | 80.93 | 73.74, 88.13", "", "neg_c_7: 9", "c82cop1: 3", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 91.86 | 86.87, 96.85", " 45 | 88.81 | 83.78, 93.85", " 85 | 86.10 | 80.78, 91.43", " 170 | 80.35 | 73.81, 86.89", "", "neg_c_7: 9", "c82cop1: 4", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 91.28 | 86.07, 96.48", " 45 | 88.23 | 83.02, 93.44", " 85 | 85.52 | 80.06, 90.98", " 170 | 79.76 | 73.16, 86.37", "", "neg_c_7: 11", "c82cop1: 1", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 90.02 | 83.03, 97.01", " 45 | 86.98 | 79.92, 94.03", " 85 | 84.27 | 76.98, 91.56", " 170 | 78.51 | 70.24, 86.78", "", "neg_c_7: 11", "c82cop1: 2", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 89.44 | 83.70, 95.18", " 45 | 86.39 | 80.61, 92.18", " 85 | 83.68 | 77.64, 89.73", " 170 | 77.93 | 70.77, 85.08", "", "neg_c_7: 11", "c82cop1: 3", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 88.86 | 83.67, 94.04", " 45 | 85.81 | 80.61, 91.01", " 85 | 83.10 | 77.64, 88.56", " 170 | 77.34 | 70.72, 83.96", "", "neg_c_7: 11", "c82cop1: 4", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 88.27 | 82.74, 93.80", " 45 | 85.22 | 79.70, 90.74", " 85 | 82.52 | 76.78, 88.25", " 170 | 76.76 | 69.96, 83.56", "", "neg_c_7: 14", "c82cop1: 1", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 95.03 | 87.81, 102.26", " 45 | 91.98 | 84.67, 99.30", " 85 | 89.28 | 81.71, 96.84", " 170 | 83.52 | 74.96, 92.08", "", "neg_c_7: 14", "c82cop1: 2", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 94.45 | 88.65, 100.24", " 45 | 91.40 | 85.52, 97.28", " 85 | 88.69 | 82.53, 94.86", " 170 | 82.93 | 75.63, 90.24", "", "neg_c_7: 14", "c82cop1: 3", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 93.86 | 88.88, 98.85", " 45 | 90.82 | 85.77, 95.86", " 85 | 88.11 | 82.76, 93.46", " 170 | 82.35 | 75.76, 88.94", "", "neg_c_7: 14", "c82cop1: 4", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 93.28 | 88.18, 98.37", " 45 | 90.23 | 85.11, 95.35", " 85 | 87.52 | 82.13, 92.91", " 170 | 81.77 | 75.20, 88.34", "", "Adjusted for:", "* e42dep = independent", "* c161sex = 1.76", "* c172code = low level of education" ) , ignore_attr = TRUE ) out <- utils::capture.output(ggpredict(fit, terms = c("c12hour", "neg_c_7", "c82cop1"))) expect_equal( out, c( "# Predicted values of Total score BARTHEL INDEX", "", "neg_c_7: 8", "c82cop1: 1", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 92.23 | 85.19, 99.27", " 45 | 89.18 | 82.06, 96.29", " 85 | 86.47 | 79.11, 93.83", " 170 | 80.71 | 72.36, 89.07", "", "neg_c_7: 8", "c82cop1: 2", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 91.64 | 85.94, 97.34", " 45 | 88.60 | 82.83, 94.36", " 85 | 85.89 | 79.85, 91.92", " 170 | 80.13 | 72.96, 87.30", "", "neg_c_7: 8", "c82cop1: 3", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 91.06 | 86.04, 96.08", " 45 | 88.01 | 82.95, 93.07", " 85 | 85.30 | 79.96, 90.64", " 170 | 79.55 | 73.00, 86.09", "", "neg_c_7: 8", "c82cop1: 4", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 90.47 | 85.20, 95.75", " 45 | 87.43 | 82.15, 92.70", " 85 | 84.72 | 79.20, 90.24", " 170 | 78.96 | 72.32, 85.60", "", "neg_c_7: 11.8", "c82cop1: 1", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 88.32 | 81.31, 95.33", " 45 | 85.27 | 78.21, 92.34", " 85 | 82.57 | 75.28, 89.85", " 170 | 76.81 | 68.55, 85.06", "", "neg_c_7: 11.8", "c82cop1: 2", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 87.74 | 81.90, 93.58", " 45 | 84.69 | 78.81, 90.57", " 85 | 81.98 | 75.86, 88.10", " 170 | 76.22 | 69.02, 83.42", "", "neg_c_7: 11.8", "c82cop1: 3", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 87.15 | 81.77, 92.53", " 45 | 84.11 | 78.72, 89.49", " 85 | 81.40 | 75.77, 87.02", " 170 | 75.64 | 68.90, 82.38", "", "neg_c_7: 11.8", "c82cop1: 4", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 86.57 | 80.77, 92.36", " 45 | 83.52 | 77.75, 89.29", " 85 | 80.81 | 74.84, 86.78", " 170 | 75.06 | 68.08, 82.04", "", "neg_c_7: 15.7", "c82cop1: 1", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 96.03 | 88.71, 103.35", " 45 | 92.99 | 85.57, 100.40", " 85 | 90.28 | 82.61, 97.95", " 170 | 84.52 | 75.86, 93.18", "", "neg_c_7: 15.7", "c82cop1: 2", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 95.45 | 89.58, 101.32", " 45 | 92.40 | 86.44, 98.36", " 85 | 89.69 | 83.44, 95.94", " 170 | 83.94 | 76.55, 91.33", "", "neg_c_7: 15.7", "c82cop1: 3", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 94.86 | 89.84, 99.89", " 45 | 91.82 | 86.73, 96.91", " 85 | 89.11 | 83.71, 94.50", " 170 | 83.35 | 76.72, 89.99", "", "neg_c_7: 15.7", "c82cop1: 4", "", "c12hour | Predicted | 95% CI", "-----------------------------------", " 0 | 94.28 | 89.20, 99.36", " 45 | 91.23 | 86.12, 96.34", " 85 | 88.52 | 83.14, 93.91", " 170 | 82.77 | 76.19, 89.35", "", "Adjusted for:", "* e42dep = independent", "* c161sex = 1.76", "* c172code = low level of education" ) , ignore_attr = TRUE ) out <- ggpredict(fit, terms = c("c161sex", "c172code", "e42dep")) expect_snapshot(print(out, group_name = TRUE)) expect_snapshot(print(out, group_name = FALSE)) }) test_that("ggpredict, print factors", { LEV <- c( "climate", "cutwelfare", "discipline", "freedom", "ineqincOK", "leader", "police", "politduty", "refugees", "Russia", "taxesdown", "worse-off" ) n <- 100 set.seed(1) data <- data.frame( bin_choice = sample(c(0, 1), size = n, replace = TRUE), Wshort = factor(sample(LEV, size = n, replace = TRUE), levels = LEV) ) model.contcons <- glm(bin_choice ~ Wshort, data = data, family = binomial()) pr <- ggemmeans(model.contcons, "Wshort [all]") expect_snapshot(print(pr)) pr <- ggemmeans(model.contcons, "Wshort") expect_snapshot(print(pr)) }) test_that("ggpredict, collapse CI", { data(efc, package = "ggeffects") efc <- datawizard::to_factor(efc, c("c172code", "c161sex", "e42dep")) fit <- lm(barthtot ~ c161sex * c172code * e42dep + c160age, data = efc) pr <- suppressWarnings(ggpredict(fit, terms = c("c161sex", "c172code", "e42dep"))) expect_snapshot(print(pr)) expect_snapshot(print(pr, group_name = FALSE)) expect_snapshot(print(pr, group_name = FALSE, collapse_ci = TRUE)) expect_snapshot(print(pr, group_name = FALSE, collapse_ci = TRUE, ci_brackets = c("[", "]"))) expect_snapshot(print(pr, group_name = TRUE, collapse_ci = TRUE, ci_brackets = c("[", "]"))) fit <- lm(barthtot ~ e42dep + c160age, data = efc) pr <- ggpredict(fit, terms = "e42dep") expect_snapshot(print(pr, group_name = FALSE, collapse_ci = TRUE)) }) test_that("ggpredict, collapse tables", { data(iris) m <- lm(Sepal.Length ~ Species * Petal.Length, data = iris) expect_snapshot(print(ggpredict(m, c("Petal.Length", "Species")), collapse_tables = TRUE, n = 3)) })