if (suppressWarnings( requiet("testthat") && requiet("ggeffects") && requiet("sjlabelled") && requiet("emmeans") && requiet("effects") && requiet("haven") && requiet("sjmisc") )) { # lm, linear regression ---- data(efc, package = "ggeffects") fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc) test_that("validate ggpredict lm against predict", { nd <- data_grid(fit, "c12hour [10, 50, 100]") pr <- predict(fit, newdata = nd, se.fit = TRUE) expected <- pr$fit + stats::qt(0.975, df.residual(fit)) * pr$se.fit predicted <- ggpredict(fit, "c12hour [10, 50, 100]") expect_equal(predicted$conf.high, expected, tolerance = 1e-3, ignore_attr = TRUE) expect_equal(predicted$predicted, pr$fit, tolerance = 1e-3, ignore_attr = TRUE) }) test_that("ggpredict, lm", { expect_s3_class(ggpredict(fit, "c12hour"), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex")), "data.frame") }) test_that("ggpredict, lm print", { x <- ggpredict(fit, c("c12hour", "c161sex", "c172code")) out <- utils::capture.output(print(x)) expect_identical( out, c( "# Predicted values of Total score BARTHEL INDEX", "", "# c161sex = Male", "# c172code = [1] low level of education", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 73.95 | [69.35, 78.56]", " 45 | 62.56 | [58.22, 66.89]", " 85 | 52.42 | [47.89, 56.96]", " 170 | 30.89 | [24.84, 36.95]", "", "# c161sex = Female", "# c172code = [1] low level of education", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 75.00 | [71.40, 78.59]", " 45 | 63.60 | [60.45, 66.74]", " 85 | 53.46 | [50.12, 56.80]", " 170 | 31.93 | [26.82, 37.05]", "", "# c161sex = Male", "# c172code = [2] intermediate level of education", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 74.67 | [71.05, 78.29]", " 45 | 63.27 | [59.88, 66.67]", " 85 | 53.14 | [49.39, 56.89]", " 170 | 31.61 | [25.97, 37.25]", "", "# c161sex = Female", "# c172code = [2] intermediate level of education", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 75.71 | [73.31, 78.12]", " 45 | 64.32 | [62.41, 66.22]", " 85 | 54.18 | [51.81, 56.56]", " 170 | 32.65 | [27.94, 37.37]", "", "# c161sex = Male", "# c172code = [3] high level of education", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 75.39 | [71.03, 79.75]", " 45 | 63.99 | [59.72, 68.26]", " 85 | 53.86 | [49.22, 58.50]", " 170 | 32.33 | [25.94, 38.72]", "", "# c161sex = Female", "# c172code = [3] high level of education", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 76.43 | [72.88, 79.98]", " 45 | 65.03 | [61.67, 68.39]", " 85 | 54.90 | [51.15, 58.65]", " 170 | 33.37 | [27.69, 39.05]", "", "Adjusted for:", "* neg_c_7 = 11.84" ) ) x <- ggpredict(fit, c("c12hour", "c161sex", "neg_c_7")) out <- utils::capture.output(print(x)) expect_identical( out, c( "# Predicted values of Total score BARTHEL INDEX", "", "# c161sex = Male", "# neg_c_7 = 11.8", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 74.74 | [71.11, 78.36]", " 45 | 63.34 | [59.94, 66.74]", " 85 | 53.21 | [49.46, 56.96]", " 170 | 31.68 | [26.04, 37.31]", "", "# c161sex = Female", "# neg_c_7 = 11.8", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 75.78 | [73.38, 78.19]", " 45 | 64.38 | [62.48, 66.28]", " 85 | 54.25 | [51.88, 56.62]", " 170 | 32.72 | [28.01, 37.43]", "", "# c161sex = Male", "# neg_c_7 = 15.7", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 65.78 | [61.53, 70.03]", " 45 | 54.38 | [50.49, 58.27]", " 85 | 44.25 | [40.20, 48.30]", " 170 | 22.72 | [17.10, 28.33]", "", "# c161sex = Female", "# neg_c_7 = 15.7", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 66.82 | [63.70, 69.94]", " 45 | 55.42 | [52.93, 57.91]", " 85 | 45.29 | [42.65, 47.94]", " 170 | 23.76 | [19.17, 28.34]", "", "# c161sex = Male", "# neg_c_7 = 8", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 83.47 | [79.72, 87.22]", " 45 | 72.07 | [68.36, 75.78]", " 85 | 61.94 | [57.76, 66.12]", " 170 | 40.41 | [34.27, 46.55]", "", "# c161sex = Female", "# neg_c_7 = 8", "", "c12hour | Predicted | 95% CI", "------------------------------------", " 0 | 84.51 | [81.74, 87.27]", " 45 | 73.11 | [70.51, 75.72]", " 85 | 62.98 | [59.82, 66.14]", " 170 | 41.45 | [36.06, 46.84]", "", "Adjusted for:", "* c172code = 1.97" ) ) }) test_that("ggpredict, lm by", { expect_identical(nrow(ggpredict(fit, "c12hour [10:20]")), 11L) expect_identical(nrow(ggpredict(fit, "c12hour [10:20 by=.2]")), 51L) expect_identical(nrow(ggpredict(fit, "c12hour [10:20 by = .2]")), 51L) expect_identical(nrow(ggpredict(fit, "c12hour [10:20by=.2]")), 51L) }) test_that("ggpredict, lm-vcov", { expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), vcov_fun = "vcovHC", vcov_type = "HC1"), "data.frame") }) test_that("ggpredict, lm-prediction-interval", { pr <- ggpredict(fit, c("c12hour", "c161sex"), interval = "predict") expect_equal(pr$conf.low[1], 27.36046, tolerance = 1e-4) pr <- ggpredict(fit, c("c12hour", "c161sex"), interval = "conf") expect_equal(pr$conf.low[1], 71.0235294, tolerance = 1e-4) pr <- ggpredict(fit, c("c12hour", "c161sex"), interval = "predict", vcov_fun = "vcovHC", vcov_type = "HC1") expect_equal(pr$conf.low[1], 27.37019, tolerance = 1e-4) expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), interval = "predict", ci_level = NA), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), interval = "conf", ci_level = NA), "data.frame") }) test_that("ggpredict, lm-noci", { expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), ci_level = NA), "data.frame") }) test_that("ggpredict, lm, ci_level", { expect_s3_class(ggpredict(fit, "c12hour", ci_level = 0.8), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), ci_level = 0.8), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code"), ci_level = 0.8), "data.frame") }) test_that("ggpredict, lm, typical", { expect_s3_class(ggpredict(fit, "c12hour", ci_level = 0.8, typical = "median"), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), ci_level = 0.8, typical = "median"), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex", "c172code"), ci_level = 0.8, typical = "median"), "data.frame") }) test_that("ggpredict, lm, condition", { expect_s3_class(ggpredict(fit, "c172code", condition = c(c12hour = 40), ci_level = 0.8, typical = "median"), "data.frame") expect_s3_class(ggpredict(fit, c("c172code", "c161sex"), condition = c(c12hour = 40), ci_level = 0.8, typical = "median"), "data.frame") }) test_that("ggpredict, lm, pretty", { expect_s3_class(ggpredict(fit, "c12hour", full.data = TRUE, ci_level = 0.8, typical = "median"), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c161sex"), full.data = TRUE, ci_level = 0.8, typical = "median"), "data.frame") }) test_that("ggpredict, lm, full.data", { expect_s3_class(ggpredict(fit, "c172code", full.data = TRUE, ci_level = 0.8, typical = "median"), "data.frame") expect_s3_class(ggpredict(fit, c("c172code", "c161sex"), full.data = TRUE, ci_level = 0.8, typical = "median"), "data.frame") }) test_that("ggeffect, lm", { expect_s3_class(ggeffect(fit, "c12hour"), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour", "c161sex")), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour", "c161sex", "c172code")), "data.frame") }) test_that("ggemmeans, lm", { expect_s3_class(ggemmeans(fit, "c12hour"), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex")), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex", "c172code")), "data.frame") }) test_that("ggemmeans, lm, ci_level", { expect_s3_class(ggemmeans(fit, "c12hour", ci_level = 0.8), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex"), ci_level = 0.8), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex", "c172code"), ci_level = 0.8), "data.frame") }) test_that("ggemmeans, lm, typical", { expect_s3_class(ggemmeans(fit, "c12hour", ci_level = 0.8, typical = "median"), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex"), ci_level = 0.8, typical = "median"), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex", "c172code"), ci_level = 0.8, typical = "median"), "data.frame") }) test_that("ggemmeans, lm, condition", { expect_s3_class(ggemmeans(fit, "c172code", condition = c(c12hour = 40), ci_level = 0.8, typical = "median"), "data.frame") expect_s3_class(ggemmeans(fit, c("c172code", "c161sex"), condition = c(c12hour = 40), ci_level = 0.8, typical = "median"), "data.frame") }) test_that("ggemmeans, lm, pretty", { expect_s3_class(ggemmeans(fit, "c12hour", full.data = TRUE, ci_level = 0.8, typical = "median"), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c161sex"), full.data = TRUE, ci_level = 0.8, typical = "median"), "data.frame") }) data(efc, package = "ggeffects") efc$c172code <- sjlabelled::to_label(efc$c172code) fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc) test_that("ggpredict, lm", { expect_s3_class(ggpredict(fit, "c12hour [20,30,40]"), "data.frame") expect_s3_class(ggpredict(fit, "c12hour [30:60]"), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour [30:60]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") }) test_that("ggpredict, lm", { expect_s3_class(ggpredict(fit, "c12hour [meansd]"), "data.frame") expect_s3_class(ggpredict(fit, "c12hour [minmax]"), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour [quart]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour [zeromax]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour [quart2]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") }) test_that("ggeffect, lm", { expect_s3_class(ggeffect(fit, "c12hour [20,30,40]"), "data.frame") expect_s3_class(ggeffect(fit, "c12hour [30:60]"), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour [30:60]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") }) test_that("ggeffect, lm", { expect_s3_class(ggeffect(fit, "c12hour [meansd]"), "data.frame") expect_s3_class(ggeffect(fit, "c12hour [minmax]"), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour [quart]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour [zeromax]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour [quart2]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") }) test_that("ggemmeans, lm", { expect_s3_class(ggemmeans(fit, "c12hour [20,30,40]"), "data.frame") expect_s3_class(ggemmeans(fit, "c12hour [30:60]"), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour [30:60]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") }) test_that("ggemmeans, lm", { expect_s3_class(ggemmeans(fit, "c12hour [meansd]"), "data.frame") expect_s3_class(ggemmeans(fit, "c12hour [minmax]"), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour [quart]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour [zeromax]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour [quart2]", "c161sex", "c172code [high level of education,low level of education]")), "data.frame") }) data(efc, package = "ggeffects") efc$c172code <- sjlabelled::to_label(efc$c172code) fit <- lm(barthtot ~ log(c12hour) + c161sex + c172code, data = efc) test_that("ggpredict, lm, log", { expect_warning(ggpredict(fit, "c12hour [meansd]")) expect_s3_class(ggpredict(fit, "c12hour [minmax]"), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour", "c172code [high level of education,low level of education]")), "data.frame") expect_s3_class(ggpredict(fit, c("c12hour [exp]", "c172code [high level of education,low level of education]")), "data.frame") }) test_that("ggeffect, lm, log", { expect_s3_class(ggeffect(fit, "c12hour [meansd]"), "data.frame") expect_s3_class(ggeffect(fit, "c12hour [minmax]"), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour", "c172code [high level of education,low level of education]")), "data.frame") expect_s3_class(suppressWarnings(ggeffect(fit, c("c12hour [exp]", "c172code [high level of education,low level of education]"))), "data.frame") }) test_that("ggeffect, lm, no_space", { expect_s3_class(ggeffect(fit, "c12hour[meansd]"), "data.frame") expect_s3_class(ggeffect(fit, "c12hour[minmax]"), "data.frame") expect_s3_class(ggeffect(fit, c("c12hour", "c172code[high level of education,low level of education]")), "data.frame") expect_s3_class(suppressWarnings(ggeffect(fit, c("c12hour[exp]", "c172code[high level of education,low level of education]"))), "data.frame") }) test_that("ggemmeans, lm, log", { expect_s3_class(ggemmeans(fit, "c12hour [meansd]"), "data.frame") expect_s3_class(ggemmeans(fit, "c12hour [minmax]"), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c172code [high level of education,low level of education]")), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour [exp]", "c172code [high level of education,low level of education]")), "data.frame") }) test_that("ggemmeans, lm, no_space", { expect_s3_class(ggemmeans(fit, "c12hour[meansd]"), "data.frame") expect_s3_class(ggemmeans(fit, "c12hour[minmax]"), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour", "c172code[high level of education,low level of education]")), "data.frame") expect_s3_class(ggemmeans(fit, c("c12hour[exp]", "c172code[high level of education,low level of education]")), "data.frame") }) test_that("ggpredict, lm formula", { expect_s3_class(ggpredict(fit, ~ c12hour), "data.frame") expect_s3_class(ggpredict(fit, ~ c12hour + c161sex), "data.frame") expect_s3_class(ggpredict(fit, ~ c12hour + c161sex + c172code), "data.frame") }) d <- subset(efc, select = c(barthtot, c12hour, neg_c_7, c172code)) d <- na.omit(d) m1 <- lm(barthtot ~ c12hour + poly(neg_c_7, 2) + c172code, data = d) m2 <- lm(barthtot ~ c12hour + poly(neg_c_7, 3, raw = TRUE) + c172code, data = d) m3 <- lm(barthtot ~ scale(c12hour) + poly(neg_c_7, 2) + c172code, data = d) test_that("ggpredict, lm", { expect_s3_class(ggpredict(m1, "neg_c_7"), "data.frame") expect_s3_class(ggpredict(m2, "neg_c_7"), "data.frame") expect_s3_class(ggpredict(m3, "neg_c_7"), "data.frame") expect_s3_class(ggpredict(m3, "c12hour"), "data.frame") }) test_that("ggemmeans, lm", { expect_s3_class(ggemmeans(m1, "neg_c_7"), "data.frame") expect_s3_class(ggemmeans(m2, "neg_c_7"), "data.frame") expect_s3_class(ggemmeans(m3, "neg_c_7"), "data.frame") expect_s3_class(ggemmeans(m3, "c12hour"), "data.frame") }) data(efc, package = "ggeffects") fit <- lm(barthtot ~ c12hour + neg_c_7, data = efc) test_that("ggemmeans, lm", { p1 <- ggemmeans(fit, "neg_c_7") p2 <- ggeffect(fit, "neg_c_7") p3 <- ggpredict(fit, "neg_c_7") expect_equal(p1$predicted[1], 78.2641, tolerance = 1e-3) expect_equal(p2$predicted[1], 78.2641, tolerance = 1e-3) expect_equal(p3$predicted[1], 78.2641, tolerance = 1e-3) }) test_that("ggemmeans, lm", { p1 <- ggemmeans(fit, "neg_c_7 [5,10]") p2 <- ggeffect(fit, "neg_c_7 [5,10]") p3 <- ggpredict(fit, "neg_c_7 [5,10]") expect_equal(p1$predicted[1], 80.58504, tolerance = 1e-3) expect_equal(p2$predicted[1], 80.58504, tolerance = 1e-3) expect_equal(p3$predicted[1], 80.58504, tolerance = 1e-3) }) }