lmem.gwaser (version 0.1.0)

pca.analysis: Principal Component Analysis.

Description

Performs Principal Component Analysis of marker data from an object of cross class created by the gwas.cross function.

Usage

pca.analysis(crossobj, p.val)

Arguments

crossobj

An object of class = cross obtained from the gwas.cross function from this package, or the read.cross function from r/qtl package (Broman and Sen, 2009). This file contains phenotypic means, genotypic marker score, and genetic map.

p.val

Alpha level (a number) to identify the number of significant axis

Value

A PCA plot with two principal components and a scree plot for all significant axes indicating the proportion of the variance explained by each marker.

Details

Performs two plots.

References

Comadran J, Thomas W, van Eeuwijk F, Ceccarelli S, Grando S, Stanca A, Pecchioni N, Akar T, Al-Yassin A, Benbelkacem A, Ouabbou H, Bort J, Romagosa I, Hackett C, Russell J (2009) Patterns of genetic diversity and linkage disequilibrium in a highly structured Hordeum vulgare association-mapping population for the Mediterranean basin. Theor Appl Genet 119:175-187

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Mardia, K. V., J. T. Kent, and J. M. Bibby (1979) Multivariate Analysis, London: Academic Press.

Venables, W. N. and B. D. Ripley (2002) Modern Applied Statistics with S, Springer-Verlag.

See Also

gwas.analysis

Examples

## Not run: 
# data (QA_geno)
# data (QA_map)
# data (QA_pheno)
# 
# P.data <- QA_pheno
# G.data <- QA_geno
# map.data <- QA_map
# 
# cross.data <- gwas.cross (P.data, G.data, map.data,
# cross='gwas', heterozygotes=FALSE)
# summary (cross.data)
# 
# pca <- pca.analysis(crossobj=cross.data, p.val=0.05)
# 
# ## End(Not run)