Supported by Dr. Osamu Ogasawara and  providing  . |
|
Last data update: 2014.03.03 |
perTurbo classificationDescriptionClassification using the PerTurbo algorithm. Usage
perTurboClassification(object, assessRes, scores = c("prediction", "all",
"none"), pRegul, sigma, inv, reg, fcol = "markers")
Arguments
ValueAn instance of class Author(s)Thomas Burger and Samuel Wieczorek ReferencesN. Courty, T. Burger, J. Laurent. "PerTurbo: a new classification algorithm based on the spectrum perturbations of the Laplace-Beltrami operator", The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), D. Gunopulos et al. (Eds.): ECML PKDD 2011, Part I, LNAI 6911, pp. 359 - 374, Athens, Greece, September 2011. Examples
library(pRolocdata)
data(dunkley2006)
## reducing parameter search space
params <- perTurboOptimisation(dunkley2006,
pRegul = 2^seq(-2,2,2),
sigma = 10^seq(-1, 1, 1),
inv = "Inversion Cholesky",
reg ="tikhonov",
times = 3)
params
plot(params)
f1Count(params)
levelPlot(params)
getParams(params)
res <- perTurboClassification(dunkley2006, params)
getPredictions(res, fcol = "perTurbo")
getPredictions(res, fcol = "perTurbo", t = 0.75)
plot2D(res, fcol = "perTurbo")
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(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/perTurboClassification.Rd_%03d_medium.png", width=480, height=480)
> ### Name: perTurboClassification
> ### Title: perTurbo classification
> ### Aliases: perTurboClassification
>
> ### ** Examples
>
> library(pRolocdata)
This is pRolocdata version 1.10.0.
Use 'pRolocdata()' to list available data sets.
> data(dunkley2006)
> ## reducing parameter search space
> params <- perTurboOptimisation(dunkley2006,
+ pRegul = 2^seq(-2,2,2),
+ sigma = 10^seq(-1, 1, 1),
+ inv = "Inversion Cholesky",
+ reg ="tikhonov",
+ times = 3)
| | | 0% | |===== | 7% | |========= | 13% | |============== | 20% | |=================== | 27% | |======================= | 33% | |============================ | 40% | |================================= | 47% | |===================================== | 53% | |========================================== | 60% | |=============================================== | 67% | |=================================================== | 73% | |======================================================== | 80% | |============================================================= | 87% | |================================================================= | 93% | |======================================================================| 100%
> params
Object of class "GenRegRes"
Algorithm: perTurbo
Hyper-parameters:
pRegul: 0.25 1 4
sigma: 0.1 1 10
other: Inversion Cholesky tikhonov
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.
0.6667 0.7222 0.7778 0.8148 0.8889 1.0000
best sigma: 0.1 1
best pRegul: 4 0.25
> plot(params)
> f1Count(params)
0.25 4
0.1 NA 0
1 1 NA
> levelPlot(params)
> getParams(params)
sigma pRegul
1.00 0.25
> res <- perTurboClassification(dunkley2006, params)
> getPredictions(res, fcol = "perTurbo")
ans
ER lumen ER membrane Golgi Mitochondrion PM
17 184 96 106 75
Plastid Ribosome TGN vacuole
26 51 26 108
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 ... perTurbo.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 perTurbo prediction (sigma=1 pRegul=0.25) Thu Jul 7 01:45:34 2016
Added perTurbo predictions according to global threshold = 0 Thu Jul 7 01:45:34 2016
MSnbase version: 1.17.12
> getPredictions(res, fcol = "perTurbo", t = 0.75)
ans
ER lumen ER membrane Golgi Mitochondrion PM
17 184 96 106 75
Plastid Ribosome TGN vacuole
26 51 26 108
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 ... perTurbo.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 perTurbo prediction (sigma=1 pRegul=0.25) Thu Jul 7 01:45:34 2016
Added perTurbo predictions according to global threshold = 0.75 Thu Jul 7 01:45:34 2016
MSnbase version: 1.17.12
> plot2D(res, fcol = "perTurbo")
>
>
>
>
>
> dev.off()
null device
1
>
|
Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and