calcfunpca: Do dimensional reduction using functional pca.

In ikwak2/funqtl: QTL Mapping for Function-Valued Traits

Do dimensional reduction using functional pca.

Description

Do dimensional reduction using functional pca.

Usage

calcfunpca(cross, pheno.cols, n.max = 4, criteria = 0.9, nbasis, nn = 0)

Arguments

cross	An object of class "cross". See the read.cross for details.
pheno.cols	Columns in the phenotype matrix to be used as the phenotype.
n.max	The number of maximum reduced dimension.
criteria	how much of variance explained.
nbasis	The number of basis to use.
nn	The number of exact reduced dimension

Value

It gives a list, Y is a matrix that each column have principal components. eigf is a eigen function object from functional PCA using fda package.

Author(s)

Il-Youp Kwak, <email: [e-mail]>

See Also

scanoneM

Examples

data(exd)
exd <- calc.genoprob(exd, step=2)
cvout <- cvfold(exd, basisset = 4:7, fold = 10)
cvout # basis number 5 have the smallest sse. So we take nbasis = 5.
Y <- calcfunpca(exd, criteria=0.9, nbasis = 5)$Y
out1 <- scanoneM(exd, Y, method = "hk")

ikwak2/funqtl documentation built on April 20, 2022, 3:58 a.m.