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Parameters of h-tests (correlations, t-tests, chi-squared, ...).
# S3 method for htest
model_parameters(
model,
cramers_v = NULL,
phi = NULL,
standardized_d = NULL,
ci = 0.95,
bootstrap = FALSE,
verbose = TRUE,
...
)# S3 method for pairwise.htest
model_parameters(model, verbose = TRUE, ...)
Object of class htest or pairwise.htest.
Compute Cramer's V or phi as index of effect size.
Can be "raw" or "adjusted" (effect size will be bias-corrected).
Only applies to objects from chisq.test().
If TRUE, compute standardized d as index of
effect size. Only applies to objects from t.test(). Calculation of
d is based on the t-value (see t_to_d)
for details.
Level of confidence intervals for Cramer's V or phi. Currently only
applies to objects from chisq.test().
Should estimates be bootstrapped?
Toggle warnings and messages.
Arguments passed to or from other methods.
A data frame of indices related to the model's parameters.
# NOT RUN {
model <- cor.test(mtcars$mpg, mtcars$cyl, method = "pearson")
model_parameters(model)
model <- t.test(iris$Sepal.Width, iris$Sepal.Length)
model_parameters(model)
model <- t.test(mtcars$mpg ~ mtcars$vs)
model_parameters(model)
model <- t.test(iris$Sepal.Width, mu = 1)
model_parameters(model)
data(airquality)
airquality$Month <- factor(airquality$Month, labels = month.abb[5:9])
model <- pairwise.t.test(airquality$Ozone, airquality$Month)
model_parameters(model)
smokers <- c(83, 90, 129, 70)
patients <- c(86, 93, 136, 82)
model <- pairwise.prop.test(smokers, patients)
model_parameters(model)
# }
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