GLM: 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)

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.

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

Author

Kyun-Seop Bae k@acr.kr

Details

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

Examples

Run this code

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)

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