parameters (version 0.16.0)

model_parameters.PMCMR: Parameters from Hypothesis Testing

Description

Parameters from Hypothesis Testing.

Usage

# S3 method for PMCMR
model_parameters(model, ...)
# S3 method for glht
model_parameters(model, ci = 0.95, exponentiate = FALSE, verbose = TRUE, ...)

Arguments

model

Object of class multcomp::glht() (multcomp) or of class PMCMR, trendPMCMR or osrt (PMCMRplus).

...

Arguments passed to or from other methods. For instance, when bootstrap = TRUE, arguments like type or parallel are passed down to bootstrap_model(), and arguments like ci_method are passed down to bayestestR::describe_posterior().

ci

Confidence Interval (CI) level. Default to 0.95 (95%).

exponentiate

Logical, indicating whether or not to exponentiate the the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. Note: Delta-method standard errors are also computed (by multiplying the standard errors by the transformed coefficients). This is to mimic behaviour of other software packages, such as Stata, but these standard errors poorly estimate uncertainty for the transformed coefficient. The transformed confidence interval more clearly captures this uncertainty. For compare_parameters(), exponentiate = "nongaussian" will only exponentiate coefficients from non-Gaussian families.

verbose

Toggle warnings and messages.

Value

A data frame of indices related to the model's parameters.

Examples

# NOT RUN {
if (require("multcomp", quietly = TRUE)) {
  # multiple linear model, swiss data
  lmod <- lm(Fertility ~ ., data = swiss)
  mod <- glht(
    model = lmod,
    linfct = c(
      "Agriculture = 0",
      "Examination = 0",
      "Education = 0",
      "Catholic = 0",
      "Infant.Mortality = 0"
    )
  )
  model_parameters(mod)
}
if (require("PMCMRplus", quietly = TRUE)) {
  model <- kwAllPairsConoverTest(count ~ spray, data = InsectSprays)
  model_parameters(model)
}
# }