LassoCMA: L1 penalized logistic regression
In CMA: Synthesis of microarray-based classification

Description Usage Arguments Value Note Author(s) References See Also Examples

The Lasso (Tibshirani, 1996) is one of the most popular tools for simultaneous shrinkage and variable selection. Recently, Friedman, Hastie and Tibshirani (2008) have developped and algorithm to compute the entire solution path of the Lasso for an arbitrary generalized linear model, implemented in the package glmnet. The method can be used for variable selection alone, s. GeneSelection.
For S4 method information, see LassoCMA-methods.

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

`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.
`models`	a logical value indicating whether the model object shall be returned
`...`	Further arguments passed to the function `glmpath` from the package of the same name.

An object of class clvarseloutput.

For a strongly related method, s. ElasticNetCMA.
Up to now, this method can only be applied to binary classification.

Martin Slawski ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
Christoph Bernau bernau@ibe.med.uni-muenchen.de

Tibshirani, R. (1996)
Regression shrinkage and selection via the lasso.
Journal of the Royal Statistical Society B, 58(1), 267-288

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization
Paths for Generalized Linear Models via Coordinate Descent
http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf

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

### 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)

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

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Loaded glmnet 4.0-2
Warning message:
In lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs,  :
  one multinomial or binomial class has fewer than 8  observations; dangerous ground
binary Classification with Lasso
number of predictions: 13
number of missclassifications:  1 
missclassification rate:  0.077 
sensitivity: 0.8 
specificity: 1 
    predicted
true 0 1
   0 8 0
   1 1 4