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ef67d338 BA |
1 | #' Generate a sample of (X,Y) of size n with default values |
2 | #' @param n sample size | |
3 | #' @param p number of covariates | |
4 | #' @param m size of the response | |
5 | #' @param k number of clusters | |
6 | #' @return list with X and Y | |
7 | #' @export | |
8 | generateXYdefault = function(n, p, m, k) | |
9 | { | |
39062512 BA |
10 | meanX = rep(0, p) |
11 | covX = diag(p) | |
12 | covY = array(dim=c(m,m,k)) | |
13 | for(r in 1:k) | |
39062512 | 14 | covY[,,r] = diag(m) |
39062512 BA |
15 | pi = rep(1./k,k) |
16 | #initialize beta to a random number of non-zero random value | |
17 | beta = array(0, dim=c(p,m,k)) | |
18 | for (j in 1:p) | |
19 | { | |
20 | nonZeroCount = sample(1:m, 1) | |
21 | beta[j,1:nonZeroCount,] = matrix(runif(nonZeroCount*k), ncol=k) | |
22 | } | |
23 | ||
24 | sample_IO = generateXY(meanX, covX, covY, pi, beta, n) | |
25 | return (list(X=sample_IO$X,Y=sample_IO$Y)) | |
ef67d338 BA |
26 | } |
27 | ||
28 | #' Initialize the parameters in a basic way (zero for the conditional mean, uniform for weights, | |
29 | #' identity for covariance matrices, and uniformly distributed for the clustering) | |
30 | #' @param n sample size | |
31 | #' @param p number of covariates | |
32 | #' @param m size of the response | |
33 | #' @param k number of clusters | |
34 | #' @return list with phiInit, rhoInit,piInit,gamInit | |
35 | #' @export | |
36 | basicInitParameters = function(n,p,m,k) | |
37 | { | |
39062512 BA |
38 | phiInit = array(0, dim=c(p,m,k)) |
39 | ||
40 | piInit = (1./k)*rep(1,k) | |
41 | ||
42 | rhoInit = array(dim=c(m,m,k)) | |
43 | for (i in 1:k) | |
44 | rhoInit[,,i] = diag(m) | |
45 | ||
46 | gamInit = 0.1 * matrix(1, nrow=n, ncol=k) | |
47 | R = sample(1:k, n, replace=TRUE) | |
48 | for (i in 1:n) | |
49 | gamInit[i,R[i]] = 0.9 | |
50 | gamInit = gamInit/sum(gamInit[1,]) | |
51 | ||
52 | return (list("phiInit" = phiInit, "rhoInit" = rhoInit, "piInit" = piInit, "gamInit" = gamInit)) | |
ef67d338 | 53 | } |