hist.DHARMa | R Documentation |
The function produces a histogram from a DHARMa output
## S3 method for class 'DHARMa' hist(x, breaks = seq(-0.02, 1.02, len = 53), col = c("red", rep("lightgrey", 50), "red"), main = "Hist of DHARMa residuals", xlab = "Residuals (outliers are marked red)", cex.main = 1, ...)
x |
a DHARMa simulation output (class DHARMa) |
breaks |
breaks for hist() function |
col |
col for hist bars |
main |
plot main |
xlab |
plot xlab |
cex.main |
plot cex.main |
... |
other arguments to be passed on to hist |
plotSimulatedResiduals
, plotResiduals
testData = createData(sampleSize = 200, family = poisson(), randomEffectVariance = 1, numGroups = 10) fittedModel <- glm(observedResponse ~ Environment1, family = "poisson", data = testData) simulationOutput <- simulateResiduals(fittedModel = fittedModel) ######### main plotting function ############# # for all functions, quantreg = T will be more # informative, but slower plot(simulationOutput, quantreg = FALSE) ############# Distribution ###################### plotQQunif(simulationOutput = simulationOutput, testDispersion = FALSE, testUniformity = FALSE, testOutliers = FALSE) hist(simulationOutput ) ############# residual plots ############### # rank transformation, using a simulationOutput plotResiduals(simulationOutput, rank = TRUE, quantreg = FALSE) # smooth scatter plot - usually used for large datasets, default for n > 10000 plotResiduals(simulationOutput, rank = TRUE, quantreg = FALSE, smoothScatter = TRUE) # residual vs predictors, using explicit values for pred, residual plotResiduals(simulationOutput, form = testData$Environment1, quantreg = FALSE) # if pred is a factor, or if asFactor = T, will produce a boxplot plotResiduals(simulationOutput, form = testData$group) # All these options can also be provided to the main plotting function # If you want to plot summaries per group, use simulationOutput = recalculateResiduals(simulationOutput, group = testData$group) plot(simulationOutput, quantreg = FALSE) # we see one residual point per RE
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