# get_probabilities: Probabilities for multinomial regression In CARRoT: Predicting Categorical and Continuous Outcomes Using One in Ten Rule

## Description

Function which computes probabilities of outcomes on the test set by applying regression parameters inferred by a run on the training set. Works for logistic or multinomial regression

## Usage

 `1` ```get_probabilities(trset,testset,outc,mode) ```

## Arguments

 `trset` values of predictors on the training set `testset` values of predictors on the test set `outc` values of outcomes on the training set `mode` `'binary'` (logistic regression) or `'multin'` (multinomial regression)

## Details

In binary mode this function computes the probabilities of the event '0'. In multinomial mode computes the probabilities of the events '0','1',...,'N-1'.

## Value

Probabilities of the outcomes. In `'binary'` mode returns an array of the size of the number of observations in a testset. In `'multin'` returns an M x N matrix where M is the size of the number of observations in a testset and N is the number of unique outcomes minus 1.

Function uses `multinom` and `coef`
 ```1 2 3 4 5``` ```trset<-matrix(c(rbinom(70,1,0.5),runif(70,0.1)),ncol=2) testset<-matrix(c(rbinom(10,1,0.5),runif(10,0.1)),ncol=2) get_probabilities(trset,testset,rbinom(70,1,0.6),'binary') ```