compareModeltoTruth: Compare result of binary classification to truth

In nbenn/singleCellFeatures: Analyze single cell features stored in an openBIS instance

Description

Given two vectors of factors (two identical levels) of equal length, calculate confusion matrix marginals

Usage

compareModeltoTruth(estim, truth)

Arguments

estim	Vector holding a two level factor with the classification result.
truth	Vector holding a two level factor with the truth.

Value

A list with various key charactereistics of the resulting confusion matrix

Examples

# get gene locations
mtor.loc <- findWells(experiments="brucella-du-k1", contents="MTOR")
scra.loc <- findWells(plates=sapply(mtor.loc, getBarcode),
                      contents="SCRAMBLED", well.names="G23")
# combine for faster fetching
data     <- getSingleCellData(list(mtor.loc[[1]], scra.loc[[1]]))
mtor.dat <- meltData(cleanData(data[[1]]$H6))
scra.dat <- meltData(cleanData(data[[1]]$G23))
# prepare data for glm
data <- prepareDataforGlm(mtor.dat$mat$Cells, scra.dat$mat$Cells)
data <- makeRankFull(data)
# run glm
model <- glm("Response ~ .", binomial, data$train)
# compare to testing data
predi <- as.factor(round(predict(model, newdata=data$test,
                                 type="response")))
levels(predi) <- c("active", "control")
comparison <- compareModeltoTruth(predi, data$test$Response)

nbenn/singleCellFeatures documentation built on May 23, 2019, 12:24 p.m.