regress: Linear regression using OLS

Description Usage Arguments Details Value See Also Examples

View source: R/regress.R

Description

Linear regression using OLS

Usage

1
regress(dataset, rvar, evar, int = "", check = "", data_filter = "")

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)

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")

Details

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

Value

A list of all variables 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

1
2
regress(diamonds, "price", c("carat", "clarity"), check = "standardize") %>% summary()
regress(diamonds, "price", c("carat", "clarity")) %>% str()

radiant-rstats/radiant.model documentation built on Nov. 13, 2018, 7 a.m.