GLM {sasLM}

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 SOLUTION option of SAS PROC GLM

EMEAN

if TRUE, least square means (or expected means) will be returned. This is equivalent to LSMEANS clause 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 square

Details

It performs the core function of SAS PROC GLM. Least square means for the interaction term of three variables is 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 square table

Type II

Type II sum of square table

Type III

Type III sum of square 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 BETA=TRUE option.

Expected Mean

Least square (or expected) mean table with confidence limit. This is returned only with EMEAN=TRUE option.

Fitted

Fitted value or y hat. This is returned only with Resid=TRUE option.

Residual

Weigthed residuals. This is returned only with Resid=TRUE option.

Author(s)

Kyun-Seop Bae [e-mail]

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)