rrp.predict: Nonparametric nearest neighbor predictor using RRP...

In rrp: Random Recursive Partitioning

Description

This method is a simple nearest neighbor based on the RRP dissimilarity matrix. It can be used also as a supervised method, i.e. using the outcome variable in the construction of the RRP dissimilarity matrix and by imputing missing values to the test set.

Usage

rrp.predict(x, y, train, test, k = 1)

Arguments

x	a XPtr object
y	numeric vector of train outcomes
train	the vecotr of indexes of the training set
test	the vector of training indexes of test set
k	number of nearest to consider

Details

From version 1.6 of the package the RRP matrix is stored as an external pointer to avoid duplications. This allow to work on bigger datasets. Hence this function no longer accepts dist objects.

Value

a vector of type numeric with predicted outcomes.

Author(s)

S.M. Iacus

References

Iacus, S.M., Porro, G. (2009) Random Recursive Partitioning: a matching method for the estimation of the average treatment effect, Journal of Applied Econometrics, 24, 163-185.

Iacus, S.M., Porro, G. (2007) Missing data imputation, matching and other applications of random recursive partitioning, Computational Statistics and Data Analysis, 52, 2, 773-789.

See Also

rrp.dist, rrp.class

Examples

require(MASS)
attach(birthwt)

race <- factor(race, labels= c("white", "black", "other"))
ptd <- factor(ptl>0)
ftv <- factor(ftv)
levels(ftv)[-(1:2)] <- "2+"
table(ftv)

bwt <- data.frame(bwt, age, lwt, race, smoke=(smoke>0), ptd, 
ht=(ht>0), ui = (ui>0), ftv)

detach()
rm(race, ptd, ftv)

set.seed(123)
n <- dim(bwt)[1]
test <- sample(1:n, 15)
train <- (1:n)[-test]

D <- rrp.dist(bwt[,-1])
true.wht <- bwt$bwt[test]
pred.wht <- rrp.predict(D, bwt$bwt[train], train, test)
mean(pred.wht-true.wht)
sd(pred.wht-true.wht)

mod <- lm(bwt ~ ., data=bwt[train,])
pred.wht <- predict(mod, newdata = bwt[test,])
mean(pred.wht-true.wht)
sd(pred.wht-true.wht)

rrp documentation built on May 2, 2019, 5:25 p.m.