psycModel (version 0.5.0)

model_summary: Model Summary for Regression Models

Description

The function will extract the relevant coefficients from the regression models (see below for supported model).

Usage

model_summary(
  model,
  digits = 3,
  assumption_plot = FALSE,
  quite = FALSE,
  streamline = TRUE,
  return_result = FALSE,
  standardize = NULL,
  ci_method = "satterthwaite"
)

Value

a list of model estimate data frame, model performance data frame, and the assumption plot (an ggplot object)

Arguments

model

an model object. The following model are tested for accuracy: lm, glm, lme, lmer, glmer. Other model object may work if it work with parameters::model_parameters()

digits

number of digits to round to

assumption_plot

Generate an panel of plots that check major assumptions. It is usually recommended to inspect model assumption violation visually. In the background, it calls performance::check_model().

quite

suppress printing output

streamline

print streamlined output. Only print model estimate and performance.

return_result

It set to TRUE, it return the model estimates data frame.

standardize

The method used for standardizing the parameters. Can be NULL (default; no standardization), "refit" (for re-fitting the model on standardized data) or one of "basic", "posthoc", "smart", "pseudo". See 'Details' in parameters::standardize_parameters()

ci_method

see options in the Mixed model section in ?parameters::model_parameters()

References

Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142. https://doi.org/10.1111/j.2041-210x.2012.00261.x

Examples

# I am going to show the more generic usage of this function
# You can also use this package's built in function to fit the models
# I recommend using the integrated_multilevel_model_summary to get everything

# lme example
lme_fit <- lme4::lmer("popular ~ texp  + (1 | class)",
  data = popular
)

model_summary(lme_fit)

# lm example

lm_fit <- lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width,
  data = iris
)

model_summary(lm_fit)