ANOVA for heteroscedastic data

Univariate ANOVA for heteroscedastic data.

Usage

fanova.hetero(object = NULL, formula, pr = FALSE, contrast = NULL, ...)

Arguments

object: A data frame with dimension (n x p+1). In the first column contains the n response values and on the following p columns the explanatory variables specified in the formula.
formula: as formula.
pr: If TRUE, print intermediate results.
contrast: List of special contrast to be used, by default no special contrasts are used (contrast=NULL).
...: Further arguments passed to or from other methods.

Value

Return:

ans: A list with components including: the Beta estimation Est, the factor degrees of freedom df1, the residual degrees of freedom df2, and the p-value for each factor.
contrast: List of special contrasts.

Details

This function fits a univariate analysis of variance model and allows calculate special contrasts defined by the user. The list of special contrast to be used for some of the factors in the formula. Each matrix of the list has r rows and r-1 columns.

The user can also request special predetermined contrasts, for example using contr.helmert, contr.sum or contr.treatment functions.

Note

anova.hetero deprecated

It only works with categorical variables.

References

Brunner, E., Dette, H., Munk, A. Box-Type Approximations in Nonparametric Factorial Designs. Journal of the American Statistical Association, Vol. 92, No. 440 (Dec., 1997), pp. 1494-1502.

Author

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es

Examples

if (FALSE) { # \dontrun{
data(phoneme)
ind=1 # beetwen 1:150
fdataobj=data.frame(phoneme$learn[["data"]][,ind])
n=dim(fdataobj)[1]
group<-factor(phoneme$classlearn)

#ex 1: real factor and random factor
group.rand=as.factor(sample(rep(1:3,n),n))
f=data.frame(group,group.rand)
mm=data.frame(fdataobj,f)
colnames(mm)=c("value","group","group.rand")
out1=fanova.hetero(object=mm[,-2],value~group.rand,pr=FALSE)
out2=fanova.hetero(object=mm[,-3],value~group,pr=FALSE)
out1
out2

#ex 2: real factor, random factor and  special contrasts
cr5=contr.sum(5)  #each level vs last level
cr3=c(1,0,-1)      #first level vs last level
out.contrast=fanova.hetero(object=mm[,-3],value~group,pr=FALSE,
contrast=list(group=cr5))
out.contrast
} # }