slide_givenS: Fits SLIDE model for X using the pre-specified structure S.

In irinagain/SLIDE: Structural Learning and Integrative Decomposition of Multi-View Data

Description

Fits SLIDE model for X using the pre-specified structure S.

Usage

slide_givenS(
  X,
  pvec,
  S,
  Ustart = NULL,
  eps = 1e-06,
  k_max = 1000,
  standardized = F
)

Arguments

X	A n x p concatenated data matrix of views X_1,...,X_d.
pvec	A vector of values p_1,....,p_d corresponding to the number of measurements within each view.
S	A binary matrix with nonzero columns of size d x r.
Ustart	An n X r optional starting value for U, if not supplied the first r left singular vectors of X are used.
eps	A convergence tolerance criterion, the default value is 1e-6.
k_max	A maximal number of allowable iterations, the default values is 1000.
standardized	A logical indicator of whether X is centered and standardized. The default value is FALSE and the standardization is performed within the function.

Value

A list with the elements

U	A n x r score matrix for the SLIDE model.
V	A p x r loadings matrix for the SLIDE model with sparsity pattern according to S.
error	Tolerance value at convergence.

Examples

n = 100
p1 = 25
p2 = 25
data = generateModel1(n = n, pvec = c(p1, p2))

# Specify binary structure
S = matrix(c(1,1,0,1,0,1),nrow = 2, ncol = 3)

# Unstandardized
fit_slide = slide_givenS(data$X, pvec = c(p1,p2), S = S)

# Standardized
out = standardizeX(data$X, pvec = c(p1,p2))
fit_slide = slide_givenS(out$X, pvec = c(p1,p2), S = S, standardized = TRUE)

irinagain/SLIDE documentation built on Aug. 14, 2021, 2:56 p.m.