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Last data update: 2014.03.03 |
ksvm classificationDescriptionClassification using the support vector machine algorithm. Usage
ksvmClassification(object, assessRes, scores = c("prediction", "all", "none"),
cost, fcol = "markers", ...)
Arguments
ValueAn instance of class Author(s)Laurent Gatto Exampleslibrary(pRolocdata) data(dunkley2006) ## reducing parameter search space and iterations params <- ksvmOptimisation(dunkley2006, cost = 2^seq(-1,4,5), times = 3) params plot(params) f1Count(params) levelPlot(params) getParams(params) res <- ksvmClassification(dunkley2006, params) getPredictions(res, fcol = "ksvm") getPredictions(res, fcol = "ksvm", t = 0.75) plot2D(res, fcol = "ksvm") Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(pRoloc)
Loading required package: MSnbase
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
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: mzR
Loading required package: Rcpp
Loading required package: BiocParallel
Loading required package: ProtGenerics
This is MSnbase version 1.20.7
Read '?MSnbase' and references therein for information
about the package and how to get started.
Attaching package: 'MSnbase'
The following object is masked from 'package:stats':
smooth
The following object is masked from 'package:base':
trimws
Loading required package: MLInterfaces
Loading required package: annotate
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:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
Loading required package: XML
Loading required package: cluster
This is pRoloc version 1.12.4
Read '?pRoloc' and references therein for information
about the package and how to get started.
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/pRoloc/ksvmClassification.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ksvmClassification
> ### Title: ksvm classification
> ### Aliases: ksvmClassification ksvmPrediction
>
> ### ** Examples
>
> library(pRolocdata)
This is pRolocdata version 1.10.0.
Use 'pRolocdata()' to list available data sets.
> data(dunkley2006)
> ## reducing parameter search space and iterations
> params <- ksvmOptimisation(dunkley2006, cost = 2^seq(-1,4,5), times = 3)
| | | 0% | |===== | 7% | |========= | 13% | |============== | 20% | |=================== | 27% | |======================= | 33% | |============================ | 40% | |================================= | 47% | |===================================== | 53% | |========================================== | 60% | |=============================================== | 67% | |=================================================== | 73% | |======================================================== | 80% | |============================================================= | 87% | |================================================================= | 93% | |======================================================================| 100%
> params
Object of class "GenRegRes"
Algorithm: ksvm
Hyper-parameters:
cost: 0.5 16
Design:
Replication: 3 x 5-fold X-validation
Partitioning: 0.2/0.8 (test/train)
Results
macro F1:
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.9500 0.9646 0.9791 0.9764 0.9896 1.0000
best cost: 0.5 16
> plot(params)
> f1Count(params)
16
1
> levelPlot(params)
> getParams(params)
cost
16
> res <- ksvmClassification(dunkley2006, params)
> getPredictions(res, fcol = "ksvm")
ans
ER lumen ER membrane Golgi Mitochondrion PM
18 191 120 140 53
Plastid Ribosome TGN vacuole
54 19 73 21
MSnSet (storageMode: lockedEnvironment)
assayData: 689 features, 16 samples
element names: exprs
protocolData: none
phenoData
sampleNames: M1F1A M1F4A ... M2F11B (16 total)
varLabels: membrane.prep fraction replicate
varMetadata: labelDescription
featureData
featureNames: AT1G09210 AT1G21750 ... AT4G39080 (689 total)
fvarLabels: assigned evidence ... ksvm.pred (11 total)
fvarMetadata: labelDescription
experimentData: use 'experimentData(object)'
pubMedIds: 16618929
Annotation:
- - - Processing information - - -
Loaded on Thu Jul 16 22:53:08 2015.
Normalised to sum of intensities.
Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015
Performed ksvm prediction (cost=16) Thu Jul 7 01:43:52 2016
Added ksvm predictions according to global threshold = 0 Thu Jul 7 01:43:52 2016
MSnbase version: 1.17.12
> getPredictions(res, fcol = "ksvm", t = 0.75)
ans
ER lumen ER membrane Golgi Mitochondrion PM
14 157 67 55 46
Plastid Ribosome TGN unknown vacuole
20 19 13 277 21
MSnSet (storageMode: lockedEnvironment)
assayData: 689 features, 16 samples
element names: exprs
protocolData: none
phenoData
sampleNames: M1F1A M1F4A ... M2F11B (16 total)
varLabels: membrane.prep fraction replicate
varMetadata: labelDescription
featureData
featureNames: AT1G09210 AT1G21750 ... AT4G39080 (689 total)
fvarLabels: assigned evidence ... ksvm.pred (11 total)
fvarMetadata: labelDescription
experimentData: use 'experimentData(object)'
pubMedIds: 16618929
Annotation:
- - - Processing information - - -
Loaded on Thu Jul 16 22:53:08 2015.
Normalised to sum of intensities.
Added markers from 'mrk' marker vector. Thu Jul 16 22:53:08 2015
Performed ksvm prediction (cost=16) Thu Jul 7 01:43:52 2016
Added ksvm predictions according to global threshold = 0.75 Thu Jul 7 01:43:52 2016
MSnbase version: 1.17.12
> plot2D(res, fcol = "ksvm")
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> dev.off()
null device
1
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