tests/testthat/_snaps/testTests.md

In DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models

tests work

Code
  testUniformity(simulationOutput, plot = FALSE)
Output

    Asymptotic one-sample Kolmogorov-Smirnov test

  data:  simulationOutput$scaledResiduals
  D = 0.035577, p-value = 0.9619
  alternative hypothesis: two-sided

Code
  testUniformity(simulationOutput, plot = FALSE, alternative = "less")
Output

    Asymptotic one-sample Kolmogorov-Smirnov test

  data:  simulationOutput$scaledResiduals
  D^- = 0.035577, p-value = 0.6027
  alternative hypothesis: the CDF of x lies below the null hypothesis

Code
  testUniformity(simulationOutput, plot = FALSE, alternative = "greater")
Output

    Asymptotic one-sample Kolmogorov-Smirnov test

  data:  simulationOutput$scaledResiduals
  D^+ = 0.034418, p-value = 0.6226
  alternative hypothesis: the CDF of x lies above the null hypothesis

Code
  testDispersion(simulationOutput, plot = FALSE)
Output

    DHARMa nonparametric dispersion test via sd of residuals fitted vs.
    simulated

  data:  simulationOutput
  dispersion = 1.2578, p-value = 0.096
  alternative hypothesis: two.sided

Code
  testDispersion(simulationOutput, plot = FALSE, alternative = "less")
Output

    DHARMa nonparametric dispersion test via sd of residuals fitted vs.
    simulated

  data:  simulationOutput
  dispersion = 1.2578, p-value = 0.952
  alternative hypothesis: less

Code
  testDispersion(simulationOutput, plot = FALSE, alternative = "greater")
Output

    DHARMa nonparametric dispersion test via sd of residuals fitted vs.
    simulated

  data:  simulationOutput
  dispersion = 1.2578, p-value = 0.048
  alternative hypothesis: greater

Code
  testDispersion(simulationOutput, plot = FALSE, alternative = "two.sided")
Output

    DHARMa nonparametric dispersion test via sd of residuals fitted vs.
    simulated

  data:  simulationOutput
  dispersion = 1.2578, p-value = 0.096
  alternative hypothesis: two.sided

Code
  testResiduals(simulationOutput, plot = FALSE)
Output
  $uniformity

    Asymptotic one-sample Kolmogorov-Smirnov test

  data:  simulationOutput$scaledResiduals
  D = 0.035577, p-value = 0.9619
  alternative hypothesis: two-sided


  $dispersion

    DHARMa nonparametric dispersion test via sd of residuals fitted vs.
    simulated

  data:  simulationOutput
  dispersion = 1.2578, p-value = 0.096
  alternative hypothesis: two.sided


  $outliers

    DHARMa bootstrapped outlier test

  data:  simulationOutput
  outliers at both margin(s) = 1, observations = 200, p-value = 0.82
  alternative hypothesis: two.sided
   percent confidence interval:
   0.000000 0.012625
  sample estimates:
  outlier frequency (expected: 0.0028 ) 
                                  0.005

Code
  testZeroInflation(simulationOutput, plot = FALSE)
Output

    DHARMa zero-inflation test via comparison to expected zeros with
    simulation under H0 = fitted model

  data:  simulationOutput
  ratioObsSim = 1.0565, p-value = 0.552
  alternative hypothesis: two.sided

Code
  testZeroInflation(simulationOutput, plot = FALSE, alternative = "less")
Output

    DHARMa zero-inflation test via comparison to expected zeros with
    simulation under H0 = fitted model

  data:  simulationOutput
  ratioObsSim = 1.0565, p-value = 0.784
  alternative hypothesis: less

Code
  testGeneric(simulationOutput, summary = countOnes, plot = FALSE)
Output

    DHARMa generic simulation test

  data:  simulationOutput
  ratioObsSim = 0.97457, p-value = 0.808
  alternative hypothesis: two.sided

Code
  testGeneric(simulationOutput, summary = countOnes, plot = FALSE, alternative = "less")
Output

    DHARMa generic simulation test

  data:  simulationOutput
  ratioObsSim = 0.97457, p-value = 0.404
  alternative hypothesis: less

Code
  testGeneric(simulationOutput, summary = means, plot = FALSE)
Output

    DHARMa generic simulation test

  data:  simulationOutput
  ratioObsSim = 1.0077, p-value = 0.944
  alternative hypothesis: two.sided

Code
  testGeneric(simulationOutput, summary = spread, plot = FALSE)
Output

    DHARMa generic simulation test

  data:  simulationOutput
  ratioObsSim = 1.0834, p-value = 0.208
  alternative hypothesis: two.sided

Code
  testUniformity(simulationOutput, plot = FALSE)
Output

    Exact one-sample Kolmogorov-Smirnov test

  data:  simulationOutput$scaledResiduals
  D = 0.22547, p-value = 0.613
  alternative hypothesis: two-sided

Code
  testUniformity(simulationOutput, plot = FALSE, alternative = "less")
Output

    Exact one-sample Kolmogorov-Smirnov test

  data:  simulationOutput$scaledResiduals
  D^- = 0.16279, p-value = 0.5338
  alternative hypothesis: the CDF of x lies below the null hypothesis

Code
  testUniformity(simulationOutput, plot = FALSE, alternative = "greater")
Output

    Exact one-sample Kolmogorov-Smirnov test

  data:  simulationOutput$scaledResiduals
  D^+ = 0.22547, p-value = 0.315
  alternative hypothesis: the CDF of x lies above the null hypothesis

Code
  testDispersion(simulationOutput, plot = FALSE)
Output

    DHARMa nonparametric dispersion test via sd of residuals fitted vs.
    simulated

  data:  simulationOutput
  dispersion = 2.1091, p-value = 0.048
  alternative hypothesis: two.sided

Code
  testDispersion(simulationOutput, plot = FALSE, alternative = "less")
Output

    DHARMa nonparametric dispersion test via sd of residuals fitted vs.
    simulated

  data:  simulationOutput
  dispersion = 2.1091, p-value = 0.976
  alternative hypothesis: less

Code
  testDispersion(simulationOutput, plot = FALSE, alternative = "greater")
Output

    DHARMa nonparametric dispersion test via sd of residuals fitted vs.
    simulated

  data:  simulationOutput
  dispersion = 2.1091, p-value = 0.024
  alternative hypothesis: greater

Code
  testDispersion(simulationOutput, plot = FALSE, alternative = "two.sided")
Output

    DHARMa nonparametric dispersion test via sd of residuals fitted vs.
    simulated

  data:  simulationOutput
  dispersion = 2.1091, p-value = 0.048
  alternative hypothesis: two.sided

Code
  testResiduals(simulationOutput, plot = FALSE)
Output
  $uniformity

    Exact one-sample Kolmogorov-Smirnov test

  data:  simulationOutput$scaledResiduals
  D = 0.22547, p-value = 0.613
  alternative hypothesis: two-sided


  $dispersion

    DHARMa nonparametric dispersion test via sd of residuals fitted vs.
    simulated

  data:  simulationOutput
  dispersion = 2.1091, p-value = 0.048
  alternative hypothesis: two.sided


  $outliers

    DHARMa bootstrapped outlier test

  data:  simulationOutput
  outliers at both margin(s) = 20, observations = 200, p-value = 0.1
  alternative hypothesis: two.sided
   percent confidence interval:
   0.0 0.1
  sample estimates:
  outlier frequency (expected: 0.005 ) 
                                   0.1

Code
  testZeroInflation(simulationOutput, plot = FALSE)
Output

    DHARMa zero-inflation test via comparison to expected zeros with
    simulation under H0 = fitted model

  data:  simulationOutput
  ratioObsSim = NaN, p-value = 1
  alternative hypothesis: two.sided

Code
  testZeroInflation(simulationOutput, plot = FALSE, alternative = "less")
Output

    DHARMa zero-inflation test via comparison to expected zeros with
    simulation under H0 = fitted model

  data:  simulationOutput
  ratioObsSim = NaN, p-value = 1
  alternative hypothesis: less

Code
  testGeneric(simulationOutput, summary = countOnes, plot = FALSE)
Output

    DHARMa generic simulation test

  data:  simulationOutput
  ratioObsSim = NaN, p-value = 1
  alternative hypothesis: two.sided

Code
  testGeneric(simulationOutput, summary = countOnes, plot = FALSE, alternative = "less")
Output

    DHARMa generic simulation test

  data:  simulationOutput
  ratioObsSim = NaN, p-value = 1
  alternative hypothesis: less

Code
  testGeneric(simulationOutput, summary = means, plot = FALSE)
Output

    DHARMa generic simulation test

  data:  simulationOutput
  ratioObsSim = 1.0077, p-value = 0.944
  alternative hypothesis: two.sided

Code
  testGeneric(simulationOutput, summary = spread, plot = FALSE)
Output

    DHARMa generic simulation test

  data:  simulationOutput
  ratioObsSim = 1.4725, p-value = 0.04
  alternative hypothesis: two.sided

Code
  testDispersion(simulationOutput, plot = FALSE)
Output

    DHARMa nonparametric dispersion test via mean deviance residual fitted
    vs. simulated-refitted

  data:  simulationOutput
  dispersion = 1.1231, p-value = 0.248
  alternative hypothesis: two.sided

correlation tests work

Code
  testSpatialAutocorrelation(simulationOutput, x = testData$x, y = testData$y,
  plot = FALSE)
Output

    DHARMa Moran's I test for distance-based autocorrelation

  data:  simulationOutput
  observed = -0.0166319, expected = -0.0050251, sd = 0.0112750, p-value =
  0.3033
  alternative hypothesis: Distance-based autocorrelation

Code
  testSpatialAutocorrelation(simulationOutput, x = testData$x, y = testData$y,
  plot = FALSE, alternative = "two.sided")
Output

    DHARMa Moran's I test for distance-based autocorrelation

  data:  simulationOutput
  observed = -0.0166319, expected = -0.0050251, sd = 0.0112750, p-value =
  0.3033
  alternative hypothesis: Distance-based autocorrelation

Code
  testSpatialAutocorrelation(simulationOutput, distMat = dM, plot = FALSE)
Output

    DHARMa Moran's I test for distance-based autocorrelation

  data:  simulationOutput
  observed = -0.0166319, expected = -0.0050251, sd = 0.0112750, p-value =
  0.3033
  alternative hypothesis: Distance-based autocorrelation

Code
  testSpatialAutocorrelation(simulationOutput, distMat = dM, plot = FALSE,
    alternative = "two.sided")
Output

    DHARMa Moran's I test for distance-based autocorrelation

  data:  simulationOutput
  observed = -0.0166319, expected = -0.0050251, sd = 0.0112750, p-value =
  0.3033
  alternative hypothesis: Distance-based autocorrelation

Code
  testSpatialAutocorrelation(simulationOutput, plot = FALSE, x = testData$x[1:10],
  y = testData$y[1:9])
Condition
  Warning in `cbind()`:
  number of rows of result is not a multiple of vector length (arg 2)
  Error in `testSpatialAutocorrelation()`:
  ! Dimensions of x / y coordinates don't match the dimension of the residuals.

Code
  testSpatialAutocorrelation(simulationOutput[1:10], plot = FALSE, x = testData$x[
    1:10], y = testData$y[1:10])
Condition
  Error in `ensureDHARMa()`:
  ! wrong argument to function, simulationOutput must be a DHARMa object or a numeric vector of quantile residuals!

Code
  testSpatialAutocorrelation(simulationOutput, distMat = dM, plot = FALSE, x = testData$
    x)
Message
  Both coordinates and distMat provided, calculations will be done based on the distance matrix, coordinates will only be used for plotting.
Output

    DHARMa Moran's I test for distance-based autocorrelation

  data:  simulationOutput
  observed = -0.0166319, expected = -0.0050251, sd = 0.0112750, p-value =
  0.3033
  alternative hypothesis: Distance-based autocorrelation

Code
  testSpatialAutocorrelation(simulationOutput, distMat = dM, plot = FALSE, y = testData$
    y)
Message
  Both coordinates and distMat provided, calculations will be done based on the distance matrix, coordinates will only be used for plotting.
Output

    DHARMa Moran's I test for distance-based autocorrelation

  data:  simulationOutput
  observed = -0.0166319, expected = -0.0050251, sd = 0.0112750, p-value =
  0.3033
  alternative hypothesis: Distance-based autocorrelation

Code
  testTemporalAutocorrelation(simulationOutput, plot = FALSE, time = testData$
  time)
Output

    Durbin-Watson test

  data:  simulationOutput$scaledResiduals ~ 1
  DW = 1.9703, p-value = 0.833
  alternative hypothesis: true autocorrelation is not 0

Code
  testTemporalAutocorrelation(simulationOutput, plot = FALSE, time = testData$
  time, alternative = "greater")
Output

    Durbin-Watson test

  data:  simulationOutput$scaledResiduals ~ 1
  DW = 1.9703, p-value = 0.4165
  alternative hypothesis: true autocorrelation is greater than 0

test phylogenetic autocorrelation

Code
  restest
Output

    DHARMa Moran's I test for phylogenetic autocorrelation

  data:  res
  observed = 0.851667, expected = -0.016949, sd = 0.088733, p-value <
  2.2e-16
  alternative hypothesis: Phylogenetic autocorrelation

Code
  restest2
Output

    DHARMa Moran's I test for phylogenetic autocorrelation

  data:  res2
  observed = 0.047474, expected = -0.016949, sd = 0.088260, p-value =
  0.4654
  alternative hypothesis: Phylogenetic autocorrelation

testOutliers

Code
  testOutliers(simulationOutput, plot = F, margin = "lower")
Output

    DHARMa outlier test based on exact binomial test with approximate
    expectations

  data:  simulationOutput
  outliers at lower margin(s) = 4, observations = 1000, p-value = 0.8037
  alternative hypothesis: true probability of success is not equal to 0.003984064
  95 percent confidence interval:
   0.001090908 0.010209665
  sample estimates:
  frequency of outliers (expected: 0.00398406374501992 ) 
                                                   0.004

Code
  testOutliers(simulationOutput, plot = F, alternative = "two.sided", margin = "lower")
Output

    DHARMa outlier test based on exact binomial test with approximate
    expectations

  data:  simulationOutput
  outliers at lower margin(s) = 4, observations = 1000, p-value = 0.8037
  alternative hypothesis: true probability of success is not equal to 0.003984064
  95 percent confidence interval:
   0.001090908 0.010209665
  sample estimates:
  frequency of outliers (expected: 0.00398406374501992 ) 
                                                   0.004

Code
  testOutliers(simulationOutput, plot = F, margin = "upper")
Output

    DHARMa outlier test based on exact binomial test with approximate
    expectations

  data:  simulationOutput
  outliers at upper margin(s) = 4, observations = 1000, p-value = 0.8037
  alternative hypothesis: true probability of success is not equal to 0.003984064
  95 percent confidence interval:
   0.001090908 0.010209665
  sample estimates:
  frequency of outliers (expected: 0.00398406374501992 ) 
                                                   0.004

DHARMa documentation built on Oct. 18, 2024, 5:09 p.m.