report (version 0.1.0)

report_intercept: Model description

Description

Model textual description.

Usage

report_intercept(model, parameters, ...)
report_model(model, ...)
# S3 method for default
report_model(
  model,
  parameters = NULL,
  ci = NULL,
  ci_method = NULL,
  standardize = "refit",
  standardize_robust = FALSE,
  interpretation = NULL,
  bootstrap = FALSE,
  iterations = 500,
  test = NULL,
  rope_range = NULL,
  rope_ci = NULL,
  df_method = NULL,
  ...
)
report_parameters(model, parameters, prefix = "  - ", ...)
report_performance(model, performance = NULL, ...)

Arguments

model

Object.

parameters

A parameters table obtained via parameters::model_parameters().

...

Arguments passed to other methods, in particular model_parameters().

ci

Confidence Interval (CI) level

ci_method

The type of index used for Credible Interval. Can be "HDI" (default, see hdi), "ETI" (see eti) or "SI" (see si).

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.

interpretation

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

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.

test

The indices of effect existence to compute. Character (vector) or list with one or more of these options: "p_direction" (or "pd"), "rope", "p_map", "equivalence_test" (or "equitest"), "bayesfactor" (or "bf") or "all" to compute all tests. For each "test", the corresponding bayestestR function is called (e.g. rope or p_direction) and its results included in the summary output.

rope_range

ROPE's lower and higher bounds. Should be a list of two values (e.g., c(-0.1, 0.1)) or "default". If "default", the bounds are set to x +- 0.1*SD(response).

rope_ci

The Credible Interval (CI) probability, corresponding to the proportion of HDI, to use for the percentage in ROPE.

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.

prefix

Prefix character that appears in front of each line.

performance

A performance table obtained via performance::model_performance().

Value

A list with elements of class report_text (which are character vectors), containing a short and long version of the textual output of the model summary.

See Also

report

Examples

# NOT RUN {
model <- lm(Sepal.Length ~ Species, data = iris)
report_intercept(model)

library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_intercept(model)
model <- lm(Sepal.Length ~ Species, data = iris)
report_model(model)

library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_model(model)
model <- lm(Sepal.Length ~ Species, data = iris)
report_parameters(model)

library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_parameters(model)
model <- lm(Sepal.Length ~ Species, data = iris)
report_performance(model)

library(lme4)
model <- lme4::lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
report_performance(model)
# }