Classification using the naive Bayes algorithm.

nbClassification(
  object,
  assessRes,
  scores = c("prediction", "all", "none"),
  laplace,
  fcol = "markers",
  ...
)

Arguments

object

An instance of class "MSnSet".

assessRes

An instance of class "GenRegRes", as generated by nbOptimisation.

scores

One of "prediction", "all" or "none" to report the score for the predicted class only, for all classes or none.

laplace

If assessRes is missing, a laplace must be provided.

fcol

The feature meta-data containing marker definitions. Default is markers.

...

Additional parameters passed to naiveBayes from package e1071.

Value

An instance of class "MSnSet" with

nb and nb.scores feature variables storing the classification results and scores respectively.

Author

Laurent Gatto

Examples

library(pRolocdata)
data(dunkley2006)
## reducing parameter search space and iterations 
params <- nbOptimisation(dunkley2006, laplace = c(0, 5),  times = 3)
#> 
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params
#> Object of class "GenRegRes"
#> Algorithm: naiveBayes 
#> Hyper-parameters:
#>  laplace: 0 5
#> 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. 
#>  0.9377  0.9516  0.9654  0.9677  0.9827  1.0000 
#>  best laplace: 0 5   
plot(params)

f1Count(params)
#> 
#> 5 
#> 1 
levelPlot(params)

getParams(params)
#> laplace 
#>       5 
res <- nbClassification(dunkley2006, params)
#> [1] "markers"
getPredictions(res, fcol = "naiveBayes")
#> ans
#>      ER lumen   ER membrane         Golgi Mitochondrion            PM 
#>            18           175            98            98           123 
#>       Plastid      Ribosome           TGN       vacuole 
#>            52            70            24            31 
#> 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 ... naiveBayes.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 naiveBayes prediction (laplace=5) Tue Mar 12 07:14:00 2024 
#> Added naiveBayes predictions according to global threshold = 0 Tue Mar 12 07:14:00 2024 
#>  MSnbase version: 1.17.12 
getPredictions(res, fcol = "naiveBayes", t = 1)
#> ans
#>      ER lumen   ER membrane         Golgi Mitochondrion            PM 
#>            14            45            69            61            46 
#>       Plastid      Ribosome           TGN       unknown       vacuole 
#>            43            35            13           336            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 ... naiveBayes.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 naiveBayes prediction (laplace=5) Tue Mar 12 07:14:00 2024 
#> Added naiveBayes predictions according to global threshold = 1 Tue Mar 12 07:14:00 2024 
#>  MSnbase version: 1.17.12 
plot2D(res, fcol = "naiveBayes")