# Visualize power assessment

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

This function plots the results of `evaluateDE` for assessing the error rates and sample size requirements.

    plotEvalDE(evalRes, rate=c('marginal', 'conditional'),
                       quick=TRUE, Annot=TRUE)

## Arguments

evalRes | The output of `evaluateDE`.
rate | Character vector defining whether the `"marginal"` or `"conditional"` rates should be plotted. Conditional depends on the choice of stratify.by in `evaluateDE`.
quick | A logical vector. If `TRUE`, the TPR and FDR are only plotted. If `FALSE`, then all rates are plotted.
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("Bulk_Read_Counts")
    data("GeneLengths_hg19")
    estparam_gene <- estimateParam(countData = Bulk_Read_Counts,
                                   readData = NULL,
                                   batchData = NULL,
                                   spikeData = NULL, spikeInfo = NULL,
                                   Lengths = GeneLengths_hg19, MeanFragLengths = NULL,
                                   RNAseq = 'bulk', Protocol = 'Read',
                                   Distribution = 'NB', Normalisation = "MR",
                                   GeneFilter = 0.25, SampleFilter = 3,
                                   sigma = 1.96, NCores = NULL, verbose = TRUE)
    # define log fold change
    p.lfc <- function(x) sample(c(-1,1), size=x,replace=T)*rgamma(x, shape = 2, rate = 2)
    # set up simulations
    setupres <- Setup(ngenes = 10000, nsims = 10,
                      p.DE = 0.1, pLFC = p.lfc,
                      n1 = c(3,6,12), n2 = c(3,6,12),
                      Thinning = c(1,0.9,0.8), LibSize = 'given',
                      estParamRes = estparam_gene,
                      estSpikeRes = NULL,
                      DropGenes = FALSE,
                      sim.seed = 4379, verbose = TRUE)
    # run simulation
    simres <- simulateDE(SetupRes = setupres,
                         Prefilter = NULL, Imputation = NULL,
                         Normalisation = 'MR', Label = 'none',
                         DEmethod = "limma-trend", DEFilter = FALSE,
                         NCores = NULL, verbose = TRUE)
    # DE evaluation
    evalderes <- evaluateDE(simRes = simres, alpha.type="adjusted",
                            MTC='BH', alpha.nominal=0.05,
                            stratify.by = "mean", filter.by = "none")
    plotEvalDE(evalderes, rate = "marginal", quick = FALSE, Annot = TRUE)
    plotEvalDE(evalderes, rate = "conditional", quick = FALSE, Annot = TRUE)
    }