nipals (version 1.0)

empca: Principal component analysis by weighted EMPCA, expectation maximization principal component-analysis

Description

Used for finding principal components of a numeric matrix. Missing values in the matrix are allowed. Weights for each element of the matrix are allowed. Principal Components are extracted one a time. The algorithm computes x = TP', where T is the 'scores' matrix and P is the 'loadings' matrix.

Usage

empca(
  x,
  w,
  ncomp = min(nrow(x), ncol(x)),
  center = TRUE,
  scale = TRUE,
  maxiter = 100,
  tol = 1e-06,
  seed = NULL,
  fitted = FALSE,
  gramschmidt = TRUE,
  verbose = FALSE
)

Value

A list with components eig, scores, loadings, fitted, ncomp, R2, iter, center, scale.

Arguments

x

Numerical matrix for which to find principal components. Missing values are allowed.

w

Numerical matrix of weights.

ncomp

Maximum number of principal components to extract from x.

center

If TRUE, subtract the mean from each column of x before PCA.

scale

if TRUE, divide the standard deviation from each column of x before PCA.

maxiter

Maximum number of EM iterations for each principal component.

tol

Default 1e-6 tolerance for testing convergence of the EM iterations for each principal component.

seed

Random seed to use when initializing the random rotation matrix.

fitted

Default FALSE. If TRUE, return the fitted (reconstructed) value of x.

gramschmidt

Default TRUE. If TRUE, perform Gram-Schmidt orthogonalization at each iteration.

verbose

Default FALSE. Use TRUE or 1 to show some diagnostics.

Author

Kevin Wright

References

Stephen Bailey (2012). Principal Component Analysis with Noisy and/or Missing Data. Publications of the Astronomical Society of the Pacific. http://doi.org/10.1086/668105

Examples

B <- matrix(c(50, 67, 90, 98, 120,
              55, 71, 93, 102, 129,
              65, 76, 95, 105, 134,
              50, 80, 102, 130, 138,
              60, 82, 97, 135, 151,
              65, 89, 106, 137, 153,
              75, 95, 117, 133, 155), ncol=5, byrow=TRUE)
rownames(B) <- c("G1","G2","G3","G4","G5","G6","G7")
colnames(B) <- c("E1","E2","E3","E4","E5")
dim(B) # 7 x 5
p1 <- empca(B)
dim(p1$scores) # 7 x 5
dim(p1$loadings) # 5 x 5

B2 = B
B2[1,1] = B2[2,2] = NA
p2 = empca(B2, fitted=TRUE)