SQBreg: SQBreg

In SQB: Sequential Bagging on Regression

Description

Do prediction using sequential bagging method with tree based learning algorithm

Usage

SQBreg(data.train, data.test, y, res, reps, cores, FunKDE, control,
  SQBalgorithm.1, SQBalgorithm.2, k, ncomp, nnet.size)

Arguments

data.train	Training dataset
data.test	Testing dataset
y	Numeric response variable
res	Resampling size. Could not be greater than the input data size.
reps	Replicates for the first bagging, default 100
cores	Use multi-cores, default one core, use cores='maxcores' for full use.
FunKDE	Kernel density estimate function. Use different kernel to fit, default logistic kernel.
control	Use in rpart package, rpart.control to tune the control
SQBalgorithm.1	Use for the initial training. Option: CART, lm(default), knnreg, nnet, PCR.
SQBalgorithm.2	Use for the last training. Option: CART, lm(default), knnreg, nnet, PCR.
k	The number of nearest neighbour used for knnreg
ncomp	The number of component used for PCR
nnet.size	The number of hidden layer and neuron for nnet

Value

Given testing set input, make a regression prediction

References

Breiman L., Friedman J. H., Olshen R. A., and Stone, C. J. (1984) Classification and Regression Trees.

Soleymani, M. and Lee S.M.S(2014). Sequential combination of weighted and nonparametric bagging for classification. Biometrika, 101, 2, pp. 491–498.

Efron, B. (1979). Bootstrap methods: Another lo ok at the jackknife. Ann. Statist., 7(1):1-26.

Examples

data(hills, package="MASS")
rt.df <- hills[sample(nrow(hills)),]
data.train <- rt.df[1 : (length(rt.df[, 1]) - 1), ]
data.test <- rt.df[-(1 : (length(rt.df[, 1]) - 1)),]
fit <- SQBreg(data.train, data.test, reps = 30, y = "time")
fit

SQB documentation built on May 2, 2019, 9:27 a.m.