ipmpartynew: IPM casewise with CIT-RF by 'party' for new cases, whose...
In IPMRF: Intervention in Prediction Measure (IPM) for Random Forests

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

The IPM of a new case, i.e. one not used to grow the forest and whose true response does not need to be known, is computed as follows. The new case is put down each of the ntree trees in the forest. 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 new case is obtained by averaging those percentages over the ntree trees. The random forest is based on CIT (Conditional Inference Trees).

1	ipmpartynew(marbol, da, ntree)

`marbol`	Random forest obtained with `cforest` . Responses in the training set can be of the same type supported by `cforest`, not only numerical or nominal, but also ordered responses, censored response variables and multivariate responses.
`da`	Data frame with the predictors only, not responses, for the new cases. Each row corresponds to an observation and each column corresponds to a predictor, which obviously must be the same variables used as predictors in the training set. 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 new cases. It is a matrix with as many rows as cases are in da, and as many columns as predictors are in da. IPM can be estimated for any kind of RF computed by cforest, including multivariate RF.

See Epifanio (2017) about advantages and limitations of IPM, and about the parameters to be used in cforest.

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, ipmrf, ipmranger, ipmrfnew, ipmrangernew, ipmgbmnew

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

## -------------------------------------------------------
## Example from \code{\link[party]{varimp}} in \pkg{party}
## Classification RF
## -------------------------------------------------------


library(party)


#IMP based on CIT-RF (party package)
ntree=50
#readingSkills: data from party package
da=readingSkills[,1:3] 
set.seed(290875)
readingSkills.cf3 <- cforest(score ~ ., data = readingSkills,
control = cforest_unbiased(mtry = 3, ntree = 50))

#new case
nativeSpeaker='yes'
age=8
shoeSize=28
da1=data.frame(nativeSpeaker, age, shoeSize)

#IPM case-wise computed for new cases for party package
pupfn=ipmpartynew(readingSkills.cf3,da1,ntree)
pupfn