# Visualize error rate curves and associated summary statistics

Source: `R/Plot.R`
`plotEvalROC.Rd`

This function plots the results of `evaluateROC` for assessing relative operating characteristic curves and summary statistics.

    plotEvalROC(evalRes,
    cutoff=c('liberal', 'conservative'),
    Annot=TRUE)

## Arguments

evalRes | The output of `evaluateROC`.
cutoff | Character vector defining whether the `"liberal"` or `"conservative"` FDR control is considered.
Annot | A logical vector. If `TRUE`, a short figure legend under the plot is included.

## Value

A ggplot object.

## Examples

    if (FALSE) {
    # estimate gene parameters
    data("CELseq2_Gene_UMI_Counts")
    estparam_gene <- estimateParam(countData = CELseq2_Gene_UMI_Counts,
                                   readData = NULL,
                                   batchData = NULL,
                                   spikeData = NULL, spikeInfo = NULL,
                                   Lengths = NULL, MeanFragLengths = NULL,
                                   RNAseq = 'singlecell', Protocol = 'UMI',
                                   Distribution = 'NB', Normalisation = "scran",
                                   GeneFilter = 0.1, SampleFilter = 3,
                                   sigma = 1.96, NCores = NULL, verbose = TRUE)
    # define log2 fold change
    p.lfc <- function(x) sample(c(-1,1), size=x,replace=T)*rgamma(x, shape = 1, rate = 2)
    # set up simulations
    setupres <- Setup(ngenes = 10000, nsims = 10,
                      p.DE = 0.1, pLFC = p.lfc,
                      n1 = c(20,50,100), n2 = c(30,60,120),
                      Thinning = NULL, LibSize = 'equal',
                      estParamRes = estparam_gene,
                      estSpikeRes = NULL,
                      DropGenes = FALSE,
                      sim.seed = 34269, verbose = TRUE)
    # run simulation
    simres <- simulateDE(SetupRes = setupres,
                         Prefilter = "FreqFilter", Imputation = NULL,
                         Normalisation = 'scran', Label = 'none',
                         DEmethod = "limma-trend", DEFilter = FALSE,
                         NCores = NULL, verbose = TRUE)
    # evaluation
    evalrocres <- evaluateROC(simRes = simres,
                              alpha.type = "adjusted",
                              MTC = 'BH', alpha.nominal = 0.05,
                              raw = FALSE)
    # plot evaluation
    plotEvalROC(evalRes = evalrocres, cutoff = "conservative", Annot = TRUE)
    }