Simulate/Generate Data for Multinomial Logistic regression

How to simulate data for Multinomial Logistic regression?

For Example i want to generate a high dimensional data set with 90 subjects and 500 independent predictors. The ratio of Classes should given as 30:30:30.

Like, Class 1, Class 2 and Class 3 in equal proportion ?

This is what i have done so far. But i think this is not correct what i want is something different

# High Dimensional Data
mX = matrix(rnorm(45000), 90, 500)
vCoef1 = rep(0, 500)
vCoef2 = rnorm(500)
vCoef3 = rnorm(500)

# vector of probabilities
vProb = cbind(exp(mX%*%vCoef1), exp(mX%*%vCoef2), exp(mX%*%vCoef3))

# multinomial draws
mChoices = t(apply(vProb, 1, rmultinom, n = 1, size = 1))
y = apply(mChoices, 1, function(x) which(x==1))
dfM = cbind.data.frame(y = apply(mChoices, 1, function(x) which(x==1)), mX)
count(y)

The count of y shows different count for each class here. I am struggling to get equal ratio of classes.

Typically, what i want is 30 significant variables of the 500 predictor variables to be generated from three different normal distributions.

The predictors are to be generated from normal distribution with standard deviation of 1,2 or 3.

Among the 30 significant variables:
The first 10 variables to be generated from N(0,$$\sigma^2$$) for class 1, N(1,$$\sigma^2$$) for class 2 and N(2,$$\sigma^2$$) for class 3.
The Next 10 variables were generated from N(0,$$\sigma^2$$) for class 1 and N(1,$$\sigma^2$$) for class 2 and 3.
The next 10 variables were generated from N(0,$$\sigma^2$$) for class 1 and 2 and N(1,$$\sigma^2$$) for class 3

And the remaining 470 predictor variables to be generated from one normal distribution N(0,$$\sigma^2$$).

• Can you post what you have done so far so we can help you? – Jay Schyler Raadt Oct 15 '18 at 17:29
• Thank you for response, i have posted my work done so far @JaySchylerRaadt – Abhijeet Patil Oct 15 '18 at 18:01
• @Abhijeet: Did you by any chance find a solution yet? – Mr. Zen Nov 14 '18 at 9:58
• I did not get help here but i was able to solve it. I am posting the solution hope it might help you.. – Abhijeet Patil Nov 14 '18 at 15:20
• @Mr.Zen I have posted the solution. – Abhijeet Patil Nov 14 '18 at 15:30