FactoMineR (version 2.6)

LinearModel: Analysis of variance with the contrasts sum (the sum of the coefficients is 0)

Description

Analysis of variance with the contrasts sum (the sum of the coefficients is 0)
Test for all the coefficients
Handle missing values

Usage

LinearModel(formula, data, na.action = na.omit, type = c("II","III", 2, 3), 
       selection=c("none","aic","bic"), ...)

Value

The outouts

Ftest

a table with the F-tests

Ttest

a table with the t-tests

lmResult

the summary of the function lm

call

the matched call

lmResultComp

the summary of the lm function for the complete model (given only if a selection is performed)

callComp

the matched call for the complete model (given only if a selection is performed)

Arguments

formula

the formula for the model 'y~x1+x2+x1:x2'

data

a data-frame

na.action

(where relevant) information returned by model.frame on the special handling of NAs.

type

type of test, "II", "III", 2, or 3. Roman numerals are equivalent to the corresponding Arabic numerals.

selection

a string that defines the model selection according to "BIC" for Bayesian Information Criterion or "AIC" for Akaike Information Criterion; "none", by defaut, means that there is no selection.

...

other arguments, cf the function lm

Author

Francois Husson

Details

The Anova function of the package car is used to calculate the F-tests.

The t-tests are obtained using the contrasts "contr.sum" which means that 'sum to zero contrasts'.

A stepwise procedure (using both backword and forward selections) is performed to select a model if selection="AIC" or selection="BIC".

See Also

aov, lm, print.LinearModel

Examples

## Example two-way anova
data(senso)
res <- LinearModel(Score~ Product + Day , data=senso, selection="none")
res

## Example two-way anova with interaction
data(senso)
res2 <- LinearModel(Score~ Product + Day + Product : Day, data=senso, selection="none")
res2

## Example two-way anova with selection
data(senso)
res2 <- LinearModel(Score~ Product + Day + Product : Day, data=senso, selection="BIC")
res2

## Example ancova
data(footsize)
res3 <- LinearModel(footsize ~ size + sex + size : sex, data=footsize)
res3