I would like to apply the traditional logistic regression, but there are limitations. My data has a total of 18 variables (or 36 coefficients in the LRM with intercept and dummy variables) and 101 observations. My predictors consist of a mix of continuous and categorical variables.

I obtained the following warning messages:

```
1: glm.fit: algorithm did not converge
2: glm.fit: fitted probabilities numerically 0 or 1 occurred
```

The p-values are all 1 or nearing 1. I increased the maximum iterations by specifying `control=glm.control(maxit=50)`

. The algorithm converged with a number of fisher scoring iterations of 28, but the following warning message still appears and the p-values are still so large at 1.

```
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
```

I understand from reading some posts here that this is due to **complete separation** due to **small size / sparse data, where some cells have missing observations**. I hope to seek some advice on how to proceed. Which of the following should I attempt to do:

- Exact Logistic Regression Model
- Penalised Regression (âą Penalized maximum likelihood logistic regression (Firth method)?)
- Bayesian Analysis

I explored (1) and read about `elrm`

in r, but I gathered that they only take categorical predictors. Any resources out there that I can refer to? Or perhaps the name of the method that is suitable, so that I can search on the internet.

Thanks!