Plot assignment probabilities

Description

Plots the assignment probabilities of a previous query.

Usage

plotAssignments(queryResult, realLabels, 
  minProbAssignCoeff = 1, minDiffAssignCoeff = 0.8, 
  totalNumberOfClasses = NULL, pointSize=0.8, identify = FALSE)

Arguments

Value

Plot.

See Also

Main package function and classifier training: geNetClassifier
Querying the classifier: queryGeNetClassifier

Examples

##########################
## Classifier training
##########################

# Load an expressionSet:
library(leukemiasEset)
data(leukemiasEset)

# Select the train samples: 
trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) 
# summary(leukemiasEset$LeukemiaType[trainSamples])

# Train a classifier or load a trained one:
# leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], 
#    sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") 
data(leukemiasClassifier) # Sample trained classifier

##########################
## External Validation:
##########################
# Select the samples to query the classifier 
#   - External validation: samples not used for training
testSamples <- c(1:60)[-trainSamples]         

# Make a query to the classifier:
queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,testSamples])

##########################
## Plot:
##########################
plotAssignments(queryResult, realLabels=leukemiasEset[,testSamples]$LeukemiaType)

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)

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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
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> library(geNetClassifier)
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: EBarrays
Loading required package: lattice
Loading required package: minet
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/geNetClassifier/plotAssignments.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotAssignments
> ### Title: Plot assignment probabilities
> ### Aliases: plotAssignments
> ### Keywords: classif
> 
> ### ** Examples
> 
> ##########################
> ## Classifier training
> ##########################
> 
> # Load an expressionSet:
> library(leukemiasEset)
> data(leukemiasEset)
> 
> # Select the train samples: 
> trainSamples<- c(1:10, 13:22, 25:34, 37:46, 49:58) 
> # summary(leukemiasEset$LeukemiaType[trainSamples])
> 
> # Train a classifier or load a trained one:
> # leukemiasClassifier <- geNetClassifier(leukemiasEset[,trainSamples], 
> #    sampleLabels="LeukemiaType", plotsName="leukemiasClassifier") 
> data(leukemiasClassifier) # Sample trained classifier
> 
> ##########################
> ## External Validation:
> ##########################
> # Select the samples to query the classifier 
> #   - External validation: samples not used for training
> testSamples <- c(1:60)[-trainSamples]         
> 
> # Make a query to the classifier:
> queryResult <- queryGeNetClassifier(leukemiasClassifier, leukemiasEset[,testSamples])
Coefficients for assignment: Minimum Probability to be assigned = 0.4(default).
 Minimum difference between the probabilities of first and second most likely classes  = 0.16
> 
> ##########################
> ## Plot:
> ##########################
> plotAssignments(queryResult, realLabels=leukemiasEset[,testSamples]$LeukemiaType)
Warning in plotAssignments(queryResult, realLabels = leukemiasEset[, testSamples]$LeukemiaType) :
  The real sample's labels  vector is not named, it will be assumed the labels are in order: the first label applies to the first predicted sample... 
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>