Principal Component Analysis exploits sample covariance matrix whose eigenvectors and eigenvalues are principal components and projected variance, correspondingly. Given varratio, it thresholds the accumulated variance and selects the estimated dimension. Note that other than linear submanifold case, the naive selection scheme from this algorithm lacks flexibility in discovering intrinsic dimension.

est.pcathr(X, varratio = 0.95)

## Arguments

X

an $$(n\times p)$$ matrix or data frame whose rows are observations.

varratio

target explainability for accumulated variance in $$(0,1)$$.

## Value

a named list containing containing

estdim

estimated dimension according to varratio.

values

eigenvalues of sample covariance matrix.

do.pca

Kisung You

## Examples

## generate 3-dimensional normal data
X = matrix(rnorm(100*3), nrow=100)

## replicate 3 times with translations
Y = cbind(X-10,X,X+10)

## use PCA thresholding estimation with 95% variance explainability
## desired return is for dimension 3.
output   = est.pcathr(Y)
pmessage = paste("* estimated dimension is ",output$estdim, sep="") print(pmessage) #> [1] "* estimated dimension is 3" ## use screeplot opar <- par(no.readonly=TRUE) plot(output$values, main="scree plot", type="b")

par(opar)