Commit | Line | Data |
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d1531659 | 1 | #' Generate a sample of (X,Y) of size n |
2 | #' @param covX covariance for covariates | |
3 | #' @param covY covariance for the response vector | |
4 | #' @param pi proportion for each cluster | |
5 | #' @param beta regression matrix | |
6 | #' @param n sample size | |
7 | #' @return list with X and Y | |
8 | #' @export | |
9 | #----------------------------------------------------------------------- | |
39046da6 BA |
10 | generateIO = function(covX, covY, pi, beta, n) |
11 | { | |
d1531659 | 12 | size_covX = dim(covX) |
13 | p = size_covX[1] | |
14 | k = size_covX[3] | |
15 | ||
16 | size_covY = dim(covY) | |
17 | m = size_covY[1] | |
18 | ||
19 | Y = matrix(0,n,m) | |
20 | BX = array(0, dim=c(n,m,k)) | |
21 | ||
22 | require(MASS) #simulate from a multivariate normal distribution | |
23 | for (i in 1:n) | |
24 | { | |
25 | for (r in 1:k) | |
26 | { | |
27 | BXir = rep(0,m) | |
28 | for (mm in 1:m) | |
29 | Bxir[[mm]] = X[i,] %*% beta[,mm,r] | |
30 | Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r]) | |
31 | } | |
32 | } | |
33 | ||
34 | return (list(X=X,Y=Y)) | |
39046da6 | 35 | } |