Nothing
tests/testthat/test-print.R
In ggeffects: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs
.runThisTest <- Sys.getenv("RunAllggeffectsTests") == "yes"
if (.runThisTest &&
suppressWarnings(
requiet("testthat") &&
requiet("ggeffects") &&
requiet("haven") &&
requiet("sjlabelled") &&
requiet("sjmisc")
)) {
# lm, linear regression ----
data(efc, package = "ggeffects")
efc$c172code <- to_label(efc$c172code)
efc$e42dep <- to_label(efc$e42dep)
efc$c82cop1 <- as.numeric(efc$c82cop1)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c82cop1 + e42dep + c161sex + c172code, data = efc)
test_that("ggpredict, print", {
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 | 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 = 11",
"# 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 = 14",
"# 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 = 9",
"# 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 = 11",
"# 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 = 14",
"# 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 = 9",
"# 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 = 11",
"# 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 = 14",
"# 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 = 9",
"# 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]", "", "# neg_c_7 = 11",
"# 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 = 14",
"# 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]", "", "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 | 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 = 11.8",
"# 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 = 15.7",
"# 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 = 8",
"# 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 = 11.8",
"# 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 = 15.7",
"# 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 = 8",
"# 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 = 11.8",
"# 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 = 15.7",
"# 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 = 8",
"# 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]", "", "# neg_c_7 = 11.8",
"# 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 = 15.7",
"# 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]", "", "Adjusted for:",
"* e42dep = independent", "* c161sex = 1.76",
"* c172code = low level of education"),
ignore_attr = TRUE)
})
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))
})
}
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ggeffects documentation built on Oct. 17, 2023, 5:07 p.m.
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.runThisTest <- Sys.getenv("RunAllggeffectsTests") == "yes"
if (.runThisTest &&
suppressWarnings(
requiet("testthat") &&
requiet("ggeffects") &&
requiet("haven") &&
requiet("sjlabelled") &&
requiet("sjmisc")
)) {
# lm, linear regression ----
data(efc, package = "ggeffects")
efc$c172code <- to_label(efc$c172code)
efc$e42dep <- to_label(efc$e42dep)
efc$c82cop1 <- as.numeric(efc$c82cop1)
fit <- lm(barthtot ~ c12hour + neg_c_7 + c82cop1 + e42dep + c161sex + c172code, data = efc)
test_that("ggpredict, print", {
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 | 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 = 11",
"# 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 = 14",
"# 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 = 9",
"# 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 = 11",
"# 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 = 14",
"# 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 = 9",
"# 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 = 11",
"# 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 = 14",
"# 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 = 9",
"# 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]", "", "# neg_c_7 = 11",
"# 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 = 14",
"# 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]", "", "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 | 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 = 11.8",
"# 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 = 15.7",
"# 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 = 8",
"# 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 = 11.8",
"# 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 = 15.7",
"# 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 = 8",
"# 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 = 11.8",
"# 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 = 15.7",
"# 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 = 8",
"# 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]", "", "# neg_c_7 = 11.8",
"# 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 = 15.7",
"# 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]", "", "Adjusted for:",
"* e42dep = independent", "* c161sex = 1.76",
"* c172code = low level of education"),
ignore_attr = TRUE)
})
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))
})
}
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