Description Usage Arguments Value Examples
Using EM algorithm to fit the SupCP model
1 2 3 4 5 6 7 8 9 10 | SupParafacEM(
Y,
X,
R,
AnnealIters = 100,
ParafacStart = 0,
max_niter = 1000,
convg_thres = 10^-3,
Sf_diag = 1
)
|
Y |
n*q full column rank reponse matrix(necessarily n>=q) |
X |
n*p1*...*pk design array |
R |
fixed rankd of approximation, R<=min(n,p) |
AnnealIters |
Annealing iterations (default = 100) |
ParafacStart |
binary argument for wether to initialize with Parafac factorization (default = 0) |
max_niter |
maximum number of iterations (default = 1000) |
convg_thres |
convergence threshold for difference in log likelihood (default = 10^-3) |
Sf_diag |
whether Sf is diagnal (default=1,diagnoal) |
list with components
B: |
q*r coefficient matrix for U~Y |
V |
list of length K-1. V[k] is a p*r coefficient matrix with columns of norm 1 |
U: |
Conditional expectation of U: n*r |
se2: |
scalar, var(E) |
Sf: |
r*r diagonal matrix, cov(F) |
rec: |
log likelihood for each iteration |
1 2 3 4 5 6 7 8 9 10 11 | sigmaF <- diag(c(100,64,36,16,4))
# F matrix n*r
Fmatrix1 <- matrix(MASS::mvrnorm(n=100,rep(0,5),sigmaF),100,5)
U<-Fmatrix1
V1 <- matrix(stats::rnorm(10*5),10,5)
V2 <- matrix(stats::rnorm(10*5),10,5)
L <- list(U,V1,V2)
X <- TensProd(L)
Y <- matrix(stats::rnorm(100*10),100,10)
R <-3
SupParafacEM(Y,X,R)
|
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