ipmrf: IPM casewise with CART-RF by 'randomForest' for OOB samples
In IPMRF: Intervention in Prediction Measure (IPM) for Random Forests

Description Usage Arguments Details Value Note Author(s) References See Also Examples

The IPM for a case in the training set is calculated by considering and averaging over only the trees where the case belongs to the OOB set. The case is put down each of the trees where the case belongs to the OOB set. For each tree, the case goes from the root node to a leaf through a series of nodes. The variable split in these nodes is recorded. The percentage of times a variable is selected along the case's way from the root to the terminal node is calculated for each tree. Note that we do not count the percentage of times a split occurred on variable k in tree t, but only the variables that intervened in the prediction of the case. The IPM for this case is obtained by averaging those percentages over only the trees where the case belongs to the OOB set. The random forest is based on CART.

1	ipmrf(marbolr, da, ntree)

`marbolr`	Random forest obtained with `randomForest`. Responses can be of the same type supported by `randomForest`. Note that not only numerical or nominal, but also ordered responses, censored response variables and multivariate responses can be considered with `ipmparty`.
`da`	Data frame with the predictors only, not responses, of the training set used for computing marbolr. Each row corresponds to an observation and each column corresponds to a predictor. Predictors can be numeric, nominal or an ordered factor.
`ntree`	Number of trees in the random forest.

All details are given in Epifanio (2017).

It returns IPM for cases in the training set. It is estimated when they are OOB observations. It is a matrix with as many rows as cases are in da, and as many columns as predictors are in da.

See Epifanio (2017) about the parameters of RFs to be used, the advantages and limitations of IPM, and in particular when CART is considered with predictors of different types.

Irene Epifanio

Pierola, A. and Epifanio, I. and Alemany, S. (2016) An ensemble of ordered logistic regression and random forest for child garment size matching. Computers & Industrial Engineering, 101, 455–465.

Epifanio, I. (2017) Intervention in prediction measure: a new approach to assessing variable importance for random forests. BMC Bioinformatics, 18, 230.

ipmparty, ipmranger, ipmpartynew, ipmrfnew, ipmrangernew, ipmgbmnew

#Note: more examples can be found at 
#https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1650-8

## Not run: 

library(mlbench)
#data used by Breiman, L.: Random forests. Machine Learning 45(1), 5--32 (2001)
data(PimaIndiansDiabetes2) 
Diabetes <- na.omit(PimaIndiansDiabetes2)

set.seed(2016)
require(randomForest)
ri<- randomForest(diabetes  ~ ., data=Diabetes, ntree=500, importance=TRUE,
keep.inbag=TRUE,replace = FALSE) 

#GVIM and PVIM (CART-RF)
im=importance(ri)
im
#rank
ii=apply(im,2,rank)
ii

#IPM based on CART-RF (randomForest package)
da=Diabetes[,1:8]
ntree=500
#IPM case-wise computed with OOB 
pupf=ipmrf(ri,da,ntree) 

#global IPM
pua=apply(pupf,2,mean) 
pua

#IPM by classes
attach(Diabetes)
puac=matrix(0,nrow=2,ncol=dim(da)[2])
puac[1,]=apply(pupf[diabetes=='neg',],2,mean) 
puac[2,]=apply(pupf[diabetes=='pos',],2,mean) 
colnames(puac)=colnames(da)
rownames(puac)=c( 'neg', 'pos')
puac

#rank IPM 
#global rank 
rank(pua) 
#rank by class
apply(puac,1,rank) 

## End(Not run)