model_parameters.cpglmm  R Documentation 
Parameters from (linear) mixed models.
## S3 method for class 'cpglmm'
model_parameters(
model,
ci = 0.95,
ci_method = NULL,
ci_random = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
effects = "all",
group_level = FALSE,
exponentiate = FALSE,
p_adjust = NULL,
include_sigma = FALSE,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
## S3 method for class 'glmmTMB'
model_parameters(
model,
ci = 0.95,
ci_method = "wald",
ci_random = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
effects = "all",
component = "all",
group_level = FALSE,
exponentiate = FALSE,
p_adjust = NULL,
wb_component = TRUE,
summary = getOption("parameters_mixed_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
include_sigma = FALSE,
...
)
## S3 method for class 'merMod'
model_parameters(
model,
ci = 0.95,
ci_method = NULL,
ci_random = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
effects = "all",
group_level = FALSE,
exponentiate = FALSE,
p_adjust = NULL,
wb_component = TRUE,
summary = getOption("parameters_mixed_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
include_sigma = FALSE,
vcov = NULL,
vcov_args = NULL,
...
)
## S3 method for class 'mixed'
model_parameters(
model,
ci = 0.95,
ci_method = "wald",
ci_random = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
effects = "all",
component = "all",
group_level = FALSE,
exponentiate = FALSE,
p_adjust = NULL,
wb_component = TRUE,
summary = getOption("parameters_mixed_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
include_sigma = FALSE,
...
)
## S3 method for class 'MixMod'
model_parameters(
model,
ci = 0.95,
ci_method = "wald",
ci_random = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
effects = "all",
component = "all",
group_level = FALSE,
exponentiate = FALSE,
p_adjust = NULL,
wb_component = TRUE,
summary = getOption("parameters_mixed_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
include_sigma = FALSE,
...
)
## S3 method for class 'lme'
model_parameters(
model,
ci = 0.95,
ci_method = NULL,
ci_random = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
effects = "all",
group_level = FALSE,
exponentiate = FALSE,
p_adjust = NULL,
wb_component = TRUE,
summary = getOption("parameters_mixed_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
include_sigma = FALSE,
vcov = NULL,
vcov_args = NULL,
...
)
## S3 method for class 'clmm2'
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
component = c("all", "conditional", "scale"),
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
summary = getOption("parameters_summary", FALSE),
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
## S3 method for class 'clmm'
model_parameters(
model,
ci = 0.95,
ci_method = NULL,
ci_random = NULL,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
effects = "all",
group_level = FALSE,
exponentiate = FALSE,
p_adjust = NULL,
include_sigma = FALSE,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
model 
A mixed model. 
ci 
Confidence Interval (CI) level. Default to 
ci_method 
Method for computing degrees of freedom for
confidence intervals (CI) and the related pvalues. Allowed are following
options (which vary depending on the model class): 
ci_random 
Logical, if 
bootstrap 
Should estimates be based on bootstrapped model? If

iterations 
The number of draws to simulate/bootstrap. 
standardize 
The method used for standardizing the parameters. Can be

effects 
Should parameters for fixed effects ( 
group_level 
Logical, for multilevel models (i.e. models with random
effects) and when 
exponentiate 
Logical, indicating whether or not to exponentiate the
coefficients (and related confidence intervals). This is typical for
logistic regression, or more generally speaking, for models with log or
logit links. It is also recommended to use 
p_adjust 
Character vector, if not 
include_sigma 
Logical, if 
keep 
Character containing a regular expression pattern that
describes the parameters that should be included (for 
drop 
See 
verbose 
Toggle warnings and messages. 
... 
Arguments passed to or from other methods. For instance, when

component 
Should all parameters, parameters for the conditional model,
for the zeroinflation part of the model, or the dispersion model be returned?
Applies to models with zeroinflation and/or dispersion component. 
wb_component 
Logical, if 
summary 
Logical, if 
vcov 
Variancecovariance matrix used to compute uncertainty estimates (e.g., for robust standard errors). This argument accepts a covariance matrix, a function which returns a covariance matrix, or a string which identifies the function to be used to compute the covariance matrix.

vcov_args 
List of arguments to be passed to the function identified by
the 
A data frame of indices related to the model's parameters.
For models of class merMod
and glmmTMB
, confidence intervals for random
effect variances can be calculated.
For models of from package lme4, when ci_method
is either "profile"
or "boot"
, and effects
is either "random"
or "all"
, profiled resp.
bootstrapped confidence intervals are computed for the random effects.
For all other options of ci_method
, and only when the merDeriv
package is installed, confidence intervals for random effects are based on
normaldistribution approximation, using the deltamethod to transform
standard errors for constructing the intervals around the logtransformed
SD parameters. These are than backtransformed, so that random effect
variances, standard errors and confidence intervals are shown on the original
scale. Due to the transformation, the intervals are asymmetrical, however,
they are within the correct bounds (i.e. no negative interval for the SD,
and the interval for the correlations is within the range from 1 to +1).
For models of class glmmTMB
, confidence intervals for random effect
variances always use a Wald tdistribution approximation.
If a model is "singular", this means that some dimensions of the variancecovariance matrix have been estimated as exactly zero. This often occurs for mixed models with complex random effects structures.
There is no goldstandard about how to deal with singularity and which
randomeffects specification to choose. One way is to fully go Bayesian
(with informative priors). Other proposals are listed in the documentation
of performance::check_singularity()
. However, 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 ) 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 safely try larger values.
For some models from package glmmTMB, both the dispersion parameter and the residual variance from the random effects parameters are shown. Usually, these are the same but presented on different scales, e.g.
model < glmmTMB(Sepal.Width ~ Petal.Length + (1Species), data = iris) exp(fixef(model)$disp) # 0.09902987 sigma(model)^2 # 0.09902987
For models where the dispersion parameter and the residual variance are the same, only the residual variance is shown in the output.
There are different ways of approximating the degrees of freedom depending
on different assumptions about the nature of the model and its sampling
distribution. The ci_method
argument modulates the method for computing degrees
of freedom (df) that are used to calculate confidence intervals (CI) and the
related pvalues. Following options are allowed, depending on the model
class:
Classical methods:
Classical inference is generally based on the Wald method. The Wald approach to inference computes a test statistic by dividing the parameter estimate by its standard error (Coefficient / SE), then comparing this statistic against a t or normal distribution. This approach can be used to compute CIs and pvalues.
"wald"
:
Applies to nonBayesian models. For linear models, CIs computed using the Wald method (SE and a tdistribution with residual df); pvalues computed using the Wald method with a tdistribution with residual df. For other models, CIs computed using the Wald method (SE and a normal distribution); pvalues computed using the Wald method with a normal distribution.
"normal"
Applies to nonBayesian models. Compute Wald CIs and pvalues, but always use a normal distribution.
"residual"
Applies to nonBayesian models. Compute Wald CIs and pvalues, but always use a tdistribution with residual df when possible. If the residual df for a model cannot be determined, a normal distribution is used instead.
Methods for mixed models:
Compared to fixed effects (or singlelevel) models, determining appropriate df for Waldbased inference in mixed models is more difficult. See the R GLMM FAQ for a discussion.
Several approximate methods for computing df are available, but you should
also consider instead using profile likelihood ("profile"
) or bootstrap ("boot"
)
CIs and pvalues instead.
"satterthwaite"
Applies to linear mixed models. CIs computed using the Wald method (SE and a tdistribution with Satterthwaite df); pvalues computed using the Wald method with a tdistribution with Satterthwaite df.
"kenward"
Applies to linear mixed models. CIs computed using the Wald method (KenwardRoger SE and a tdistribution with KenwardRoger df); pvalues computed using the Wald method with KenwardRoger SE and tdistribution with KenwardRoger df.
"ml1"
Applies to linear mixed models. CIs computed using the Wald
method (SE and a tdistribution with ml1 approximated df); pvalues
computed using the Wald method with a tdistribution with ml1 approximated df.
See ci_ml1()
.
"betwithin"
Applies to linear mixed models and generalized linear mixed models.
CIs computed using the Wald method (SE and a tdistribution with betweenwithin df);
pvalues computed using the Wald method with a tdistribution with betweenwithin df.
See ci_betwithin()
.
Likelihoodbased methods:
Likelihoodbased inference is based on comparing the likelihood for the
maximumlikelihood estimate to the the likelihood for models with one or more
parameter values changed (e.g., set to zero or a range of alternative values).
Likelihood ratios for the maximumlikelihood and alternative models are compared
to a \chi
squared distribution to compute CIs and pvalues.
"profile"
Applies to nonBayesian models of class glm
, polr
, merMod
or glmmTMB
.
CIs computed by profiling the likelihood curve for a parameter, using
linear interpolation to find where likelihood ratio equals a critical value;
pvalues computed using the Wald method with a normaldistribution (note:
this might change in a future update!)
"uniroot"
Applies to nonBayesian models of class glmmTMB
. CIs
computed by profiling the likelihood curve for a parameter, using root
finding to find where likelihood ratio equals a critical value; pvalues
computed using the Wald method with a normaldistribution (note: this
might change in a future update!)
Methods for bootstrapped or Bayesian models:
Bootstrapbased inference is based on resampling and refitting the model to the resampled datasets. The distribution of parameter estimates across resampled datasets is used to approximate the parameter's sampling distribution. Depending on the type of model, several different methods for bootstrapping and constructing CIs and pvalues from the bootstrap distribution are available.
For Bayesian models, inference is based on drawing samples from the model posterior distribution.
"quantile"
(or "eti"
)
Applies to all models (including Bayesian models).
For nonBayesian models, only applies if bootstrap = TRUE
. CIs computed
as equal tailed intervals using the quantiles of the bootstrap or
posterior samples; pvalues are based on the probability of direction.
See bayestestR::eti()
.
"hdi"
Applies to all models (including Bayesian models). For nonBayesian
models, only applies if bootstrap = TRUE
. CIs computed as highest density intervals
for the bootstrap or posterior samples; pvalues are based on the probability of direction.
See bayestestR::hdi()
.
"bci"
(or "bcai"
)
Applies to all models (including Bayesian models).
For nonBayesian models, only applies if bootstrap = TRUE
. CIs computed
as bias corrected and accelerated intervals for the bootstrap or
posterior samples; pvalues are based on the probability of direction.
See bayestestR::bci()
.
"si"
Applies to Bayesian models with proper priors. CIs computed as
support intervals comparing the posterior samples against the prior samples;
pvalues are based on the probability of direction. See bayestestR::si()
.
"boot"
Applies to nonBayesian models of class merMod
. CIs computed
using parametric bootstrapping (simulating data from the fitted model);
pvalues computed using the Wald method with a normaldistribution)
(note: this might change in a future update!).
For all iterationbased methods other than "boot"
("hdi"
, "quantile"
, "ci"
, "eti"
, "si"
, "bci"
, "bcai"
),
pvalues are based on the probability of direction (bayestestR::p_direction()
),
which is converted into a pvalue using bayestestR::pd_to_p()
.
If the calculation of random effects parameters takes too long, you may
use effects = "fixed"
. There is also a plot()
method
implemented in the seepackage.
Chung Y, RabeHesketh S, Dorie V, Gelman A, and Liu J. 2013. "A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models." Psychometrika 78 (4): 685–709. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s1133601393282")}
insight::standardize_names()
to
rename columns into a consistent, standardized naming scheme.
library(parameters)
data(mtcars)
model < lme4::lmer(mpg ~ wt + (1  gear), data = mtcars)
model_parameters(model)
data(Salamanders, package = "glmmTMB")
model < glmmTMB::glmmTMB(
count ~ spp + mined + (1  site),
ziformula = ~mined,
family = poisson(),
data = Salamanders
)
model_parameters(model, effects = "all")
model < lme4::lmer(mpg ~ wt + (1  gear), data = mtcars)
model_parameters(model, bootstrap = TRUE, iterations = 50, verbose = FALSE)
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