Referring to the simulation approach suggested by Snow for ordinal logistic regression: https://stats.stackexchange.com/a/22410/231675
Here is a simple example with ordinal regression:
library(rms) tmpfun <- function(n, beta0, beta1, beta2) { x <- runif(n, 0, 10) eta1 <- beta0 + beta1*x eta2 <- eta1 + beta2 p1 <- exp(eta1)/(1+exp(eta1)) p2 <- exp(eta2)/(1+exp(eta2)) tmp <- runif(n) y <- (tmp < p1) + (tmp < p2) fit <- lrm(y~x) fit$stats[5] } out <- replicate(1000, tmpfun(100, -1/2, 1/4, 1/4)) mean( out < 0.05 )
I am looking to do the same thing for Difference-in-Difference Analysis. I am not sure how to adjust the parameters to fit a Difference-in-Difference. Does anybody have any suggestions?