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Parameters of (linear) mixed models.
# S3 method for merMod
model_parameters(model, ci = 0.95, bootstrap = FALSE,
p_method = "wald", ci_method = "wald", iterations = 1000,
standardize = NULL, exponentiate = FALSE, ...)# S3 method for glmmTMB
model_parameters(model, ci = 0.95, bootstrap = FALSE,
iterations = 1000, component = c("all", "conditional", "zi",
"zero_inflated"), standardize = NULL, exponentiate = FALSE, ...)
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 parameters_bootstrap()
).
Method for computing p values. See p_value()
.
Method for computing confidence intervals (CI). See ci()
.
The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.
The method used for standardizing the parameters. Can be "refit"
, "posthoc"
, "smart"
, "basic"
or NULL
(default) for no standardization. See 'Details' in standardize_parameters
.
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.
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"
or "all"
(default). May be abbreviated.
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)
library(lme4)
library(glmmTMB)
model <- lmer(mpg ~ wt + (1 | gear), data = mtcars)
model_parameters(model)
model <- glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~mined,
family = poisson(),
data = Salamanders
)
model_parameters(model)
# }
# NOT RUN {
model <- lme4::lmer(mpg ~ wt + (1 | gear), data = mtcars)
model_parameters(model, bootstrap = TRUE, iterations = 50)
# }
# NOT RUN {
# }
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