tests/testthat/test-print.R
In javifar/ggeffects: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs
.runThisTest <- Sys.getenv("RunAllggeffectsTests") == "yes"
if (.runThisTest &&
suppressWarnings(
require("testthat") &&
require("ggeffects") &&
require("haven") &&
require("sjlabelled") &&
require("sjmisc")
)) {
# lm, linear regression ----
data(efc)
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", {
ggpredict(fit, terms = "c12hour")
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"))
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 | [87.82, 102.24]",
" 45 | 91.98 | [84.68, 99.29]", " 85 | 89.28 | [81.72, 96.83]",
" 170 | 83.52 | [74.97, 92.07]", "", "# neg_c_7 = 11",
"# c82cop1 = 1", "", "c12hour | Predicted | 95% CI",
"-------------------------------------", " 0 | 93.03 | [85.96, 100.10]",
" 45 | 89.98 | [82.83, 97.13]", " 85 | 87.27 | [79.88, 94.67]",
" 170 | 81.52 | [73.13, 89.90]", "", "# neg_c_7 = 14",
"# c82cop1 = 1", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 90.02 | [83.05, 97.00]",
" 45 | 86.98 | [79.93, 94.02]", " 85 | 84.27 | [76.99, 91.54]",
" 170 | 78.51 | [70.25, 86.77]", "", "# neg_c_7 = 9",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"-------------------------------------", " 0 | 94.45 | [88.66, 100.24]",
" 45 | 91.40 | [85.53, 97.27]", " 85 | 88.69 | [82.53, 94.85]",
" 170 | 82.93 | [75.64, 90.23]", "", "# neg_c_7 = 11",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 92.44 | [86.74, 98.14]",
" 45 | 89.40 | [83.63, 95.17]", " 85 | 86.69 | [80.64, 92.74]",
" 170 | 80.93 | [73.75, 88.12]", "", "# neg_c_7 = 14",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 89.44 | [83.71, 95.17]",
" 45 | 86.39 | [80.61, 92.17]", " 85 | 83.68 | [77.65, 89.72]",
" 170 | 77.93 | [70.78, 85.07]", "", "# neg_c_7 = 9",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 93.86 | [88.88, 98.84]",
" 45 | 90.82 | [85.78, 95.86]", " 85 | 88.11 | [82.77, 93.45]",
" 170 | 82.35 | [75.77, 88.93]", "", "# neg_c_7 = 11",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 91.86 | [86.88, 96.84]",
" 45 | 88.81 | [83.78, 93.84]", " 85 | 86.10 | [80.79, 91.42]",
" 170 | 80.35 | [73.81, 86.88]", "", "# neg_c_7 = 14",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 88.86 | [83.68, 94.03]",
" 45 | 85.81 | [80.61, 91.00]", " 85 | 83.10 | [77.65, 88.55]",
" 170 | 77.34 | [70.73, 83.95]", "", "# neg_c_7 = 9",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 93.28 | [88.19, 98.36]",
" 45 | 90.23 | [85.12, 95.34]", " 85 | 87.52 | [82.14, 92.90]",
" 170 | 81.77 | [75.21, 88.33]", "", "# neg_c_7 = 11",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 91.28 | [86.08, 96.47]",
" 45 | 88.23 | [83.02, 93.43]", " 85 | 85.52 | [80.06, 90.98]",
" 170 | 79.76 | [73.17, 86.36]", "", "# neg_c_7 = 14",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 88.27 | [82.75, 93.79]",
" 45 | 85.22 | [79.71, 90.73]", " 85 | 82.52 | [76.79, 88.24]",
" 170 | 76.76 | [69.97, 83.55]", "", "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 | [88.72, 103.34]",
" 45 | 92.99 | [85.58, 100.39]", " 85 | 90.28 | [82.62, 97.93]",
" 170 | 84.52 | [75.87, 93.17]", "", "# neg_c_7 = 11.8",
"# c82cop1 = 1", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 92.23 | [85.20, 99.26]",
" 45 | 89.18 | [82.08, 96.28]", " 85 | 86.47 | [79.12, 93.82]",
" 170 | 80.71 | [72.37, 89.06]", "", "# neg_c_7 = 15.7",
"# c82cop1 = 1", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 88.32 | [81.32, 95.32]",
" 45 | 85.27 | [78.22, 92.33]", " 85 | 82.57 | [75.29, 89.84]",
" 170 | 76.81 | [68.57, 85.05]", "", "# neg_c_7 = 8",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"-------------------------------------", " 0 | 95.45 | [89.58, 101.31]",
" 45 | 92.40 | [86.45, 98.35]", " 85 | 89.69 | [83.45, 95.93]",
" 170 | 83.94 | [76.56, 91.32]", "", "# neg_c_7 = 11.8",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 91.64 | [85.95, 97.33]",
" 45 | 88.60 | [82.84, 94.35]", " 85 | 85.89 | [79.86, 91.91]",
" 170 | 80.13 | [72.97, 87.29]", "", "# neg_c_7 = 15.7",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 87.74 | [81.90, 93.57]",
" 45 | 84.69 | [78.82, 90.56]", " 85 | 81.98 | [75.87, 88.09]",
" 170 | 76.22 | [69.04, 83.41]", "", "# neg_c_7 = 8",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 94.86 | [89.85, 99.88]",
" 45 | 91.82 | [86.73, 96.90]", " 85 | 89.11 | [83.72, 94.50]",
" 170 | 83.35 | [76.73, 89.98]", "", "# neg_c_7 = 11.8",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 91.06 | [86.04, 96.07]",
" 45 | 88.01 | [82.96, 93.06]", " 85 | 85.30 | [79.97, 90.64]",
" 170 | 79.55 | [73.01, 86.08]", "", "# neg_c_7 = 15.7",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 87.15 | [81.78, 92.53]",
" 45 | 84.11 | [78.73, 89.48]", " 85 | 81.40 | [75.78, 87.01]",
" 170 | 75.64 | [68.91, 82.37]", "", "# neg_c_7 = 8",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 94.28 | [89.21, 99.35]",
" 45 | 91.23 | [86.13, 96.34]", " 85 | 88.52 | [83.14, 93.90]",
" 170 | 82.77 | [76.20, 89.34]", "", "# neg_c_7 = 11.8",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 90.47 | [85.21, 95.74]",
" 45 | 87.43 | [82.16, 92.69]", " 85 | 84.72 | [79.21, 90.23]",
" 170 | 78.96 | [72.33, 85.59]", "", "# neg_c_7 = 15.7",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 86.57 | [80.78, 92.36]",
" 45 | 83.52 | [77.76, 89.28]", " 85 | 80.81 | [74.85, 86.77]",
" 170 | 75.06 | [68.09, 82.03]", "", "Adjusted for:",
"* e42dep = independent", "* c161sex = 1.76",
"* c172code = low level of education"),
ignore_attr = TRUE)
})
}
javifar/ggeffects documentation built on Jan. 21, 2022, 12:04 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
.runThisTest <- Sys.getenv("RunAllggeffectsTests") == "yes"
if (.runThisTest &&
suppressWarnings(
require("testthat") &&
require("ggeffects") &&
require("haven") &&
require("sjlabelled") &&
require("sjmisc")
)) {
# lm, linear regression ----
data(efc)
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", {
ggpredict(fit, terms = "c12hour")
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"))
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 | [87.82, 102.24]",
" 45 | 91.98 | [84.68, 99.29]", " 85 | 89.28 | [81.72, 96.83]",
" 170 | 83.52 | [74.97, 92.07]", "", "# neg_c_7 = 11",
"# c82cop1 = 1", "", "c12hour | Predicted | 95% CI",
"-------------------------------------", " 0 | 93.03 | [85.96, 100.10]",
" 45 | 89.98 | [82.83, 97.13]", " 85 | 87.27 | [79.88, 94.67]",
" 170 | 81.52 | [73.13, 89.90]", "", "# neg_c_7 = 14",
"# c82cop1 = 1", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 90.02 | [83.05, 97.00]",
" 45 | 86.98 | [79.93, 94.02]", " 85 | 84.27 | [76.99, 91.54]",
" 170 | 78.51 | [70.25, 86.77]", "", "# neg_c_7 = 9",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"-------------------------------------", " 0 | 94.45 | [88.66, 100.24]",
" 45 | 91.40 | [85.53, 97.27]", " 85 | 88.69 | [82.53, 94.85]",
" 170 | 82.93 | [75.64, 90.23]", "", "# neg_c_7 = 11",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 92.44 | [86.74, 98.14]",
" 45 | 89.40 | [83.63, 95.17]", " 85 | 86.69 | [80.64, 92.74]",
" 170 | 80.93 | [73.75, 88.12]", "", "# neg_c_7 = 14",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 89.44 | [83.71, 95.17]",
" 45 | 86.39 | [80.61, 92.17]", " 85 | 83.68 | [77.65, 89.72]",
" 170 | 77.93 | [70.78, 85.07]", "", "# neg_c_7 = 9",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 93.86 | [88.88, 98.84]",
" 45 | 90.82 | [85.78, 95.86]", " 85 | 88.11 | [82.77, 93.45]",
" 170 | 82.35 | [75.77, 88.93]", "", "# neg_c_7 = 11",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 91.86 | [86.88, 96.84]",
" 45 | 88.81 | [83.78, 93.84]", " 85 | 86.10 | [80.79, 91.42]",
" 170 | 80.35 | [73.81, 86.88]", "", "# neg_c_7 = 14",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 88.86 | [83.68, 94.03]",
" 45 | 85.81 | [80.61, 91.00]", " 85 | 83.10 | [77.65, 88.55]",
" 170 | 77.34 | [70.73, 83.95]", "", "# neg_c_7 = 9",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 93.28 | [88.19, 98.36]",
" 45 | 90.23 | [85.12, 95.34]", " 85 | 87.52 | [82.14, 92.90]",
" 170 | 81.77 | [75.21, 88.33]", "", "# neg_c_7 = 11",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 91.28 | [86.08, 96.47]",
" 45 | 88.23 | [83.02, 93.43]", " 85 | 85.52 | [80.06, 90.98]",
" 170 | 79.76 | [73.17, 86.36]", "", "# neg_c_7 = 14",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 88.27 | [82.75, 93.79]",
" 45 | 85.22 | [79.71, 90.73]", " 85 | 82.52 | [76.79, 88.24]",
" 170 | 76.76 | [69.97, 83.55]", "", "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 | [88.72, 103.34]",
" 45 | 92.99 | [85.58, 100.39]", " 85 | 90.28 | [82.62, 97.93]",
" 170 | 84.52 | [75.87, 93.17]", "", "# neg_c_7 = 11.8",
"# c82cop1 = 1", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 92.23 | [85.20, 99.26]",
" 45 | 89.18 | [82.08, 96.28]", " 85 | 86.47 | [79.12, 93.82]",
" 170 | 80.71 | [72.37, 89.06]", "", "# neg_c_7 = 15.7",
"# c82cop1 = 1", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 88.32 | [81.32, 95.32]",
" 45 | 85.27 | [78.22, 92.33]", " 85 | 82.57 | [75.29, 89.84]",
" 170 | 76.81 | [68.57, 85.05]", "", "# neg_c_7 = 8",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"-------------------------------------", " 0 | 95.45 | [89.58, 101.31]",
" 45 | 92.40 | [86.45, 98.35]", " 85 | 89.69 | [83.45, 95.93]",
" 170 | 83.94 | [76.56, 91.32]", "", "# neg_c_7 = 11.8",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 91.64 | [85.95, 97.33]",
" 45 | 88.60 | [82.84, 94.35]", " 85 | 85.89 | [79.86, 91.91]",
" 170 | 80.13 | [72.97, 87.29]", "", "# neg_c_7 = 15.7",
"# c82cop1 = 2", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 87.74 | [81.90, 93.57]",
" 45 | 84.69 | [78.82, 90.56]", " 85 | 81.98 | [75.87, 88.09]",
" 170 | 76.22 | [69.04, 83.41]", "", "# neg_c_7 = 8",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 94.86 | [89.85, 99.88]",
" 45 | 91.82 | [86.73, 96.90]", " 85 | 89.11 | [83.72, 94.50]",
" 170 | 83.35 | [76.73, 89.98]", "", "# neg_c_7 = 11.8",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 91.06 | [86.04, 96.07]",
" 45 | 88.01 | [82.96, 93.06]", " 85 | 85.30 | [79.97, 90.64]",
" 170 | 79.55 | [73.01, 86.08]", "", "# neg_c_7 = 15.7",
"# c82cop1 = 3", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 87.15 | [81.78, 92.53]",
" 45 | 84.11 | [78.73, 89.48]", " 85 | 81.40 | [75.78, 87.01]",
" 170 | 75.64 | [68.91, 82.37]", "", "# neg_c_7 = 8",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 94.28 | [89.21, 99.35]",
" 45 | 91.23 | [86.13, 96.34]", " 85 | 88.52 | [83.14, 93.90]",
" 170 | 82.77 | [76.20, 89.34]", "", "# neg_c_7 = 11.8",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 90.47 | [85.21, 95.74]",
" 45 | 87.43 | [82.16, 92.69]", " 85 | 84.72 | [79.21, 90.23]",
" 170 | 78.96 | [72.33, 85.59]", "", "# neg_c_7 = 15.7",
"# c82cop1 = 4", "", "c12hour | Predicted | 95% CI",
"------------------------------------", " 0 | 86.57 | [80.78, 92.36]",
" 45 | 83.52 | [77.76, 89.28]", " 85 | 80.81 | [74.85, 86.77]",
" 170 | 75.06 | [68.09, 82.03]", "", "Adjusted for:",
"* e42dep = independent", "* c161sex = 1.76",
"* c172code = low level of education"),
ignore_attr = TRUE)
})
}
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