Parameters from (linear) mixed models.
# S3 method for cpglmm
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
effects = "fixed",
group_level = FALSE,
exponentiate = FALSE,
details = FALSE,
df_method = NULL,
p_adjust = NULL,
verbose = TRUE,
...
)# S3 method for glmmTMB
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
effects = "fixed",
component = "all",
group_level = FALSE,
standardize = NULL,
exponentiate = FALSE,
df_method = NULL,
details = FALSE,
p_adjust = NULL,
wb_component = TRUE,
summary = FALSE,
verbose = TRUE,
...
)
# S3 method for merMod
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
df_method = "wald",
iterations = 1000,
standardize = NULL,
effects = "fixed",
group_level = FALSE,
exponentiate = FALSE,
robust = FALSE,
details = FALSE,
p_adjust = NULL,
wb_component = TRUE,
summary = FALSE,
verbose = TRUE,
...
)
# S3 method for mixor
model_parameters(
model,
ci = 0.95,
effects = "fixed",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
details = FALSE,
verbose = TRUE,
...
)
# S3 method for clmm
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
effects = "fixed",
group_level = FALSE,
exponentiate = FALSE,
details = 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. Note that
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,
say, logistic regressions, or more generally speaking: for models with log
or logit link. Note: standard errors are also transformed (by
multiplying the standard errors with the exponentiated coefficients), to
mimic behaviour of other software packages, such as Stata. For
compare_parameters(), exponentiate = "nongaussian" will only
exponentiate coefficients for all models except those from Gaussian family.
Logical, if TRUE, a summary of the random effects is
included. See random_parameters for details.
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. 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).
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, details = TRUE)
}
if (require("lme4")) {
model <- lmer(mpg ~ wt + (1 | gear), data = mtcars)
model_parameters(model, bootstrap = TRUE, iterations = 50)
}
# }
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