tracy.widom {LEA} | R Documentation |
Perform tracy-widom tests on a set of eigenvalues to determine the number of
significative eigenvalues and calculate the percentage of variance explained
by each principal component. For an example, see pca
.
tracy.widom (object)
object |
a pcaProject object. |
tracy.widom
returns a list containing the following components:
eigenvalues |
The sorted input vector of eigenvalues (by descreasing order). |
twstats |
The vector of tracy-widom statistics. |
pvalues |
The vector of p-values associated with each eigenvalue. |
effecn |
The vector of effective sizes. |
percentage |
The vector containing the percentage of variance explained by each principal component. |
Eric Frichot
Tracy CA and Widom H. (1994). Level spacing distributions and the bessel kernel. Commun Math Phys. 161 :289–309. Patterson N, Price AL and Reich D. (2006). Population structure and eigenanalysis. PLoS Genet. 2 :20.
# Creation of the genotype file "genotypes.lfmm" # with 1000 SNPs for 165 individuals. data("tutorial") write.lfmm(tutorial.R,"genotypes.lfmm") ################# # Perform a PCA # ################# # run of PCA # Available options, K (the number of PCs calculated), # center and scale. # Creation of genotypes.pcaProject - the pcaProject object. # a directory genotypes.pca containing: # Create files: genotypes.eigenvalues - eigenvalues, # genotypes.eigenvectors - eigenvectors, # genotypes.sdev - standard deviations, # genotypes.projections - projections, # Create a pcaProject object: pc. pc = pca("genotypes.lfmm", scale = TRUE) ############################# # Perform Tracy-Widom tests # ############################# # Perfom Tracy-Widom tests on all eigenvalues. # Create file: genotypes.tracyWidom - tracy-widom test information, # in the directory genotypes.pca/. tw = tracy.widom(pc) # Plot the percentage of variance explained by each component. plot(tw$percentage) # Display the p-values for the Tracy-Widom tests. tw$pvalues # remove pca Project remove.pcaProject("genotypes.pcaProject")