Returns the coefficients (or posterior samples for Bayesian models) from a model.
get_parameters(x, ...)# S3 method for betamfx
get_parameters(
x,
component = c("all", "conditional", "precision", "marginal"),
...
)
# S3 method for logitmfx
get_parameters(x, component = c("all", "conditional", "marginal"), ...)
# S3 method for emmGrid
get_parameters(x, summary = FALSE, merge_parameters = FALSE, ...)
# S3 method for averaging
get_parameters(x, component = c("conditional", "full"), ...)
# S3 method for betareg
get_parameters(x, component = c("all", "conditional", "precision"), ...)
# S3 method for DirichletRegModel
get_parameters(x, component = c("all", "conditional", "precision"), ...)
# S3 method for clm2
get_parameters(x, component = c("all", "conditional", "scale"), ...)
# S3 method for coxme
get_parameters(x, effects = c("fixed", "random"), ...)
# S3 method for merMod
get_parameters(x, effects = c("fixed", "random"), ...)
# S3 method for mixed
get_parameters(x, effects = c("fixed", "random"), ...)
# S3 method for glmmTMB
get_parameters(
x,
effects = c("fixed", "random"),
component = c("all", "conditional", "zi", "zero_inflated", "dispersion"),
...
)
# S3 method for BBmm
get_parameters(x, effects = c("fixed", "random"), ...)
# S3 method for glimML
get_parameters(x, effects = c("fixed", "random", "all"), ...)
# S3 method for gam
get_parameters(x, component = c("all", "conditional", "smooth_terms"), ...)
# S3 method for zeroinfl
get_parameters(
x,
component = c("all", "conditional", "zi", "zero_inflated"),
...
)
# S3 method for zcpglm
get_parameters(
x,
component = c("all", "conditional", "zi", "zero_inflated"),
...
)
# S3 method for BGGM
get_parameters(
x,
component = c("correlation", "conditional", "intercept", "all"),
summary = FALSE,
centrality = "mean",
...
)
# S3 method for MCMCglmm
get_parameters(
x,
effects = c("fixed", "random", "all"),
summary = FALSE,
centrality = "mean",
...
)
# S3 method for BFBayesFactor
get_parameters(
x,
effects = c("all", "fixed", "random"),
component = c("all", "extra"),
iterations = 4000,
progress = FALSE,
verbose = TRUE,
...
)
# S3 method for stanmvreg
get_parameters(
x,
effects = c("fixed", "random", "all"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for brmsfit
get_parameters(
x,
effects = c("fixed", "random", "all"),
component = c("all", "conditional", "zi", "zero_inflated", "dispersion", "simplex",
"sigma", "smooth_terms"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for stanreg
get_parameters(
x,
effects = c("fixed", "random", "all"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
# S3 method for sim.merMod
get_parameters(
x,
effects = c("fixed", "random", "all"),
parameters = NULL,
summary = FALSE,
centrality = "mean",
...
)
A fitted model.
Currently not used.
Should all parameters, parameters for the conditional model, the zero-inflated part of the model, the dispersion term, the instrumental variables or marginal effects be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variables (so called fixed-effects regressions), or models with marginal effects from mfx. May be abbreviated. Note that the conditional component is also called count or mean component, depending on the model.
Logical, indicates whether the full posterior samples
(summary = FALSE)) or the summarized centrality indices of
the posterior samples (summary = TRUE)) should be returned as
estimates.
Logical, if TRUE and x has multiple
columns for parameter names (like emmGrid objects may have), these
are merged into a single parameter column, with parameters names and values
as values.
Should parameters for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
Only for models with posterior samples, and when
summary = TRUE. In this case, centrality = "mean" would
calculate means of posterior samples for each parameter, while
centrality = "median" would use the more robust median value as
measure of central tendency.
Number of posterior draws.
Display progress.
Toggle messages and warnings.
Regular expression pattern that describes the parameters that should be returned.
for non-Bayesian models and if effects = "fixed", a data frame with two columns: the parameter names and the related point estimates
if effects = "random", a list of data frames with the random effects (as returned by ranef()), unless the random effects have the same simplified structure as fixed effects (e.g. for models from MCMCglmm)
for Bayesian models, the posterior samples from the requested parameters as data frame
for Anova (aov()) with error term, a list of parameters for the conditional and the random effects parameters
for models with smooth terms or zero-inflation component, a data frame with three columns: the parameter names, the related point estimates and the component
Note that for BFBayesFactor models (from the BayesFactor
package), posteriors are only extracted from the first numerator model (i.e.,
model[1]). If you want to apply some function foo() to another
model stored in the BFBayesFactor object, index it directly, e.g.
foo(model[2]), foo(1/model[5]), etc.
See also weighted_posteriors.
In most cases when models either return different "effects" (fixed,
random) or "components" (conditional, zero-inflated, ...), the arguments
effects and component can be used.
get_parameters() is comparable to coef(), however, the coefficients
are returned as data frame (with columns for names and point estimates of
coefficients). For Bayesian models, the posterior samples of parameters are
returned.
# NOT RUN {
data(mtcars)
m <- lm(mpg ~ wt + cyl + vs, data = mtcars)
get_parameters(m)
# }
Run the code above in your browser using DataLab