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z*c~fM-k+>Tq_-^(+{8g}bD4jiZpjai#4z=f$dIpcrbSEKahGiM?+$+!yA?~gecjR8+UkOE z2Hg_((BRU&CieD1+S=MI0W#md^s1QWXlbRZquad>4c{p% ztDWeUy@ai8-Q6RGog<2ii$8w)bPD+7k|j&T_Uw7#EK&XL-AT-#h{(>)ZZOX|@z8T` zy88A3M{)cY_+Ay3YQ3rOj}HY$tE;QCva^HV+1`4re-;0onwt7JIM_2jzB~9icaGLh zignvFc!cnA>nug_d+G)pvx|x%T#G%E$2Lq4WDN%J>y;Sc!}j<0gVmIGbaaHdwz{S! z`|jPV)_LLhg0OdYp-%60EL3q2<*!S7`3Y4I_4Vs#z->Mwgy6DF?Bxm?P)VgTable 1Summary of Statistical Outlier Detection Methods Recommendations

- - + +
- - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + +

Statistical Test

Diagnosis Method

Recommended Threshold

Function Usage

Statistical Test

Diagnosis Method

Recommended Threshold

Function Usage

Supported regression model

Model-based: Cook (or Pareto for Bayesian models)

qf(0.5, ncol(x), nrow(x) - ncol(x)) (or 0.7 for Pareto)

check_outliers(model, method = cook)

Supported regression model

Model-based: Cook (or Pareto for Bayesian models)

qf(0.5, ncol(x), nrow(x) - ncol(x)) (or 0.7 for Pareto)

check_outliers(model, method = cook)

Structural Equation Modeling (or other unsupported model)

Multivariate: Minimum Covariance Determinant (MCD)

qchisq(p = 1 - 0.001, df = ncol(x))

check_outliers(data, method = mcd)

Structural Equation Modeling (or other unsupported model)

Multivariate: Minimum Covariance Determinant (MCD)

qchisq(p = 1 - 0.001, df = ncol(x))

check_outliers(data, method = mcd)

Simple test with few variables (t test, correlation, etc.)

Univariate: robust z scores (MAD)

qnorm(p = 1 - 0.001 / 2), ~ 3.29

check_outliers(data, method = zscore_robust)

Simple test with few variables (t test, correlation, etc.)

Univariate: robust z scores (MAD)

qnorm(p = 1 - 0.001 / 2), ~ 3.29

check_outliers(data, method = zscore_robust)

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"user.child" ... #> $ randomState :List of 4 #> - attr(*, "class")= chr [1:3] "performance_simres" "see_performance_simres" "DHARMa"

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  • since version 1.1.9, the glmmTMB allows to use priors in a frequentist -framework, too. One recommendation is to use a Gamma prior (Chung et al. 2013). -The mean may vary from 1 to very large values (like 1e8), and the shape -parameter should be set to a value of 2.5. You can then update() your model -with the specified prior. In glmmTMB, the code would look like this:

    +
  • since version 1.1.9, the glmmTMB package allows to use priors in a +frequentist framework, too. One recommendation is to use a Gamma prior +(Chung et al. 2013). The mean may vary from 1 to very large values +(like 1e8), and the shape parameter should be set to a value of 2.5. You +can then update() your model with the specified prior. In glmmTMB, +the code would look like this:

    # "model" is an object of class gmmmTMB
     prior <- data.frame(
    -  prior = "gamma(1, 2.5)", # mean can be 1, but even 1e8
    -  class = "ranef" # for random effects
    +  prior = "gamma(1, 2.5)",  # mean can be 1, but even 1e8
    +  class = "ranef"           # for random effects
     )
     model_with_priors <- update(model, priors = prior)

    Large values for the mean parameter of the Gamma prior have no large impact on the random effects variances in terms of a "bias". Thus, if 1 doesn't -fix the singular fit, you can try larger values.

  • +fix the singular fit, you can safely try larger values.

    Note the different meaning between singularity and convergence: singularity indicates an issue with the "true" best estimate, i.e. whether the maximum likelihood estimation for the variance-covariance matrix of the random @@ -193,8 +194,7 @@

    See also

    Examples

    -
    library(lme4)
    -data(sleepstudy)
    +    
    data(sleepstudy, package = "lme4")
     set.seed(123)
     sleepstudy$mygrp <- sample(1:5, size = 180, replace = TRUE)
     sleepstudy$mysubgrp <- NA
    @@ -204,14 +204,36 @@ 

    Examples sample(1:30, size = sum(filter_group), replace = TRUE) } -model <- lmer( +model <- lme4::lmer( Reaction ~ Days + (1 | mygrp / mysubgrp) + (1 | Subject), data = sleepstudy ) #> boundary (singular) fit: see help('isSingular') +check_singularity(model) +#> [1] TRUE +# Fixing singularity issues using priors in glmmTMB +# Example taken from `vignette("priors", package = "glmmTMB")` +dat <- readRDS(system.file("vignette_data", "gophertortoise.rds", + package = "glmmTMB")) +model <- glmmTMB::glmmTMB( + shells ~ prev + offset(log(Area)) + factor(year) + (1 | Site), + family = poisson, + data = dat +) +# singular fit check_singularity(model) #> [1] TRUE + +# impose Gamma prior on random effects parameters +prior <- data.frame( + prior = "gamma(1, 2.5)", # mean can be 1, but even 1e8 + class = "ranef" # for random effects +) +model_with_priors <- update(model, priors = prior) +# no singular fit +check_singularity(model_with_priors) +#> [1] FALSE

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    Details

    "keep it maximal", i.e. fit the most complex model consistent with the experimental design, removing only terms required to allow a non-singular fit (Barr et al. 2013)

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