Parameters from (linear) mixed models.
# S3 method for cpglmm
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
effects = "all",
group_level = FALSE,
exponentiate = FALSE,
df_method = NULL,
p_adjust = NULL,
verbose = TRUE,
...
)# S3 method for glmmTMB
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
effects = "all",
component = "all",
group_level = FALSE,
standardize = NULL,
exponentiate = FALSE,
df_method = NULL,
p_adjust = NULL,
wb_component = TRUE,
summary = FALSE,
parameters = NULL,
verbose = TRUE,
...
)
# S3 method for merMod
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
df_method = "wald",
iterations = 1000,
standardize = NULL,
effects = "all",
group_level = FALSE,
exponentiate = FALSE,
robust = FALSE,
p_adjust = NULL,
wb_component = TRUE,
summary = FALSE,
parameters = NULL,
verbose = TRUE,
...
)
# S3 method for mixor
model_parameters(
model,
ci = 0.95,
effects = "all",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
verbose = TRUE,
...
)
# S3 method for clmm
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
effects = "all",
group_level = FALSE,
exponentiate = FALSE,
df_method = NULL,
p_adjust = NULL,
verbose = TRUE,
...
)
A mixed model.
Confidence Interval (CI) level. Default to 0.95 (95%).
Should estimates be based on bootstrapped model? If
TRUE, then arguments of Bayesian
regressions apply (see also
bootstrap_parameters()).
The number of draws to simulate/bootstrap.
The method used for standardizing the parameters. Can be
"refit", "posthoc", "smart", "basic",
"pseudo" or NULL (default) for no standardization. See
'Details' in standardize_parameters.
Important: Categorical predictors (i.e. factors) are never
standardized by default, which may be a different behaviour compared to
other R packages or other software packages (like SPSS). If standardizing
categorical predictors is desired, either use standardize="basic"
to mimic behaviour of SPSS or packages such as lm.beta, or standardize
the data with effectsize::standardize(force=TRUE) before fitting
the model. Robust estimation (i.e. robust=TRUE) of standardized
parameters only works when standardize="refit".
Should parameters for fixed effects ("fixed"), random
effects ("random"), or both ("all") be returned? Only applies
to mixed models. May be abbreviated.
Logical, for multilevel models (i.e. models with random
effects) and when effects = "all" or effects = "random",
include the parameters for each group level from random effects. If
group_level = FALSE (the default), only information on SD and COR
are shown.
Logical, indicating whether or not to exponentiate the
the coefficients (and related confidence intervals). This is typical for
logistic regression, or more generally speaking, for models with log
or logit links. Note: Delta-method standard errors are also
computed (by multiplying the standard errors by the transformed
coefficients). This is to mimic behaviour of other software packages, such
as Stata, but these standard errors poorly estimate uncertainty for the
transformed coefficient. The transformed confidence interval more clearly
captures this uncertainty. For compare_parameters(),
exponentiate = "nongaussian" will only exponentiate coefficients
from non-Gaussian families.
Method for computing degrees of freedom for p values,
standard errors and confidence intervals (CI). May be "wald"
(default, see degrees_of_freedom), "ml1" (see
dof_ml1), "betwithin" (see
dof_betwithin), "satterthwaite" (see
dof_satterthwaite) or "kenward" (see
dof_kenward). The options df_method = "boot",
df_method = "profile" and df_method = "uniroot" only affect
confidence intervals; in this case, bootstrapped resp. profiled confidence
intervals are computed. "uniroot" only applies to models of class
glmmTMB. For models of class lmerMod, when
df_method = "wald", residual degrees of freedom are returned.
Note that when df_method is not "wald", robust standard
errors etc. cannot be computed.
Character vector, if not NULL, indicates the method to
adjust p-values. See p.adjust for details. Further
possible adjustment methods are "tukey", "scheffe",
"sidak" and "none" to explicitly disable adjustment for
emmGrid objects (from emmeans).
Toggle warnings and messages.
Arguments passed to or from other methods.
Should all parameters, parameters for the conditional model,
or for the zero-inflated part of the model be returned? Applies to models
with zero-inflated component. component may be one of "conditional",
"zi", "zero-inflated", "dispersion" or "all"
(default). May be abbreviated.
Logical, if TRUE and models contains within- and
between-effects (see demean), the Component column
will indicate which variables belong to the within-effects,
between-effects, and cross-level interactions. By default, the
Component column indicates, which parameters belong to the
conditional or zero-inflated component of the model.
Logical, if TRUE, prints summary information about the
model (model formula, number of observations, residual standard deviation
and more).
Character vector of length 1 with a regular expression pattern
that describes the parameters that should be returned from the data frame, or
a named list of regular expressions. All non-matching parameters will be
removed from the output. If parameters is a character vector, every
parameter in the "Parameters" column that matches the regular expression in
parameters will be selected from the returned data frame. Furthermore,
if parameters has more than one element, these will be merged into
a regular expression pattern like this: "(one|two|three)". If
parameters is a named list of regular expression patterns, the
names of the list-element should equal the column name where selection
should be applied. This is useful for model objects where
model_parameters() returns multiple columns with parameter components,
like in model_parameters.lavaan.
Logical, if TRUE, robust standard errors are calculated
(if possible), and confidence intervals and p-values are based on these
robust standard errors. Additional arguments like vcov_estimation or
vcov_type are passed down to other methods, see
standard_error_robust() for details
and this vignette
for working examples.
A data frame of indices related to the model's parameters.
standardize_names() to
rename columns into a consistent, standardized naming scheme.
# NOT RUN {
library(parameters)
if (require("lme4")) {
data(mtcars)
model <- lmer(mpg ~ wt + (1 | gear), data = mtcars)
model_parameters(model)
}
# }
# NOT RUN {
if (require("glmmTMB")) {
data(Salamanders)
model <- glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~mined,
family = poisson(),
data = Salamanders
)
model_parameters(model, effects = "all")
}
if (require("lme4")) {
model <- lmer(mpg ~ wt + (1 | gear), data = mtcars)
model_parameters(model, bootstrap = TRUE, iterations = 50)
}
# }
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