LassoCMA {CMA} | R Documentation

## L1 penalized logistic regression

### Description

The Lasso (Tibshirani, 1996) is one of the most popular tools for simultaneous shrinkage and variable selection. Recently, Young-Park and Hastie (2007) have developped and algorithm to compute the entire solution path of the Lasso for an arbitrary generalized linear model, implemented in the package `glmpath`. The method can be used for variable selection alone, s. `GeneSelection`.

For `S4` method information, see `LassoCMA-methods`.

### Usage

    LassoCMA(X, y, f, learnind, norm.fraction = 0.1, ...)

### Arguments

`X`

Gene expression data. Can be one of the following:

- A `matrix`. Rows correspond to observations, columns to variables.
- A `data.frame`, when `f` is not missing (s. below).
- An object of class `ExpressionSet`. note: by default, the predictors are scaled to have unit variance and zero mean. Can be changed by passing `standardize = FALSE` via the `...` argument.

`y`

Class labels. Can be one of the following:

- A `numeric` vector.
- A `factor`.
- A `character` if `X` is an `ExpressionSet` that specifies the phenotype variable.
- `missing`, if `X` is a `data.frame` and a proper formula `f` is provided.

WARNING: The class labels will be re-coded to range from `0` to `K-1`, where `K` is the total number of different classes in the learning set.

`f` | A two-sided formula, if `X` is a `data.frame`. The left part correspond to class labels, the right to variables.
`learnind` | An index vector specifying the observations that belong to the learning set. May be `missing`; in that case, the learning set consists of all observations and predictions are made on the learning set.
`norm.fraction` | L1 Shrinkage intensity, expressed as the fraction of the coefficient L1 norm compared to the maximum possible L1 norm (corresponds to `fraction = 1`). Lower values correspond to higher shrinkage. Note that the default (0.1) need not produce good results, i.e. tuning of this parameter is recommended.
`...` | Further arguments passed to the function `glmpath` from the package of the same name.

### Value

An object of class `clvarseloutput`.

### Note

For a strongly related method, s. `ElasticNetCMA`.

Up to now, this method can only be applied to binary classification.

### Author(s)

Martin Slawski martin.slawski@campus.lmu.de

Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix

### References

Tibshirani, R. (1996)

Regression shrinkage and selection via the lasso.

Journal of the Royal Statistical Society B, 58(1), 267-288

Young-Park, M., Hastie, T. (2007)

L1-regularization path algorithm for generalized linear models.

Journal of the Royal Statistical Society B, 69(4), 659-677

### See Also

`compBoostCMA`, `dldaCMA`, `ElasticNetCMA`, `fdaCMA`, `flexdaCMA`, `gbmCMA`, `knnCMA`, `ldaCMA`, `nnetCMA`, `pknnCMA`, `plrCMA`, `pls_ldaCMA`, `pls_lrCMA`, `pls_rfCMA`, `pnnCMA`, `qdaCMA`, `rfCMA`, `scdaCMA`, `shrinkldaCMA`, `svmCMA`

### Examples

    ### load Golub AML/ALL data
    data(golub)
    ### extract class labels
    golubY <- golub[,1]
    ### extract gene expression
    golubX <- as.matrix(golub[,-1])
    ### select learningset
    ratio <- 2/3
    set.seed(111)
    learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
    ### run L1 penalized logistic regression (no tuning)
    lassoresult <- LassoCMA(X=golubX, y=golubY, learnind=learnind, norm.fraction = 0.2)
    show(lassoresult)
    ftable(lassoresult)
    plot(lassoresult)

[Package CMA version 1.2.0 Index]