report (version 0.1.0)

report.lmerMod: Mixed Models Report

Description

Create a report of a mixed model (lme4).

Usage

# S3 method for lmerMod
report(
  model,
  interpretation = "default",
  ci = 0.95,
  standardize = NULL,
  standardize_robust = FALSE,
  bootstrap = FALSE,
  iterations = 500,
  performance_metrics = "all",
  df_method = "wald",
  ...
)

Arguments

model

Model object.

interpretation

Interpret the standardized parameters using a set of rules. Default corresponds to "cohen1988" for linear models and "chen2010" for logistic models.

ci

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

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. Note that robust estimation (i.e. robust=TRUE) of standardized parameters only works when standardize="refit".

standardize_robust

Logical, if TRUE, robust standard errors are calculated (if possible), and confidence intervals and p-values are based on these robust standard errors.

bootstrap

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

iterations

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

performance_metrics

See model_performance.

df_method

Method for computing degrees of freedom for p values, standard errors and confidence intervals (CI). May be "wald" (default, see degrees_of_freedom), "ml1" (see dof_ml1), "betwithin" (see dof_betwithin), "satterthwaite" (see dof_satterthwaite) or "kenward" (see dof_kenward). Note that when df_method is not "wald", robust standard errors etc. cannot be computed.

...

Arguments passed to or from other methods. For instance, when bootstrap = TRUE, arguments like ci_method are passed down to describe_posterior.

Value

A list-object of class report, which contains further list-objects with a short and long description of the model summary, as well as a short and long table of parameters and fit indices.

See Also

table_short or text_short to access the related content of the report-object.

Examples

# NOT RUN {
library(lme4)
library(report)

model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
r <- report(model)
text_short(r)
text_long(r)
table_short(r)
table_long(r)


model <- glmer(vs ~ disp + (1 | am), data = mtcars, family = "binomial")
r <- report(model)
text_short(r)
text_long(r)
table_short(r)
table_long(r)
# }