logReg {a4Base} | R Documentation |
Logistic regression for predicting the probability to belong to a certain class in binary classification problems.
logReg( object, groups, probesetId = NULL, geneSymbol = NULL, main = NULL, probe2gene = TRUE, ... )
object |
ExpressionSet object for the experiment |
groups |
String containing the name of the grouping variable. This should be a
the name of a column in the |
probesetId |
The probeset ID. These should be stored in the |
geneSymbol |
The gene symbol. These should be stored in the column |
main |
Main title on top of the gra |
probe2gene |
Boolean indicating whether the probeset should be translated to a gene symbol (used for the default title of the plot) |
... |
Possibility to add extra plot options. See |
It will always estimate probability scores to belong to the second level of the factor variable. If a probability score to other level is preferred, then you need to change the order of the levels of the factor.
A data.frame object with three columns and rownames
rownames |
The 'sampleNames' of the expressionSet object |
x |
The expression values for the specified gene for all samples |
y |
The labels of the samples |
fit |
The fitted probability score to belong to one of the two classes. |
Willem Talloen
## Not run: if (require(ALL)){ data(ALL, package = "ALL") ALL <- addGeneInfo(ALL) ALL$BTtype <- as.factor(substr(ALL$BT,0,1)) logRegRes <- logReg(geneSymbol = "HLA-DPB1", object = ALL, groups = "BTtype") # scoresplot probabilitiesPlot(proportions = logRegRes$fit, classVar = logRegRes$y, sampleNames = rownames(logRegRes), main = 'Probability of being a T-cell type ALL') # barplot probabilitiesPlot(proportions = logRegRes$fit, classVar = logRegRes$y, barPlot=TRUE, sampleNames = rownames(logRegRes), main = 'Probability of being a T-cell type ALL') } ## End(Not run)