Classify using the Lasso algorithm as implemented in the glmnet package
Usage
lassoClass(object, groups)
Arguments
object
object containing the expression measurements; currently the
only method supported is one for ExpressionSet objects
groups
character string indicating the column containing the class membership
Value
object of class glmnet
Author(s)
Willem Talloen
References
Goehlmann, H. and W. Talloen (2009). Gene Expression Studies Using Affymetrix
Microarrays, Chapman & Hall/CRC, pp. 183, 205 and 212.
See Also
glmnet
Examples
if (require(ALL)){
data(ALL, package = "ALL")
ALL <- addGeneInfo(ALL)
ALL$BTtype <- as.factor(substr(ALL$BT,0,1))
resultLasso <- lassoClass(object = ALL, groups = "BTtype")
plot(resultLasso, label = TRUE,
main = "Lasso coefficients in relation to degree of
penalization.")
featResultLasso <- topTable(resultLasso, n = 15)
}
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
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> library(a4Classif)
Loading required package: a4Core
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, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-5
Loading required package: a4Preproc
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following objects are masked from 'package:Matrix':
colMeans, colSums, expand, rowMeans, rowSums
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: MLInterfaces
Loading required package: annotate
Loading required package: XML
Loading required package: cluster
Loading required package: ROCR
Loading required package: gplots
Attaching package: 'gplots'
The following object is masked from 'package:IRanges':
space
The following object is masked from 'package:S4Vectors':
space
The following object is masked from 'package:stats':
lowess
Loading required package: pamr
Loading required package: survival
Loading required package: varSelRF
Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
Attaching package: 'randomForest'
The following object is masked from 'package:Biobase':
combine
The following object is masked from 'package:BiocGenerics':
combine
a4Classif version 1.20.0
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/a4Classif/lassoClass.Rd_%03d_medium.png", width=480, height=480)
> ### Name: lassoClass
> ### Title: Classify using the Lasso
> ### Aliases: lassoClass
> ### Keywords: models
>
> ### ** Examples
>
>
> if (require(ALL)){
+ data(ALL, package = "ALL")
+ ALL <- addGeneInfo(ALL)
+ ALL$BTtype <- as.factor(substr(ALL$BT,0,1))
+
+ resultLasso <- lassoClass(object = ALL, groups = "BTtype")
+ plot(resultLasso, label = TRUE,
+ main = "Lasso coefficients in relation to degree of
+ penalization.")
+ featResultLasso <- topTable(resultLasso, n = 15)
+ }
Loading required package: ALL
Loading required package: hgu95av2.db
Loading required package: org.Hs.eg.db
>
>
>
>
>
> dev.off()
null device
1
>