# make_numeric_sets: Transforming the set of predictors into a numeric set In CARRoT: Predicting Categorical and Continuous Outcomes Using One in Ten Rule

## Description

Function which turns a set of predictors containing non-numeric variables into a fully numeric set

## Usage

 `1` ```make_numeric_sets(a,ai,k,vari,ra,l,mode) ```

## Arguments

 `a` An M x N matrix, containing all possible subsets (N overall) of the size M of predictors' indices; therefore each column of `a` defines a unique subset of the predictors `ai` array of indices of the array `a` `k` index of the array `ai` `vari` set of all predictors `ra` array of sample indices of `vari` `l` size of the sample `mode` `'binary'` (logistic regression), `'multin'` (multinomial regression)

## Details

Function transforms the whole set of predictors into a numeric set by consecutively calling function `make_numeric` for each predictor

## Value

Returns a list containing two objects: `tr` and `test`

 `tr` training set transformed into a numeric one `test` test set transformed into a numeric one

`make_numeric`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22``` ```#creating a categorical numeric variable a<-t(rmultinom(100,1,c(0.2,0.3,0.5)))%*%c(1,2,3) #creating an analogous non-numeric variable c<-array(NA,100) c[a==1]='red' c[a==2]='green' c[a==3]='blue' #creating a data-set b<-data.frame(matrix(c(a,rbinom(100,1,0.3),runif(100,0,1)),ncol=3)) #making the first column of the data-set non-numeric b[,1]=data.frame(c) #running the function make_numeric_sets(combn(3,2),1:3,1,b,sample(1:100,60),100,"binary") ```