## Fitting a Generalized Linear Model (GLM) in R

I am learning about Generalized Linear Models and the use of the R statistical package, but, unfortunately, I am unable to understand some fundamental concepts.

I am trying to develop a GLM – Poisson model but using a specific log link function. The function is of the form

$$ln(E(y_i)) = ln(beta_1) + beta_2 ln(text{exp}_1) + beta_3 ln(text{exp}_2).$$

In this equation, $text{exp}_1$ and $text{exp}_2$ are measures of exposure in the model. From my understanding, in R, I would first load all the data and ensure it was properly set-up. I then believe I should be running:

```
model = glm(formula = Y~exp1+exp2, family=poisson(link="log"),data=CSV_table)
```

As I am new to GLMs and R, I am not exactly sure what specifying poisson(link=”log”) does. I hope this question isn’t too trivial. I have been trying to google clear concise explanations online for hours; however many answers/links assume a level of knowledge higher than mine.