lmhelprs"

In lmhelprs: Helper Functions for Linear Model Analysis

knitr::opts_chunk$set(
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Introduction

At the time of writing, lmhelprs has two sets of functions:

• hierarchical_lm() for ordering linear regression models in a hierarchical way and use anova() to compare them.

• test_highest() to identify the highest order term in a linear regression model and compare the model to a model with this term removed.

Hierarchical Regression Analysis

Users can use anova() manually to do hierarchical regression. hierarchical_lm() is written with these features:

• Check whether the models are really "hierarchical". If yes, order them automatically. Users do not need to put them in the correct order manually.

• Compute R-squared and R-squared change and add them to the output.

Let's fit three models for illustration:

library(lmhelprs)
data(data_test1)
lm1a <- lm(y ~ x1 + x2, data_test1)
lm1b <- lm(y ~ x1 + x2 + x3 + x4, data_test1)
lm1c <- lm(y ~ x1 + x2 + x3 + x4 + cat2, data_test1)

They are can be passed to hierarchical_lm() in any order. Hierarchical regression analysis will be conducted correctly, with R-squared and R-squared change:

hierarchical_lm(lm1b, lm1a, lm1c)

If the models are not in hierarchical order, an error will be raised:

lm2a <- lm(y ~ x1 + x2, data_test1)
lm2b <- lm(y ~ x1 + x3 + x4, data_test1)
hierarchical_lm(lm2a, lm2b)

Please refer to the help page of hierarchical_lm() on how it works, and the print method of its output (print.hierarchical_lm()) on how to customize the printout.

Test The Highest Order Term

In a linear regression model with a term of second or higher order, the function test_highest() can be used to identify the highest order term, compare the model to a model with this term removed, and estimate and test the R-squared increase due to adding this term.

For example:

lm_mod <- lm(y ~ x1 + cat2 + cat1 + cat2:cat1, data_test1)
summary(lm_mod)

Although it has six product terms, in terms of variables, it only has one higher order term: cat2:cat1, the interaction between cat2 and cat1.

To automatically find this term, and compare this model to a model without this interaction, test_highest() can be used:

test_highest(lm_mod)

mod_test <- test_highest(lm_mod)

The R-squared change due to adding this interaction (represented by six product terms) to a model without interaction is r formatC(mod_test[2, "R.sq.change"], digits = 5, format = "f"), and the p-value of this change is r formatC(mod_test[2, "Pr(>F)"], digits = 3, format = "f").

The function test_highest() can be used for third and higher order term. For example:

lm_mod3 <- lm(y ~ x1 + x2 + x3*x4*cat2, data_test1)
summary(lm_mod3)
test_highest(lm_mod3)

The model with three-way interaction is compared to a model with only two-way interactions (x3:x4, x3:cat2, and x4:cat2).

If a model has more than one term of the highest order, an error will be raised:

lm_mod2 <- lm(y ~ x1 + x2*x3 + x2*x4, data_test1)
test_highest(lm_mod2)

Please refer to the help page of test_highest() on how it works,

Issues

If you have any suggestions and found any bugs, please feel feel to open a GitHub issue. Thanks.

lmhelprs documentation built on June 8, 2025, 1:48 p.m.