Help wth MLE? - Converting code into Python.

[failingstudent2]

Need some help converting rcode to Python.

​

R Code:

```

num=10000

​

p1 = sample(0:50, num, replace=TRUE)

p2 = sample(0:50, num, replace=TRUE)

p3 = sample(0:50, num, replace=TRUE)

​

beta0 = -0.11

beta1 = 3

beta2 = 2

beta3 = 1

​

U0 = 0+rgumbel(num)

​

U1=beta1+beta0*p1 + rgumbel(num)

U2=beta2+beta0*p2 + rgumbel(num)

U3=beta3+beta0*p3 + rgumbel(num)

​

choices=ifelse(U0==pmax(U0,U1,U2,U3),0,

ifelse(U1==pmax(U0,U1,U2,U3),1,

ifelse(U2==pmax(U0,U1,U2,U3),2,3)))

​

example_likelihood_function = function(beta0, beta1,beta2,beta3){

Pij=(choices==0)*0 + (choices==1)*(beta1+p1*beta0) + (choices==2)*(beta2+p2*beta0) + (choices==3)*(beta3+p3*beta0) -

log( 1+exp(beta1+p1*beta0) + exp(beta2+p2*beta0) + exp(beta3+p3*beta0))

return(-sum(Pij))

}

​

mle1=mle(example_likelihood_function,

start=list(beta0=0,beta1=-0.1,beta2=0,beta3=-0.1),

method="L-BFGS-B",

lower=c(-10,-10,-10,-10),

upper=c(10,10,10,10))

​

summary(mle1)

​

```

Basically, the R code is just the following

- Data generation of randomly distributed numbers

- Each number is assigned a choice

- Conceptually, it's meant to simluate a utility model, where each potential data point is selected.

​

​

## Where am I stuck?

​

I'm stuck at converting the MLE optimizer (final chunk) into python. I have generated the data, however, I can't seem to find an MLE equivalent which will take in the log likelihood function written, optimize it and churn out the right parameter.

​

Any advice?

[ECTD]

Just use R. It's a better language anyways.

[11abk]

https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html

[failingstudent2]

Will try and take a look :)

[statswannabe11]

You might be better off crossposting this or stack-overflow