# Roughenen Random Forests - C2 (RRFC2)

### Description

RRFC2 algorithm

1.Impose missing values under the mechanism of missing completely at random on all covariates of the training dataset.

2.Impute the missing values in a continuous variable by its minimum value and impute the missing values in a categorical variable by its mode value (Minimum-value /mode imputation).

3.Build one tree in random forests using the above imputed training dataset, and then use it to predict the binary outcomes in the original testing dataset.

4.Repeat 1 to 3 for `number.trees`

times.

### Usage

1 |

### Arguments

`dat` |
A data frame containing both training and testing datasets |

`yvar` |
The column number of the binary outcome variable, a factor variable. The default value is set as ncol(dat) |

`tr` |
Row numbers of all training data |

`te` |
Row numbers of all testing data |

`mispct` |
Rate of missing data, ranging from 0 to 1 |

`number.trees` |
Number of trees used in roughened random forests |

### Value

A prediction matrix. Each column shows the predicted values by a single tree. Each row is sequentially associated with the observations in the testing dataset. Each cell value is either 0 or 1.

### Author(s)

Kuangnan Xiong

### References

Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.

Liaw, A. & Wiener, M., 2002. Classification and regression by randomForest. R News, 2(3), pp. 18-22.

Xiong, Kuangnan. "Roughened Random Forests for Binary Classification." PhD diss., State University of New York at Albany, 2014.

### See Also

`rrfa`

, `rrfb`

, `rrfc1`

,`rrfc3`

, `rrfc4`

, `rrfc5`

, `rrfc6`

, `rrfc7`

, `rrfd`

, `rrfe`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
if(require(MASS)){
if(require(caTools)){
dat=rbind(Pima.tr,Pima.te)
number.trees=50
#number.trees=500
tr=1:200
te=201:532
mispct=0.2
yvar=ncol(dat)
#AUC value for the testing dataset based on the original random forests
rf=randomForest(dat[tr,-yvar],dat[tr,yvar],dat[te,-yvar],ntree=number.trees)
print(colAUC(rf$test$votes[,2],dat[te,yvar]))
#AUC value for the testing dataset based on RRFC2
pred.rrfc2=rrfc2(dat,yvar,tr,te,mispct,number.trees)
print(colAUC(apply(pred.rrfc2$pred,1,mean),dat[te,yvar]))
}}
``` |

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