# Visualize power assessment

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

This function plots the results of `evaluateSim` for assessing the setup performance, i.e. normalisation method performance.

    plotEvalSim(evalRes, Annot=TRUE)

## Arguments

evalRes | The output of `evaluateSim`.
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("SmartSeq2_Gene_Read_Counts")
    Batches = data.frame(Batch = sapply(strsplit(colnames(SmartSeq2_Gene_Read_Counts), "_"), "[[", 1),
                         stringsAsFactors = F,
                         row.names = colnames(SmartSeq2_Gene_Read_Counts))
    data("GeneLengths_mm10")
    estparam_gene <- estimateParam(countData = SmartSeq2_Gene_Read_Counts,
                                   readData = NULL,
                                   batchData = Batches,
                                   spikeData = NULL, spikeInfo = NULL,
                                   Lengths = GeneLengths_mm10, MeanFragLengths = NULL,
                                   RNAseq = 'singlecell', Protocol = 'Read',
                                   Distribution = 'ZINB', Normalisation = "scran",
                                   GeneFilter = 0.1, 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 = 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 = c(1,0.9,0.8), LibSize = 'given',
                      estParamRes = estparam_gene,
                      estSpikeRes = NULL,
                      DropGenes = FALSE,
                      sim.seed = 66437, 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
    evalsimres <- evaluateSim(simRes = simres)
    plotEvalSim(evalRes = evalsimres, Annot = TRUE)
    }