implementing R's `glm` function when specifying family argument for binomial logistic regression
I've tried everything I can think of but I just started working with I tried several approaches but none seem to work. I've searched everywhere and can't find a clear answer. I've been struggling with this for a few days now and could really use some help. I'm trying to fit a logistic regression model using the `glm` function in R, but I'm running into issues when I specify the `family` argument... Specifically, I'm using R version 4.3.0 and my dataset has a binary response variable. Despite following the documentation, I keep getting the warning message: `Warning message: In eval(predvars, data, env) : NAs introduced by coercion`. Here is my attempt at fitting the model: ```r # Sample dataset df <- data.frame( outcome = c(1, 0, 1, 1, 0, 0, 1, 0, 1, 0), predictor1 = c(2.3, 1.5, 3.4, 2.0, 1.8, 1.2, 3.5, 2.1, 1.6, 2.4), predictor2 = c('A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B') ) # Fitting the model model <- glm(outcome ~ predictor1 + predictor2, family = binomial(link = 'logit'), data = df) ``` I expected the model to fit without warnings, but I suspect that the scenario may be related to how R is interpreting the `predictor2` variable, since it's a factor. I've checked for NAs in my dataset, and there don't seem to be any. I also tried converting `predictor2` to a factor explicitly using `as.factor()`, but the warning continues. ```r df$predictor2 <- as.factor(df$predictor2) model <- glm(outcome ~ predictor1 + predictor2, family = binomial(link = 'logit'), data = df) ``` Even after this conversion, the same warning appears. Could this be a question with how factors are handled in `glm`, or am I missing something else? Any insights would be greatly appreciated! This is part of a larger API I'm building. What's the best practice here? For context: I'm using R on Ubuntu. For context: I'm using R on Ubuntu. Has anyone else encountered this? My team is using R for this microservice. What's the correct way to implement this? Thanks for taking the time to read this! I'm using R 3.10 in this project. I'd love to hear your thoughts on this.