GLM: General Linear Model similar to SAS PROC GLM
In sasLM: 'SAS' Linear Model

View source: R/GLM.R

GLM	R Documentation

General Linear Model similar to SAS PROC GLM

Description

GLM is the main function of this package.

Usage

  GLM(Formula, Data, BETA=FALSE, EMEAN=FALSE, Resid=FALSE, conf.level=0.95,
      Weights=1)

Arguments

`Formula`	a conventional formula for a linear model.
`Data`	a `data.frame` to be analyzed
`BETA`	if `TRUE`, coefficients (parameters) of `REG` will be returned. This is equivalent to the SOLUTION option of SAS PROC GLM
`EMEAN`	if `TRUE`, least square means (or expected means) will be returned. This is equivalent to the LSMEANS statement of SAS PROC GLM
`Resid`	if `TRUE`, fitted values (y hat) and residuals will be returned
`conf.level`	confidence level for the confidence limit of the least square mean
`Weights`	weights for the weighted least squares. This should be a scalar or a vector of the same length as the number of rows of `Data`. Observations with nonpositive weights are excluded from the analysis as SAS does.

Details

It performs the core function of SAS PROC GLM. Least square means for the interaction term of three variables are not supported yet.

Value

The result is comparable to that of SAS PROC GLM.

`ANOVA`	ANOVA table for the model
`Fitness`	Some measures of goodness of fit such as R-square and CV
`Type I`	Type I sum of squares table
`Type II`	Type II sum of squares table
`Type III`	Type III sum of squares table
`Parameter`	Parameter table with standard error, t value, p value. `TRUE` is 1, and `FALSE` is 0 in the `Estimable` column. This is returned only with the BETA=TRUE option.
`Expected Mean`	Least square (or expected) mean table with confidence limits. This is returned only with the EMEAN=TRUE option.
`Fitted`	Fitted values or y hat in the original scale, as SAS OUTPUT P= does, even with Weights. This is returned only with the Resid=TRUE option.
`Residual`	Residuals in the original scale, as SAS OUTPUT R= does, even with Weights. This is returned only with the Resid=TRUE option.

Author(s)

Kyun-Seop Bae k@acr.kr

Examples

GLM(uptake ~ Type*Treatment + conc, CO2[-1,]) # Making data unbalanced
GLM(uptake ~ Type*Treatment + conc, CO2[-1,], BETA=TRUE)
GLM(uptake ~ Type*Treatment + conc, CO2[-1,], EMEAN=TRUE)
GLM(uptake ~ Type*Treatment + conc, CO2[-1,], Resid=TRUE)
GLM(uptake ~ Type*Treatment + conc, CO2[-1,], BETA=TRUE, EMEAN=TRUE)
GLM(uptake ~ Type*Treatment + conc, CO2[-1,], BETA=TRUE, EMEAN=TRUE, Resid=TRUE)

sasLM documentation built on June 15, 2026, 9:07 a.m.