mle

I'm attempting to write my own function to understand how the Poisson distribution behaves within a Maximum Likelihood Estimation framework (as it applies to GLM). I'm familiar with R's handy glm function, but wanted to try and hand-roll some code to understand what's going on: n <- 10000 # sample size b0 <- 1.0 # intercept b1 <- 0.2 # coefficient x <- runif(n=n, min=0, max=1.5) # generate covariate values lp <- b0+b1*x # linear predictor lambda <- exp(lp) # compute lamda y <- rpois(n=n, lambda=lambda) # generate y-values dta <- data.frame(y=y, x=x) # generate dataset negloglike <- function

I want to define my own density function to be used in the formula call to mle2 from R 's bbmle package. The parameters of the model are estimated but I cannot apply functions like residuals or predict on the returned mle2 object. This is an example in which I define a function for a simple Poisson model. library(bbmle) set.seed(1) hpoisson <- rpois(1000, 10) myf <- function(x, lambda, log = FALSE) { pmf <- (lambda^x)*exp(-lambda)/factorial(x) if (log) log(pmf) else pmf } myfit <- mle2(hpoisson ~ myf(lambda), start = list(lambda=9), data=data.frame(hpoisson)) residuals(myfit) In myfit , the

I am learning how the quasi-separation affects R binomial GLM. And I start to think that it does not matter in some circumstance . In my understanding, we say that the data has quasi separation when some linear combination of factor levels can completely identify failure/non-failure. So I created an artificial dataset with a quasi separation in R as: fail <- c(100,100,100,100) nofail <- c(100,100,0,100) x1 <- c(1,0,1,0) x2 <- c(0,0,1,1) data <- data.frame(fail,nofail,x1,x2) rownames(data) <- paste("obs",1:4) Then when x1=1 and x2=1 (obs 3) the data always doesn't fail. In this data, my