Introduction to Quantitative Research Methods > Binary regression analysis > Predicted probabilities

As you may have already realised, log-odds are not straight-forward. This is why we use the `predict()`

function which give us predictions for Y, the dependent variable.

`probabilities<-predict(logit.model,type = "response")`

The final section include’s the use of the `effects`

package developed by Fox, et al. The `effects`

package creates plots for various statistical models. In this example I will show you how it works with a logistic model- but it works with linear models, mixed effects models and many other.

First you have to install the `effects`

package. The easiest way to visualise your model is by using the `allEffects()`

function in combination with the `plot()`

function.

```
library(effects)
plot(allEffects(logit.model))
```

**References**

Fox, J. (2003) Effect displays in R for generalised linear models. Journal of Statistical Software 8:15, 1–27, http://www.jstatsoft.org/v08/i15/.