regress {radiant.model} | R Documentation |
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()