regress: Linear regression using OLS

In radiant.model: Model Menu for Radiant: Business Analytics using R and Shiny

Linear regression using OLS

Description

Linear regression using OLS

Usage

regress(
  dataset,
  rvar,
  evar,
  int = "",
  check = "",
  form,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset	Dataset
rvar	The response variable in the regression
evar	Explanatory variables in the regression
int	Interaction terms to include in the model
check	Use "standardize" to see standardized coefficient estimates. Use "stepwise-backward" (or "stepwise-forward", or "stepwise-both") to apply step-wise selection of variables in estimation. Add "robust" for robust estimation of standard errors (HC1)
form	Optional formula to use instead of rvar, evar, and int
data_filter	Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")
arr	Expression to arrange (sort) the data on (e.g., "color, desc(price)")
rows	Rows to select from the specified dataset
envir	Environment to extract data from

Details

See https://radiant-rstats.github.io/docs/model/regress.html for an example in Radiant

Value

A list of all variables used in the regress function as an object of class regress

See Also

summary.regress to summarize results

plot.regress to plot results

predict.regress to generate predictions

Examples

regress(diamonds, "price", c("carat", "clarity"), check = "standardize") %>% summary()
regress(diamonds, "price", c("carat", "clarity")) %>% str()

radiant.model documentation built on Oct. 16, 2023, 9:06 a.m.