radiant (version 0.1.95)

regression: Linear regression using OLS

Description

Linear regression using OLS

Usage

regression(dataset, reg_dep_var, reg_indep_var, data_filter = "", reg_int_var = "", reg_check = "")

Arguments

dataset

Dataset name (string). This can be a dataframe in the global environment or an element in an r_data list from Radiant

reg_dep_var

The dependent variable in the regression

reg_indep_var

Independent variables in the regression

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")

reg_int_var

Interaction terms to include in the model

reg_check

"standardize" to see standardized coefficient estimates. "stepwise" to apply step-wise selection of variables in estimation

Value

A list of all variables used in regression as an object of class regression

Details

See http://vnijs.github.io/radiant/quant/regression.html for an example in Radiant

See Also

summary.regression to summarize results

plot.regression to plot results

predict.regression to generate predictions

Examples

result <- regression("diamonds", "price", c("carat","clarity"))
result <- regression("diamonds", "price", c("carat","clarity"), reg_check = "standardize")