ANCOVA
In psycModel: Integrated Toolkit for Psychological Analysis and Modeling in R

lm_model

R Documentation

Linear Regressions / ANOVA / ANCOVA

Description

Fit a linear regression using lm(). Linear regression is used to explore the effect of continuous variables / categorical variables in predicting a normally-distributed continuous variables.

Usage

lm_model(
  data,
  response_variable,
  predictor_variable,
  two_way_interaction_factor = NULL,
  three_way_interaction_factor = NULL,
  quite = FALSE
)

Arguments

`data`	`data.frame`
`response_variable`	response variable. Support `dplyr::select()` syntax.
`predictor_variable`	predictor variable. Support `dplyr::select()` syntax. It will automatically remove the response variable from predictor variable, so you can use `contains()` or `start_with()` safely.
`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.
`quite`	suppress printing output

Value

an object class of lm representing the linear regression fit

Examples

fit <- lm_model(
  data = iris,
  response_variable = Sepal.Length,
  predictor_variable = dplyr::everything(),
  two_way_interaction_factor = c(Sepal.Width, Species)
)

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