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Fill matrix with path

I am trying to create a matrix n

on k

with k

mvn covariates using a loop. Quite simple but doesn't work yet ... Here is my code:

n=1000
k=5
p=100
mu=0
sigma=1
x=matrix(data=NA, nrow=n, ncol=k)


for (i in 1:k){
        x [[i]]= mvrnorm(n,mu,sigma)
       }

What's missing?

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2 answers


I see a few things here:

  • You might want to set a random seed for replicability ( set.seed(20430)

    ). This means that every time you run the code, you get exactly the same set of pseudo-random variations.
  • Then your data will simply be independent; they won't actually have a multidimensional structure (although that might be what you want). In general, if you want to generate multidimensional data, you should use ? Mvrnorm , from MASS . (For more information, see here .)
  • As a minor point, if you want the standard normal data, you do not need to specify mu = 0

    and sigma = 1

    as these are the defaults for rnorm()

    .
  • You don't need a loop to fill a matrix in R, just generate as many values ​​as you want and add them directly using the argument data=

    in the function matrix()

    . If you were really tied to using a loop, you should probably use a double loop so that you iterate over the columns and, within each loop, iterate over the rows. (Note that this is a very inefficient way to code in R - although I do it all the time ;-).
  • Finally, I cannot tell you what is p

    supposed to be done in your code.

Here's an easy way to do what you seem to be going to do:



set.seed(20430)
n   = 1000
k   = 5
dat = rnorm(n*k)
x   = matrix(data=dat, nrow=n, ncol=k)

If you really want to use loops, you can do it like this:

mu    = 0
sigma = 1
x     = matrix(data=NA, nrow=n, ncol=k)

for(j in 1:k){
   for(i in 1:n){
        x[i,j] = rnorm(1, mu, sigma)
   }
}
+8


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A=c(2,3,4,5);# In your case row terms
B=c(3,4,5,6);# In your case column terms
x=matrix(,nrow = length(A), ncol = length(B));
for (i in 1:length(A)){
     for (j in 1:length(B)){
          x[i,j]<-(A[i]*B[j])# do the similarity function, simi(A[i],B[j])       
     }
}
x # matrix is filled


I thought about my problem.

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