```
context("standardize")
test_that("Correct Value", {
library(psycho)
set.seed(666)
df <- data.frame(
Participant = as.factor(rep(1:25, each = 4)),
Condition = base::rep_len(c("A", "B", "C", "D"), 100),
V1 = rnorm(100, 30, .2),
V2 = runif(100, 3, 5),
V3 = rnorm(100, 100, 10)
)
dfZ <- standardize(df)
testthat::expect_equal(mean(dfZ$V1), 0, tol = 0.01)
dfZ <- standardize(df, except = "V3")
testthat::expect_equal(mean(dfZ$V2), 0, tol = 0.01)
dfZ <- standardize(df, except = c("V1", "V2"))
testthat::expect_equal(mean(dfZ$V3), 0, tol = 0.01)
dfZ <- standardize(df$V1)
testthat::expect_equal(mean(dfZ), 0, tol = 0.01)
dfZ <- standardize(df, subset = c("V1", "V2"))
testthat::expect_equal(mean(dfZ$V1), 0, tol = 0.01)
dfZ <- standardize(df, subset = "V1", except = "V3")
testthat::expect_equal(mean(dfZ$V1), 0, tol = 0.01)
dfZ <- standardize(dplyr::group_by(df, Participant))
testthat::expect_equal(mean(dfZ$V1), 0, tol = 0.01)
dfN <- standardize(df, except = "V3", normalize = TRUE)
testthat::expect_equal(mean(dfN$V2), 0.533, tol = 0.5)
# Models
fit <- rstanarm::stan_glm(
Sepal.Length ~ Sepal.Width,
data = iris,
seed = 666,
algorithm = "meanfield"
)
std <- standardize(fit, method="posterior")
testthat::expect_equal(mean(std), -0.24, tol = 0.02)
std <- standardize(fit, method="sample")
testthat::expect_equal(mean(std), 1.34, tol = 0.02)
fit <- lm(
Sepal.Length ~ Sepal.Width,
data = iris
)
std <- standardize(fit, method="posthoc")
testthat::expect_equal(mean(std$Coef_std), -0.059, tol = 0.01)
})
```

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