bootSIR: Check for outliers in SIR models.
In hlorenzo/bootSIR: bootSIR: Check for outliers in SIR models

Description Usage Arguments Value References Examples

View source: R/bootSIR.R

This function takes a matrix X defining the same n individuals and a vector y defining also those individuals.

1
2
3

bootSIR(X, y, method = c("BOOT", "TTR", "MONO"), kernel = "normal",
  bandwidth = NULL, H = 5, nb.replications = 1000, pourcent = 0.1,
  plotBandwidthEst = F, NCORES = 1)

`X`	A matrix nxp describinf n observations through p varaibles.
`y`	A vector of n observations corresponding to the n rows of X.
`method`	Which method to be performed. One of BOOT for bootstrap, TTR for train-test replication and MONO for mono-model testing.
`kernel`	The kernel to be used, one of "normal" or "box". Default is "normal". Can be abbreviated. See `ksmooth`.
`bandwidth`	A real indicating the width of the kernel. If NULL, automatic kernel width tuning is used. Default is "NULL".
`H`	A positive real value. The number of slices in the SIR model. Default is 5.
`nb.replications`	An interger giving the number of repliccations for BOOT and TTR methods. Default is "1000".
`pourcent`	A real between 0 and 1. The proportion of observations to keep in the test dataset. Default is "0.1".
`plotBandwidthEst`	An integer. Wether or not to plot the EDR direction with the kernel estimation. Default is "FALSE".
`NCORES`	An integer. Fiwing the number of cores to be used in the sampling computations. Only BOOT method has been parallelized due to its computation time.

A list containing indices of outliers in the case of the 3 methods and also the extreme observations in the case of BOOT.

\insertAllCited

data(ozone)
# MONO method
output <- bootSIR(ozone[,-c(1,12,13)],ozone[,1],method="MONO")
# TTR method
# output <- bootSIR(ozone[,-c(1,12,13)],ozone[,1],method="TTR",
# plotBandwidthEst = FALSE,kernel="normal",nb.replications = 1000)
# BOOT method, using 5 cores of computation
# output <- bootSIR(ozone[,-c(1,12,13)],ozone[,1],method="BOOT",
# plotBandwidthEst = TRUE,kernel="normal",nb.replications = 5000,NCORES=5)