Genset: Generate Mutliple Data Sets for Hands-on Learning

In genset: Generates Multiple Data Sets

Generate Mutliple Data Sets for Hands-on Learning

Description

Generate mutliple data sets to demonstrate the importance of multiple regression. Data sets are generated from an initial data set input to have the same summary statistics (mean, median, and standard deviation) but opposing regression results (significance in the predictor variables). The initial data set will have one response variable (continuous) and two predictor variables (continous or one continuous and one categorical with 2 levels) that are statistically significant in a linear regression model.

Usage

genset(y, x1, x2, method=c(1,2), option=c("x1","x2","both"), n, decrease, output)

Arguments

y	response variable (continuous).
x1	first predictor variable (continuous).
x2	second predictor variable (continuous or categorical with 2 levels). If variable is categorical then argument is factor(x2).
method	the method 1 or 2 to be used to generate the data set. 1 (default) rearranges the values within each variable, and 2 is a perturbation method that makes subtle changes to the values of the variables.
option	the variable(s) that will not statistically significant in the new data set ("x1", "x2" or "both").
n	the number of iterations. Default is 2000 iterations.
decrease	indicates an increase or decrease in level of significance. FALSE is the default.
output	shows the iterations. FALSE is the default.

Details

The summary statistics are within a (predetermined) tolerance level, and when rounded, will be the same as the original data set. The standard convention of 0.05 is used as the significance level threshold. Less than n=2000 iterations may or may not be sufficient and is dependent on the initial data set.

Author(s)

Lori Murray and John Wilson

References

Murray, L.L. & Wilson, J.G. (2021). Generating data sets for teaching the importance of regression analysis. Decision Sciences Journal of Innovative Education (DSJIE), Vol 19 (2), 157-166.

Examples

## Choose variables of interest
y <- mtcars$mpg
x1 <- mtcars$hp
x2 <- mtcars$wt
## Create a dataframe
set1 <- data.frame(y, x1, x2)
## Check summary statistics
multi.fun <- function(x) {
  c(mean = mean(x), media=median(x), sd=sd(x))
}
round(multi.fun(set1$y), 0)
round(multi.fun(set1$x1), 1)
round(multi.fun(set1$x2), 1)
## Fit linear regression model
## to verify regressors are statistically
## significant (p-value < 0.05)
summary(lm(y ~ x1, x2, data=set1))

## Set seed to reproduce same data set
set.seed(101)
set2 <- genset(y, x1, x2, method=1, option="x1", n=1000)
## Verify summary statistics match set 1
round(multi.fun(set2$y), 0)
round(multi.fun(set2$x1), 1)
round(multi.fun(set2$x2), 1)
## Fit linear regression model
## to verify x1 is not statistically
## significant (p-value > 0.05)
summary(lm(y ~ x1 + x2, data=set2))

genset documentation built on April 4, 2025, 1:44 a.m.