FSelect {multiDimBio}

A Function to perform step-wise discriminant analysis using F statistics

Description

Select data using a F tests

Usage

FSelect(Data, Group, target, p.adj.method = "holm", Missing.Data = "Complete")

Arguments

Data

A (non-empty), numeric matrix of data values

Group

A (non-empty), vector indicating group membership. Length(unique(Group))==2

target

The number of desired traits. Target cannot be greater than the number of columns in Data

p.adj.method

The method used to control for false discovery. The default setting is 'holm'

Missing.Data

The method used to handle missing data. The default, 'Complete' will use completeData to impute missing data, setting Missing.Data='Remove' will remove all individuals with missing data. FSelect cannot handle missing data.

Value

FSelect returns list containing at least the following components:

Selected

An ordered list indicating which columns were selected.

F.Selected

An ordered list containing the F statistics for each column indicated in Selected.

PrF

An ordered list containing the p values for each column indicated in Selected.

PrNotes

A string indicating which method was used to control for multiple comparisons

model

An lm object with the final model results.

References

Costanza M, Afifi A (1979). Comparison of Stopping Rules in Forward Stepwise Discriminant Analysis. Journal of the American Statistical Association, pp. 777 - 78

Habbema J, Hermans J (1977). Selection of Variables in Discriminant Analysis by F - Statistics and Error Rate. Technometrics, 19(4), 487 - 493.

Jennrich R (1977). Stepwise discriminant analysis, volume 3. New York Wiley Sons.

See Also

completeData

Examples

data(Nuclei)
data(Groups)
npcs<-floor(ncol(Nuclei)/5)

dat.comp <- completeData(data = Nuclei, n_pcs = npcs) 
groups.use <- c(1,2) 
use.dat <- which(Groups==groups.use[1]|Groups==groups.use[2])

dat.use <- Nuclei[use.dat,]
GR.use <- Groups[use.dat]

#not run
#FSelect(DAT.use,GR.use,3)