parameters (version 0.2.0)

model_parameters.glmmTMB: Mixed Model Parameters

Description

Parameters of mixed models.

Usage

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

Arguments

model

A mixed model.

ci

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

standardize

Add standardized parameters. Can be FALSE or a character indicating the standardization method (see parameters_standardize()), such as "refit", "2sd", "smart" or "classic". The two former are based on model refitting using a standardized version of data. It is the most accurate, although computationally heavy (as it must re-fit a second model). The "smart" and "classic" are post-hoc methods, fast, but inaccurate (especially if the model includes interactions).

standardize_robust

Robust standardization. See parameters_standardize.

bootstrap

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

iterations

The number of draws to simulate/bootstrap.

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.

...

Arguments passed to or from other methods.

p_method

Method for computing p values. See p_value().

ci_method

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

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, standardize = "refit")

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, standardize = "smart", bootstrap = TRUE, iterations = 50)
# }
# NOT RUN {
# }