lm_model_summary: Model Summary for Linear Regression
In psycModel: Integrated Toolkit for Psychological Analysis and Modeling in R

lm_model_summary

R Documentation

Model Summary for Linear Regression

Description

An integrated function for fitting a linear regression model.

Usage

lm_model_summary(
  data,
  response_variable = NULL,
  predictor_variable = NULL,
  two_way_interaction_factor = NULL,
  three_way_interaction_factor = NULL,
  family = NULL,
  cateogrical_var = NULL,
  graph_label_name = NULL,
  model_summary = TRUE,
  interaction_plot = TRUE,
  y_lim = NULL,
  plot_color = FALSE,
  digits = 3,
  simple_slope = FALSE,
  assumption_plot = FALSE,
  quite = FALSE,
  streamline = FALSE,
  return_result = FALSE
)

Arguments

`data`	`data.frame`
`response_variable`	DV (i.e., outcome variable / response variable). Length of 1. Support `dplyr::select()` syntax.
`predictor_variable`	IV. Support `dplyr::select()` syntax.
`two_way_interaction_factor`	two-way interaction factors. You need to pass 2+ factor. Support `dplyr::select()` syntax.
`three_way_interaction_factor`	three-way interaction factor. You need to pass exactly 3 factors. Specifying three-way interaction factors automatically included all two-way interactions, so please do not specify the two_way_interaction_factor argument. Support `dplyr::select()` syntax.
`family`	a GLM family. It will passed to the family argument in glm. See `?glm` for possible options.
`cateogrical_var`	list. Specify the upper bound and lower bound directly instead of using ± 1 SD from the mean. Passed in the form of `list(var_name1 = c(upper_bound1, lower_bound1),var_name2 = c(upper_bound2, lower_bound2))`
`graph_label_name`	optional vector or function. vector of length 2 for two-way interaction graph. vector of length 3 for three-way interaction graph. Vector should be passed in the form of c(response_var, predict_var1, predict_var2, ...). Function should be passed as a switch function (see ?two_way_interaction_plot for an example)
`model_summary`	print model summary. Required to be `TRUE` if you want `assumption_plot`.
`interaction_plot`	generate the interaction plot. Default is `TRUE`
`y_lim`	the plot's upper and lower limit for the y-axis. Length of 2. Example: `c(lower_limit, upper_limit)`
`plot_color`	If it is set to `TRUE` (default is `FALSE`), the interaction plot will plot with color.
`digits`	number of digits to round to
`simple_slope`	Slope estimate at +1/-1 SD and the mean of the moderator. Uses `interactions::sim_slope()` in the background.
`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
`return_result`	If it is set to `TRUE` (default is `FALSE`), it will return the `model`, `model_summary`, and `plot` (if the interaction term is included)

Value

a list of all requested items in the order of model, model_summary, interaction_plot, simple_slope

Examples

fit <- lm_model_summary(
  data = iris,
  response_variable = "Sepal.Length",
  predictor_variable = dplyr::everything(),
  two_way_interaction_factor = c(Sepal.Width, Species),
  interaction_plot = FALSE, # you can also request the interaction plot
  simple_slope = FALSE, # you can also request simple slope estimate 
  assumption_plot = FALSE, # you can also request assumption plot
  streamline = FALSE #you can change this to get the least amount of info
)

psycModel documentation built on Nov. 2, 2023, 6:02 p.m.