primePCA {primePCA} | R Documentation |
primePCA algorithm
Description
primePCA algorithm
Usage
primePCA(
X,
K,
V_init = NULL,
thresh_sigma = 10,
max_iter = 1000,
thresh_convergence = 1e-05,
thresh_als = 1e-10,
trace.it = F,
prob = 1,
save_file = "",
center = T,
normalize = F
)
Arguments
X |
an |
K |
the number of the principal components of interest |
V_init |
an initial estimate of the top |
thresh_sigma |
used to select the "good" rows of |
max_iter |
maximum number of iterations of refinement |
thresh_convergence |
The algorithm is halted if the Frobenius-norm sine-theta distance between the two consecutive iterates |
thresh_als |
This is fed into |
trace.it |
report the progress if |
prob |
probability of reserving the "good" rows. |
save_file |
the location that saves the intermediate results, including |
center |
center each column of |
normalize |
normalize each column of |
Value
a list is returned, with components V_cur
, step_cur
and loss_all
.
V_cur
is a d
-by-K
matrix of the top K
eigenvectors. step_cur
is the number of iterations.
loss_all
is an array of the trajectory of MSE.
Examples
X <- matrix(1:30 + .1 * rnorm(30), 10, 3)
X[1, 1] <- NA
X[2, 3] <- NA
v_tilde <- primePCA(X, 1)$V_cur