Collects and checks necessary parameters required for classifier training. The
empty constructor is provided for convenience.
Constructor
TrainParams()
Creates a default TrainParams object. The classifier function is DLDA. Users
should create an appropriate TrainParams object for the
characteristics of their data, once they are familiar with this software.
TrainParams(classifier, transposeExpression, doesTests, ...)
Creates a TrainParams object which stores the function which will do the
classifier building and parameters that the function will use.
classifier
A function which will construct a classifier, and also
possibly make the predictions. The first argument must be a matrix
object. The second argument must be a vector of classes. The third argument must be
verbose. If doesTests is TRUE, the third argument must be a matrix
of test data and the fourth argument is verbose. The function's return value can
be either a trained classifier when doesTests is FALSE or a
vector of class predictions if doesTests is TRUE.
transposeExpression
Set to TRUE if classifier expects
features as columns.
doesTests
Set to TRUE if classifier also performs
and returns predictions.
intermediate
Character vector. Names of any variables created in prior stages by
runTest that need to be passed to classifier.
...
Other named parameters which will be used by the classifier.
Author(s)
Dario Strbenac
Examples
if(require(sparsediscrim))
trainParams <- TrainParams(dlda, transposeExpression = TRUE, doesTests = FALSE)
# sparsediscrim has a separate predict method for trained DLDA objects.
# dlda expects features in columns, and samples in rows.
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.
Type 'q()' to quit R.
> library(ClassifyR)
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: BiocParallel
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/ClassifyR/TrainParams-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: TrainParams
> ### Title: Parameters for Classifier Training
> ### Aliases: TrainParams TrainParams-class TrainParams,ANY-method
> ### TrainParams,function-method
>
> ### ** Examples
>
> if(require(sparsediscrim))
+ trainParams <- TrainParams(dlda, transposeExpression = TRUE, doesTests = FALSE)
Loading required package: sparsediscrim
> # sparsediscrim has a separate predict method for trained DLDA objects.
> # dlda expects features in columns, and samples in rows.
>
>
>
>
>
> dev.off()
null device
1
>