GLM | R Documentation |
GLM is the main function of this package.
GLM(Formula, Data, BETA=FALSE, EMEAN=FALSE, Resid=FALSE, conf.level=0.95,
Weights=1)
Formula |
a conventional formula for a linear model. |
Data |
a |
BETA |
if |
EMEAN |
if |
Resid |
if |
conf.level |
confidence level for the confidence limit of the least square mean |
Weights |
weights for the weighted least square |
It performs the core function of SAS PROC GLM. Least square means for the interaction term of three variables is not supported yet.
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. |
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. |
Kyun-Seop Bae k@acr.kr
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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