Unexpected NA values in R's pnorm function when using vectorization with large datasets
I recently switched to I've been struggling with this for a few days now and could really use some help... I'm relatively new to this, so bear with me. I'm experiencing unexpected NA values when I apply the `pnorm` function to a large numeric vector in R. I have a dataset of 2 million observations that I generated using a normal distribution, and I'm trying to compute the cumulative probabilities for each value. However, after calling `pnorm` on the entire vector, some of the results are returning NA instead of the expected probabilities. Here’s a snippet of my code: ```r set.seed(123) values <- rnorm(2000000, mean = 0, sd = 1) probabilities <- pnorm(values) ``` After running this, I checked for NA values using `sum(is.na(probabilities))`, and it returned a count greater than zero, specifically 12345 NAs. I also tried running `pnorm` on a smaller subset of the data, and it worked fine, returning valid probabilities without any NA values. I suspect this might be an scenario with floating-point precision or values that are extremely far from the mean. Additionally, I have verified that the input vector does not contain any NA values prior to the call to `pnorm`. I tried using `na.omit(values)` before passing it to `pnorm`, but that didn't resolve the scenario. I'm currently using R version 4.2.1 and have checked that my packages are up to date. Is there a known scenario with `pnorm` handling large vectors or extreme values? Any suggestions on how to troubleshoot or resolve this would be greatly appreciated. I'd really appreciate any guidance on this. Has anyone else encountered this? For reference, this is a production service. What am I doing wrong?