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Hi all, I am trying to estimate a simple logit model. By using MLE, I am maximizing the log likelihood, with optim(). The thing is, each observation has different set of choice options, so I need a loop inside the objective function, which I think slows down the optimization process. The data is constructed so that each row represent the characteristics for one alternative, and CS is a

Hi all, Is there a way to do a matrix multiplication in a faster way? I am making a product of a matrix (composed of a lot of dummy variables) and a vector, and is there any way to make it faster? The simple X %*% y takes too long a time. I know using sparse matrix would help, but don't know how to do it i R. Thank you.

Hi all, I am trying to figure out a way to improve my code's efficiency by avoiding the use of loop. I want to calculate a conditional mean(?) given time. For example, from the data below, I want to calculate sum((value|choice==1)/sum(value)) across time. Is there a way to do it without using a loop? time cum_time choice value 1 4 1 3 1 4

Hi everyone, I am running this fixed effects model in R. The thing is, my own code and the canned routine keep returning different estimates for the fixed effects factors and intercept. (The other variables' estimates and R^2 are the same!!! weird!!) I attach my own code, just in case. I am pretty sure this would not have any fundamental error. Well, hopefully. I have been using this code