https://github.com/cran/sjPlot
Tip revision: ee1142603f8c7b70ac3b39557e9bba5a80b83237 authored by Daniel Lüdecke on 17 August 2023, 15:30:02 UTC
version 2.8.15
version 2.8.15
Tip revision: ee11426
test-plot_model_std.R
.runThisTest <- Sys.getenv("RunAllsjPlotTests") == "yes"
if (suppressWarnings(
require("testthat") &&
require("sjPlot") &&
require("sjmisc") &&
require("sjlabelled") &&
require("haven") &&
require("lme4")
)) {
context("sjPlot, tab_model type std")
data(sleepstudy)
data(iris)
data(efc)
efc <- to_factor(efc, e42dep, c172code, c161sex)
m1 <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy, REML = F)
m2 <- lmer(Sepal.Length ~ Sepal.Width + Petal.Length + (1 | Species), data = iris)
m3 <- lm(neg_c_7 ~ e42dep + barthtot + c161sex, data = efc)
test_that("plot_model", {
p <- plot_model(m1)
p <- plot_model(m2)
p <- plot_model(m3)
p <- plot_model(m1, type = "slope")
p <- plot_model(m2, type = "slope")
p <- plot_model(m3, type = "slope")
p <- plot_model(m1, type = "resid")
p <- plot_model(m2, type = "resid")
p <- plot_model(m3, type = "resid")
})
test_that("plot_model, std", {
p <- plot_model(m1, type = "std")
p <- plot_model(m1, type = "std2")
p <- plot_model(m2, type = "std")
p <- plot_model(m2, type = "std2")
p <- plot_model(m3, type = "std")
p <- plot_model(m3, type = "std2")
})
if (.runThisTest) {
if (suppressWarnings(
require("testthat") &&
require("rstanarm") &&
require("sjPlot") &&
require("lme4")
)) {
# fit linear model
data(sleepstudy)
sleepstudy$age <- round(runif(nrow(sleepstudy), min = 20, max = 60))
sleepstudy$Rdicho <- dicho(sleepstudy$Reaction)
m1 <- stan_glmer(
Reaction ~ Days + age + (1 | Subject),
data = sleepstudy, QR = TRUE,
# this next line is only to keep the example small in size!
chains = 2, cores = 1, seed = 12345, iter = 500
)
m2 <- stan_glmer(
Rdicho ~ Days + age + (1 | Subject),
data = sleepstudy, QR = TRUE,
family = binomial,
chains = 2, iter = 500
)
test_that("plot_model, rstan", {
p <- plot_model(m1)
p <- plot_model(m2)
p <- plot_model(m1, bpe = "mean")
p <- plot_model(m2, bpe = "mean")
p <- plot_model(m1, bpe = "mean", bpe.style = "dot")
p <- plot_model(m2, bpe = "mean", bpe.style = "dot")
p <- plot_model(m1, bpe = "mean", bpe.style = "line", bpe.color = "green")
p <- plot_model(m2, bpe = "mean", bpe.style = "line", bpe.color = "green")
p <- plot_model(m1, bpe = "mean", bpe.style = "line", bpe.color = "green", prob.inner = .4, prob.outer = .8)
p <- plot_model(m2, bpe = "mean", bpe.style = "line", bpe.color = "green", prob.inner = .4, prob.outer = .8)
p <- plot_model(m1, bpe = "mean", bpe.style = "line", bpe.color = "green", prob.inner = .4, prob.outer = .8, size.inner = .5)
p <- plot_model(m2, bpe = "mean", bpe.style = "line", bpe.color = "green", prob.inner = .4, prob.outer = .8, size.inner = .5)
})
}
}
}