parameters (version 0.3.0)

model_parameters.merMod: Mixed Model Parameters

Description

Parameters of (linear) mixed models.

Usage

# S3 method for merMod
model_parameters(model, ci = 0.95, bootstrap = FALSE,
  p_method = "wald", ci_method = "wald", iterations = 1000,
  standardize = NULL, exponentiate = FALSE, ...)
# S3 method for glmmTMB
model_parameters(model, ci = 0.95, bootstrap = FALSE,
  iterations = 1000, component = c("all", "conditional", "zi",
  "zero_inflated"), standardize = NULL, exponentiate = FALSE, ...)

Arguments

model

A mixed model.

ci

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

bootstrap

Should estimates be based on bootstrapped model? If TRUE, then arguments of Bayesian regressions apply (see also parameters_bootstrap()).

p_method

Method for computing p values. See p_value().

ci_method

Method for computing confidence intervals (CI). See ci().

iterations

The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.

standardize

The method used for standardizing the parameters. Can be "refit", "posthoc", "smart", "basic" or NULL (default) for no standardization. See 'Details' in standardize_parameters.

exponentiate

Logical, indicating whether or not to exponentiate the the coefficients (and related confidence intervals). This is typical for, say, logistic regressions, or more generally speaking: for models with log or logit link.

...

Arguments passed to or from other methods.

component

Should all parameters, parameters for the conditional model, or for the zero-inflated part of the model be returned? Applies to models with zero-inflated component. component may be one of "conditional", "zi", "zero-inflated" or "all" (default). May be abbreviated.

Value

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

See Also

standardize_names() to rename columns into a consistent, standardized naming scheme.

Examples

# NOT RUN {
library(parameters)
library(lme4)
library(glmmTMB)

model <- lmer(mpg ~ wt + (1 | gear), data = mtcars)
model_parameters(model)

model <- glmmTMB(
  count ~ spp + mined + (1 | site),
  ziformula = ~mined,
  family = poisson(),
  data = Salamanders
)
model_parameters(model)
# }
# NOT RUN {
model <- lme4::lmer(mpg ~ wt + (1 | gear), data = mtcars)
model_parameters(model, bootstrap = TRUE, iterations = 50)
# }
# NOT RUN {
# }