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

## Description

Function which makes a prediction for multinomial/logistic regression based on the given cut-off value and probabilities.

## Usage

 `1` ```get_predictions(p,k,cutoff,cmode,mode) ```

## Arguments

 `p` probabilities of the outcomes for the test set given either by an array (logistic regression) or by a matrix (multinomial regression) `k` size of the test set `cutoff` cut-off value of the probability `cmode` `'det'` or `''`; `'det'` always predicts the more likely outcome as determined by the odds ratio; `''` predicts certain outcome with probability corresponding to its odds ratio (more conservative). Option available for multinomial/logistic regression `mode` `'binary'` (logistic regression), `'multin'` (multinomial regression)

## Value

Outputs the array of the predictions of the size of `p`.

Uses `rbinom`, `rmultinom`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```#binary mode get_predictions(runif(20,0.4,0.6),20,0.5,'det','binary') #creating a data-set for multinomial mode p1<-runif(20,0.4,0.6) p2<-runif(20,0.1,0.2) p3<-1-p1-p2 #running the function get_predictions(matrix(c(p1,p2,p3),ncol=3),20,0.5,'det','multin') ```

### Example output

```     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,]    0    1    0    0    1    1    0    0    0     1     0     1     1     0
[,15] [,16] [,17] [,18] [,19] [,20]
[1,]     1     0     1     0     1     0
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
[1,]    0    0    2    0    2    0    2    0    0     0     0     0     0     0
[,15] [,16] [,17] [,18] [,19] [,20]
[1,]     0     2     2     0     0     0
```

CARRoT documentation built on June 8, 2021, 5:09 p.m.