paleoMAS (version 2.0-1)

vcrossv.da: V-fold cross-validation for discriminant analysis

Description

This function v-fold cross-validates a discriminant analysis through the leave-v-out procedure.

Usage

vcrossv.da(x, f, fold, nsimulat, funct)

Arguments

x

A matrix with samples in columns and taxa in rows. The rows must be named after taxa names (see rownames).

f

An object of class factor containing the discriminant factor (See Venables & Ripley (2002) for details on discriminant analysis).

fold

Value of v, i.e. number of elements to be left out in each validation.

nsimulat

Number of samples simulated to desaturate the model (see Correa-Metrio et al (in review) for details). If no samples were simulated nsimulat=1.

funct

lda for linear discriminant analysis, and qda for quadratic discriminant analysis.

Value

A list containing:

posterior

The a posteriori probability of each taxa belonging to each one of the defined groups.

comp2

Binary classification of the taxa.

accuracy

The percentage of cases well classified in the cross-validation.

Details

The function was designed for discrimination of pollen taxa into dichotomous ecological groups (only admits two factors). The prior information corresponds to the affinity of certain taxa to known environmental conditions. Therefore, while the taxa corrrespond to the objects to classify, the percentages through the fossil dataset correspond to the attributes. Each time the discriminant function is adjusted, v elements are left out with no replacements. Therefore, it is recommended that v be smaller than half of the total taxa, unless there is a considerable number of species. Take also into consideration that each time a taxon is left out for the crossvalidation, all the samples that were simulated for such taxon are left out too.

References

Correa-Metrio, A., K.R. Cabrera, and M.B. Bush. 2010. Quantifying ecological change through discriminant analysis: a paleoecological example from the Peruvian Amazon. Journal of Vegetation Science 21: 695-704.

Venables, W.N., and B.D. Ripley. 2002. "Modern applied statistics with S". Springer, New York.

See Also

vcrossv.all.lda and qda (package MASS) for details on the discriminant functions. simulat and simulat.t for details on samples simulations.

Examples

data(quexilper)
# Taking only a fraction of the data base so the model is not saturated
a<-quexilper[1:10,1:20]
a<-t(a)
# build a dummy factor assuming that the first 10 species belong
# to group1 and the send ten belong to group 2
b<-as.factor(rep(c("group1","group2"),each=10))
#to apply ordinary crossvalidation (leave-one-out)
vcrossv.da(a,b,fold=1,nsimulat=1,funct=lda)
#to apply 3-fold cross-validation
vcrossv.da(a,b,fold=3,nsimulat=1,funct=lda)