plot.DHARMa {DHARMa} | R Documentation |

This S3 function creates standard plots for the simulated residuals contained in an object of class DHARMa, using `plotQQunif`

(left panel) and `plotResiduals`

(right panel)

## S3 method for class 'DHARMa' plot(x, ...)

`x` |
an object of class DHARMa with simulated residuals created by |

`...` |
further options for |

The function creates a plot with two panels. The left panel is a uniform qq plot (calling `plotQQunif`

), and the right panel shows residuals against predicted values (calling `plotResiduals`

), with outliers highlighted in red.

Very briefly, we would expect that a correctly specified model shows:

a) a straight 1-1 line, as well as n.s. of the displayed tests in the qq-plot (left) -> evidence for an the correct overall residual distribution (for more details on the interpretation of this plot, see `plotQQunif`

)

b) visual homogeneity of residuals in both vertical and horizontal direction, as well as n.s. of quantile tests in the res ~ predictor plot (for more details on the interpretation of this plot, see `plotResiduals`

)

Deviations from these expectations can be interpreted similar to a linear regression. See the vignette for detailed examples.

Note that, unlike `plotResiduals`

, plot.DHARMa command uses the default rank = T.

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

[Package *DHARMa* version 0.4.3 Index]