sig_pred: Significant Predictors in an lm model

In asfarlathif/RefineMod: Refine Linear Regression Models

Description

This function finds all the predictor variables that are significant to a linear regression model from the input predictors

Usage

sig_pred(data, res, preds = NULL, p = 0.01, verbose = FALSE, ...)

Arguments

data	a data frame object containing the variables to be used as response and predictors in the model.
res	a character vector of length 1 that matches the name of the response variable column in data. Response variable in the data must be of numeric type.
preds	a character vector of predictor variables in the data. When not specified function will take all variables in data other than response variable as input predictors to start with. A default of NULL is given to this argument to provide the flexibility of using either user defined predictor variables or all but response variable as predictors from the data.
p	a numeric value that denotes the threshold for selecting the predictors based on their statistical significance in building the model. Default p-value threshold is 0.01.
verbose	a logical value denoting whether or not to print progress messages as the function is being run. Default is TRUE
...	additional arguments to be passed to the inner lm() function calls. Refer to the documentation of lm() for more details on those arguments

Value

A character vector with the names of all the significant predictors from the original lm call.

Examples

#cancer_sample data from datateachr package
library(datateachr)

sig_mod <- sig_pred(cancer_sample[,-2], res = "radius_mean")

asfarlathif/RefineMod documentation built on Dec. 31, 2020, 7:51 p.m.