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Last data update: 2014.03.03

R: Prediction of response

predict.LarsPath

R Documentation

Prediction of response

Description

Predict response of a new sample Xnew at a given level of penalty

Usage

## S3 method for class 'LarsPath'
predict(object, Xnew, lambda, mode = c("fraction",
  "lambda", "norm"), ...)

Arguments

`object`	a LarsParth object
`Xnew`	a matrix (of size n*object@p) of covariates.
`lambda`	If mode ="norm", lambda represents the l1-norm of the coefficients with which we want to predict. If mode="fraction", lambda represents the ratio (l1-norm of the coefficientswith which we want to predict)/(l1-norm maximal of the LarsPath object).
`mode`	"fraction", "lambda" or "norm".
`...`	other arguments. Not used.

Value

The predicted response

Author(s)

Quentin Grimonprez

Examples

dataset=simul(50,10000,0.4,10,50,matrix(c(0.1,0.8,0.02,0.02),nrow=2))
result=HDlars(dataset$data[1:40,],dataset$response[1:40])
y=predict(result,dataset$data[41:50,],0.3,"fraction")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(HDPenReg)
Loading required package: rtkore
Loading required package: Rcpp

Attaching package: 'rtkore'

The following object is masked from 'package:Rcpp':

    LdFlags

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HDPenReg/predict.LarsPath.Rd_%03d_medium.png", width=480, height=480)
> ### Name: predict.LarsPath
> ### Title: Prediction of response
> ### Aliases: predict.LarsPath
> 
> ### ** Examples
> 
> dataset=simul(50,10000,0.4,10,50,matrix(c(0.1,0.8,0.02,0.02),nrow=2))
> result=HDlars(dataset$data[1:40,],dataset$response[1:40])
> y=predict(result,dataset$data[41:50,],0.3,"fraction")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>