ccpca: ccpca: Sparse pca with cardinality constraints on the...
In trbKnl/sparseWeightBasedPCA: Sparse weight based PCA

Description Usage Arguments Value Examples

This function performs PCA with cardinality constraints on the component weights.

ccpca(
  X,
  ncomp,
  nzeros,
  itr,
  Wstart,
  nStarts = 1L,
  tol = 1e-07,
  printLoss = TRUE
)

`X`	A data matrix of class 'matrix'
`ncomp`	The number of components to estimate (an integer)
`nzeros`	A vector of length ncomp containing the number of desired zeros in the columns of the component weight matrix `W`
`itr`	The maximum number of iterations (an integer)
`Wstart`	A matrix of `ncomp` columns and `nrow(X)` rows with starting values for the component weight matrix `W`, if `Wstart` only contains zeros, a warm start is used: the first `ncomp` right singular vectors of `X`
`nStarts`	The number of random starts the analysis should perform. The first start will be performed with the values given by `Wstart`. The consecutive starts will be `Wstart` plus a matrix with random uniform values times the current start number (the first start has index zero). The default value is 1.
`tol`	The convergence is determined by comparing the loss function value after each iteration, if the difference is smaller than `tol` the analysis is converged. The default value is `10e-8`
`printLoss`	A boolean: `TRUE` will print the loss function value each 10th iteration.

A list containing:
W A matrix containing the component weights
P A matrix containing the loadings
loss A numeric variable containing the minimum loss function value of all the nStarts starts
converged A boolean containing TRUE if converged FALSE if not converged.

I <- 100
J <- 50 
ncomp <- 3
X <- matrix(rnorm(I*J), I, J)

ccpca(X = X, ncomp = ncomp,  nzeros = c(10, 20, 30), itr = 100000, 
     Wstart = matrix(0, J, ncomp), nStarts = 1, tol = 10^-8, printLoss = TRUE)

# Extended example: Perform CCPCA, with oracle information
# create sample data
ncomp <- 3 
J <- 30
comdis <- matrix(1, J, ncomp)

comdis <- sparsify(comdis, 0.5) #set 10 percent of the 1's to zero
variances <- makeVariance(varianceOfComps = c(100, 80, 90), J = J, error = 0.05) #create realistic eigenvalues
dat <- makeDat(n = 100, comdis = comdis, variances = variances)
X <- dat$X

#check how many zero's are in the data generating model
nzeros <- apply(dat$P[, 1:ncomp], 2, function(x) {return(sum(x == 0))} )
nzeros

#run the analysis with oracle information of the exact number of zero's in the component weights 
results <- ccpca(X = X, ncomp = ncomp,  nzeros = nzeros, itr = 10000000, 
      Wstart = matrix(0, J, ncomp), nStarts = 1, tol = 10^-8, printLoss = TRUE)

#inspect the results
head(results$W) 
head(dat$P[, 1:ncomp])