Classification using the random forest algorithm.
Usage
rfClassification(
object,
assessRes,
scores = c("prediction", "all", "none"),
mtry,
fcol = "markers",
...
)Arguments
- object
An instance of class
"MSnSet".- assessRes
An instance of class
"GenRegRes", as generated byrfOptimisation.- scores
One of
"prediction","all"or"none"to report the score for the predicted class only, for all classes or none.- mtry
If
assessResis missing, amtrymust be provided.- fcol
The feature meta-data containing marker definitions. Default is
markers.- ...
Additional parameters passed to
randomForestfrom packagerandomForest.
Value
An instance of class "MSnSet" with
rf and rf.scores feature variables storing the
classification results and scores respectively.
Examples
library(pRolocdata)
data(dunkley2006)
## reducing parameter search space and iterations
params <- rfOptimisation(dunkley2006, mtry = c(2, 5, 10), times = 3)
#>
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params
#> Object of class "GenRegRes"
#> Algorithm: randomForest
#> Hyper-parameters:
#> mtry: 2 5 10
#> 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.
#> 0.9639 0.9690 0.9740 0.9724 0.9767 0.9793
#> best mtry: 2 5
plot(params)
f1Count(params)
#>
#> 2
#> 1
levelPlot(params)
getParams(params)
#> mtry
#> 2
res <- rfClassification(dunkley2006, params)
#> [1] "markers"
getPredictions(res, fcol = "rf")
#> ans
#> ER lumen ER membrane Golgi Mitochondrion PM
#> 19 179 95 104 134
#> Plastid Ribosome TGN vacuole
#> 51 53 21 33
#> 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 ... rf.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 random forest prediction (mtry=2) Fri Oct 18 17:20:59 2024
#> Added rf predictions according to global threshold = 0 Fri Oct 18 17:20:59 2024
#> MSnbase version: 1.17.12
getPredictions(res, fcol = "rf", t = 0.75)
#> ans
#> ER lumen ER membrane Golgi Mitochondrion PM
#> 14 139 77 89 94
#> Plastid Ribosome TGN unknown vacuole
#> 45 20 13 171 27
#> 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 ... rf.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 random forest prediction (mtry=2) Fri Oct 18 17:20:59 2024
#> Added rf predictions according to global threshold = 0.75 Fri Oct 18 17:20:59 2024
#> MSnbase version: 1.17.12
plot2D(res, fcol = "rf")
