[英]How can I simulate 200 matrices (20*100) in R from a multivariate normal distribution?
I'm using the following code to simulate 20 observations for 100 predictor variables (features). 我正在使用以下代码模拟100个预测变量(功能)的20个观察值。 I want to run the simulation 200 times. 我想运行200次模拟。 Somehow it doesn't feel right to add a second 'for' loop to create a list of matrices. 不知何故添加第二个“ for”循环来创建矩阵列表是不合适的。 Do you have any suggestions on how to efficiently simulate several matrices from a multivariate normal distribution? 您是否对如何从多元正态分布中有效地模拟几个矩阵有任何建议?
x <- matrix(rep(NA, 20*100), 20, 100)
for (i in 1:20) {
x[i, ] <- mvrnorm(n = 1, mu = rep(0, 100), Sigma = diag(100))
}
Thank you! 谢谢!
If you really need no correlation, simply use 如果您真的不需要关联,只需使用
x = array( rnorm(200*20*100), dim=c(200,20,100) )
Your code could be abbreviated to 您的代码可以缩写为
library(mvtnorm)
x <- rmvnorm( n=20, mean=rep(0,100), sigma=diag(100) )
Now in order to have 200 of such matrices, I suggest the outer 'for' loop: 现在,为了拥有200个这样的矩阵,我建议使用外部“ for”循环:
x <- array( dim=c(200,20,100) )
for (i in 1:200) {
x[i,,] <- rmvnorm( n=20, mean=rep(0,100), sigma=diag(100) )
}
lapply(1:200,function(x) rmvnorm( n=20, mean=rep(0,100), sigma=diag(100) ))
将为您提供此类矩阵的列表。
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