Styjun

I have a problem when I try to run the dffits() function for an object of my own logistic regression.
When I'm running dffits(log) I get the error message:
error in if (model$rank == 0) { : Argument is of length 0

However, when I'm using the inbuilt gym function (family = binomial), then dffits(glm) works just fine.

Here is my function for the logistic regression and a short example of my problem:

mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
mydata$rank <- factor(mydata$rank)
mydata$admit <- factor(mydata$admit)

logRegEst <- function(x, y, threshold = 1e-10, maxIter = 100)

 calcPi <- function(x, beta)
 
 beta <- as.vector(beta)
 return(exp(x %*% beta) / (1 + exp(x %*% beta)))
 

 beta <- rep(0, ncol(x)) # initial guess for beta

 diff <- 1000
 # initial value bigger than threshold so that we can enter our while loop

 iterCount = 0
 # counter to ensure we're not stuck in an infinite loop

 while(diff > threshold) # tests for convergence
 
 pi <- as.vector(calcPi(x, beta))
 # calculate pi by using the current estimate of beta

 W <- diag(pi * (1 - pi)) # calculate matrix of weights W

 beta_change <- solve(t(x) %*% W %*% x) %*% t(x) %*% (y - pi)
 # calculate the change in beta

 beta <- beta + beta_change # new beta
 diff <- sum(beta_change^2)
 # calculate how much we changed beta by in this iteration
 # if this is less than threshold, we'll break the while loop

 iterCount <- iterCount + 1
 # see if we've hit the maximum number of iterations
 if(iterCount > maxIter)
 stop("This isn't converging.")
 
 # stop if we have hit the maximum number of iterations
 
 df <- length(y) - ncol(x)
 # calculating the degrees of freedom by taking the length of y minus
 # the number of x columns
 vcov <- solve(t(x) %*% W %*% x)
 list(coefficients = beta, vcov = vcov, df = df)
 # returning results


logReg <- function(formula, data)

 mf <- model.frame(formula = formula, data = data)
 # model.frame() returns us a data.frame with the variables needed to use the
 # formula.
 x <- model.matrix(attr(mf, "terms"), data = mf)
 # model.matrix() creates a disign matrix. That means that for example the
 #"Sex"-variable is given as a dummy variable with ones and zeros.
 y <- as.numeric(model.response(mf)) - 1
 # model.response gives us the response variable.
 est <- logRegEst(x, y)
 # Now we have the starting position to apply our function from above.
 est$formula <- formula
 est$call <- match.call()
 est$data <- data
 # We add the formular and the call to the list.
 est$x <- x
 est$y <- y
 # We add x and y to the list.
 class(est) <- "logReg"
 # defining the class
 est



log <- logReg(admit ~ gre + gpa, data= mydata) 
glm <- glm(admit ~ gre + gpa, data= mydata, family = binomial)
dffits(glm)
dffits(log)

log$data
glm$data

I don't understand why mydata$rank == 0, because when I look at log$data I see that the rank is just defined as in glm$data.

I really appreciate your help!