tests/testthat/test-print.R
In strengejacke/ggeffects: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs
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 | 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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
)
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", {
skip_if_not_installed("emmeans")
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))
})
test_that("ggpredict, ci-level", {
data(iris)
m <- lm(Sepal.Length ~ Species, data = iris)
out <- ggpredict(m, "Species")
expect_snapshot(print(out))
out <- ggpredict(m, "Species", ci_level = 0.8)
expect_snapshot(print(out))
})
test_that("ggpredict, weights", {
skip_if_not_installed("MASS")
data(housing, package = "MASS")
m <- lm(Freq ~ Infl * Type * Sat, data = housing)
expect_snapshot(print(ggaverage(m, c("Infl", "Type", "Sat")), collapse_tables = TRUE))
})
strengejacke/ggeffects documentation built on April 16, 2024, 12:30 a.m.
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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 | 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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
)
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", {
skip_if_not_installed("emmeans")
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))
})
test_that("ggpredict, ci-level", {
data(iris)
m <- lm(Sepal.Length ~ Species, data = iris)
out <- ggpredict(m, "Species")
expect_snapshot(print(out))
out <- ggpredict(m, "Species", ci_level = 0.8)
expect_snapshot(print(out))
})
test_that("ggpredict, weights", {
skip_if_not_installed("MASS")
data(housing, package = "MASS")
m <- lm(Freq ~ Infl * Type * Sat, data = housing)
expect_snapshot(print(ggaverage(m, c("Infl", "Type", "Sat")), collapse_tables = TRUE))
})
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