X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FgenerateIO.R;h=5f19488bb80ef7b04d16e305ff2dcef7d4cdcdc9;hb=b4476024967cdd601bf82ff4108f5c2beb855e9e;hp=9e84af5a282d4f81d683c38946cd5b7a15ebb364;hpb=493a35bfea6d1210c94ced8fbfe3e572f0389ea5;p=valse.git diff --git a/R/generateIO.R b/R/generateIO.R index 9e84af5..5f19488 100644 --- a/R/generateIO.R +++ b/R/generateIO.R @@ -1,25 +1,36 @@ -library(MASS) #simulate from a multivariate normal distribution +#' Generate a sample of (X,Y) of size n +#' @param covX covariance for covariates (of size p*p*K) +#' @param covY covariance for the response vector (of size m*m*K) +#' @param pi proportion for each cluster +#' @param beta regression matrix +#' @param n sample size +#' +#' @return list with X and Y +#' @export +#----------------------------------------------------------------------- +generateIO = function(covX, covY, pi, beta, n) +{ + p = dim(covX)[1] -generateIO = function(meanX, covX, covY, pi, beta, n){ #don't need meanX - size_covX = dim(covX) - p = size_covX[1] - k = size_covX[3] - - size_covY = dim(covY) - m = size_covY[1] - - Y = matrix(0,n,m) - BX = array(0, dim=c(n,m,k)) - - for(i in 1:n){ - for(r in 1:k){ - BXir = rep(0,m) - for(mm in 1:m){ - Bxir[[mm]] = X[i,] %*% beta[,mm,r] - } - Y[i,]=Y[i,] + pi[[r]] * mvrnorm(1,BXir, covY[,,r]) - } - } - - return(list(X,Y)) -} \ No newline at end of file + m = dim(covY)[1] + k = dim(covY)[3] + + Y = matrix(0,n,m) + require(mvtnorm) + X = rmvnorm(n, mean = rep(0,p), sigma = covX) + + require(MASS) #simulate from a multivariate normal distribution + for (i in 1:n) + { + + for (r in 1:k) + { + BXir = rep(0,m) + for (mm in 1:m) + BXir[mm] = X[i,] %*% beta[,mm,r] + Y[i,] = Y[i,] + pi[r] * mvrnorm(1,BXir, covY[,,r]) + } + } + + return (list(X=X,Y=Y)) +}