glm - outlier detection and removal in R - r

Question

I constructed a binary logistic model. The response variable is binary. There are 4 regressors - 2 binary and 2 integers. I want to find the outliers and delete them. For this i have create some plots:
par(mfrow = c(2,2))
plot(hat.ep,rstudent.ep,col="#E69F00", main="hat-values versus studentized residuals",
xlab="Hat value", ylab="Studentized residual")
dffits.ep <- dffits(model_logit)
plot(id,dffits.ep,type="l", col="#E69F00", main="Index Plot",
xlab="Identification", ylab="Diffits")
cov.ep <- covratio(model_logit)
plot(id,cov.ep,type="l",col="#E69F00", main="Covariance Ratio",
xlab="Identification", ylab="Covariance Ratio")
cook.ep <- cooks.distance(model_logit)
plot(id,cook.ep,type="l",col="#E69F00", main="Cook's Distance",
xlab="Identification", ylab="Cook's Distance")
According to the plots there is an outlier. How can I identify which observation is the outlier?
I have tried :
> outlierTest(model_logit)
No Studentized residuals with Bonferonni p < 0.05
Largest |rstudent|:
rstudent unadjusted p-value Bonferonni p
1061 1.931043 0.053478 NA
Are there some other functions for outlier detection?

Answer 1

Well this answer comes quite late. I'm unsure if you have found the answer or not. Continuing further, in the absence of a minimum reproducible example, I'll attempt to answer the question using some dummy data and two custom functions. For a given continuous variable, outliers are those observations that lie outside of 1.5*IQR, where IQR, the ‘Inter Quartile Range’ is the difference between the 75th and 25th quartiles. I also recommend you to see this post containing far better solutions than my crude answer.
> df <- data.frame(X = c(NA, rnorm(1000), runif(20, -20, 20)), Y = c(runif(1000),rnorm(20, 2), NA), Z = c(rnorm(1000, 1), NA, runif(20)))
> head(df)
X Y Z
1 NA 0.8651 0.2784
2 -0.06838 0.4700 2.0483
3 -0.18734 0.9887 1.8353
4 -0.05015 0.7731 2.4464
5 0.25010 0.9941 1.3979
6 -0.26664 0.6778 1.1277
> boxplot(df$Y) # notice the outliers above the top whisker
Now, I'll create a custom function to detect the outliers and the other function will replace the outlier values with NA.
# this function will return the indices of the outlier values
> findOutlier <- function(data, cutoff = 3) {
## Calculate the sd
sds <- apply(data, 2, sd, na.rm = TRUE)
## Identify the cells with value greater than cutoff * sd (column wise)
result <- mapply(function(d, s) {
which(d > cutoff * s)
}, data, sds)
result
}
# check for outliers
> outliers <- findOutlier(df)
# custom function to remove outliers
> removeOutlier <- function(data, outliers) {
result <- mapply(function(d, o) {
res <- d
res[o] <- NA
return(res)
}, data, outliers)
return(as.data.frame(result))
}
> filterData<- removeOutlier(df, outliers)
> boxplot(filterData$Y)