gsim {plsgenomics} | R Documentation

## GSIM for binary data

### Description

The function `gsim` performs prediction using Lambert-Lacroix and Peyre's GSIM algorithm.

### Usage

    gsim(Xtrain, Ytrain, Xtest=NULL, Lambda, hA, hB=NULL, NbIterMax=50)

### Arguments

`Xtrain` | a (ntrain x p) data matrix of predictors. `Xtrain` must be a matrix. Each row corresponds to an observation and each column to a predictor variable.
`Ytrain` | a ntrain vector of responses. `Ytrain` must be a vector. `Ytrain` is a {1,2}-valued vector and contains the response variable for each observation.
`Xtest` | a (ntest x p) matrix containing the predictors for the test data set. `Xtest` may also be a vector of length p (corresponding to only one test observation). If `Xtest` is not equal to NULL, then the prediction step is made for these new predictor variables.
`Lambda` | a positive real value. `Lambda` is the ridge regularization parameter.
`hA` | a strictly positive real value. `hA` is the bandwidth for GSIM step A.
`hB` | a strictly positive real value. `hB` is the bandwidth for GSIM step B. if `hB` is equal to NULL, then hB value is chosen using a plug-in method.
`NbIterMax` | a positive integer. `NbIterMax` is the maximal number of iterations in the Newton-Rapson parts.

### Details

The columns of the data matrices `Xtrain` and `Xtest` may not be standardized, since standardizing is performed by the function `gsim` as a preliminary step before the algorithm is run.

The procedure described in Lambert-Lacroix and Peyre (2005) is used to estimate the projection direction beta. When `Xtest` is not equal to NULL, the procedure predicts the labels for these new predictor variables.

### Value

A list with the following components:

`Ytest` | the ntest vector containing the predicted labels for the observations from `Xtest`.
`beta` | the p vector giving the projection direction estimated.
`hB` | the value of hB used in step B of GSIM (value given by the user or estimated by plug-in if the argument value was equal to NULL)
`DeletedCol` | the vector containing the column number of `Xtrain` when the variance of the corresponding predictor variable is null. Otherwise `DeletedCol`=NULL
`Cvg` | the 0-1 value indicating convergence of the algorithm (1 for convergence, 0 otherwise).

### Author(s)

Sophie Lambert-Lacroix (http://www-lmc.imag.fr/lmc-sms/Sophie.Lambert) and Julie Peyre (http://www-lmc.imag.fr/lmc-sms/Julie.Peyre/).

### References

S. Lambert-Lacroix and Julie Peyre (2005). Local likelyhood regression in generalized linear single-index models with applications to microarray data, TR0563 IAP Statistics network (http://www.stat.ucl.ac.be/IAP/).

### See Also

`gsim.cv`, `mgsim`, `mgsim.cv`.

### Examples

    # load plsgenomics library
    library(plsgenomics)

    # load Colon data
    data(Colon)
    IndexLearn <- c(sample(which(Colon$Y==2),12),sample(which(Colon$Y==1),8))

    Xtrain <- Colon$X[IndexLearn,]
    Ytrain <- Colon$Y[IndexLearn]
    Xtest <- Colon$X[-IndexLearn,]

    # preprocess data
    resP <- preprocess(Xtrain= Xtrain, Xtest=Xtest,Threshold = c(100,16000),Filtering=c(5,500),log10.scale=TRUE,row.stand=TRUE)

    # perform prediction by GSIM
    res <- gsim(Xtrain=resP$pXtrain,Ytrain= Ytrain,Xtest=resP$pXtest,Lambda=10,hA=50,hB=NULL)

    res$Cvg
    sum(res$Ytest!=Colon$Y[-IndexLearn])

[Package plsgenomics version 1.2 Index]