radiant.model (version 1.6.12)

regress: Linear regression using OLS

Description

Linear regression using OLS

Usage

regress(
  dataset,
  rvar,
  evar,
  int = "",
  check = "",
  sf = 2,
  form,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Value

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

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-1sd" or "standardize-2sd" to see standardized coefficient estimates based on one or two standard deviations ("standardize" is a synonym for "standardize-2sd"). 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)

sf

Scaling factor to use in standardization (2 is the default). Only used if the number of standard deviations is not specified through check

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

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"), check = "standardize-1sd") %>% summary()
regress(diamonds, "price", c("carat", "clarity")) %>% str()