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,
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,
wb_component = TRUE,
summary = FALSE,
keep = NULL,
drop = NULL,
parameters = keep,
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,
wb_component = TRUE,
summary = FALSE,
keep = NULL,
drop = NULL,
parameters = keep,
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,
verbose = TRUE,
...
)

## Arguments

model 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 datawizard::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 containing a regular expression pattern that describes the parameters that should be included in the returned data frame (for keep), resp. parameters to exclude (drop). keep may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If keep is a character vector, every parameter name in the "Parameter" column that matches the regular expression in parameters will be selected from the returned data frame (and vice versa, all parameter names matching drop will be excluded). Furthermore, if keep has more than one element, these will be merged with an OR operator into a regular expression pattern like this: "(one|two|three)". If keep 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. Note that the regular expression pattern should match the parameter names as they are stored in the returned data frame, which can be different from how they are printed. Inspect the $Parameter column of the parameters table to get the exact parameter names. Character containing a regular expression pattern that describes the parameters that should be included in the returned data frame (for keep), resp. parameters to exclude (drop). keep may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If keep is a character vector, every parameter name in the "Parameter" column that matches the regular expression in parameters will be selected from the returned data frame (and vice versa, all parameter names matching drop will be excluded). Furthermore, if keep has more than one element, these will be merged with an OR operator into a regular expression pattern like this: "(one|two|three)". If keep 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. Note that the regular expression pattern should match the parameter names as they are stored in the returned data frame, which can be different from how they are printed. Inspect the $Parameter column of the parameters table to get the exact parameter names. Deprecated, alias for keep. 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.

## Value

A data frame of indices related to the model's parameters.

## Note

There is also a plot()-method implemented in the see-package.

standardize_names() to rename columns into a consistent, standardized naming scheme.

## Examples

library(parameters)
if (require("lme4")) {
data(mtcars)
model <- lmer(mpg ~ wt + (1 | gear), data = mtcars)
model_parameters(model)
}
#>
#> Attaching package: ‘lme4’
#> The following object is masked from ‘package:nlme’:
#>
#>     lmList
#> # Fixed Effects
#>
#> Parameter   | Coefficient |   SE |         95% CI | t(28) |      p
#> ------------------------------------------------------------------
#> (Intercept) |       36.19 | 2.19 | [31.90, 40.48] | 16.52 | < .001
#> wt          |       -5.05 | 0.64 | [-6.30, -3.79] | -7.89 | < .001
#>
#> # Random Effects
#>
#> Parameter            | Coefficient
#> ----------------------------------
#> SD (Intercept: gear) |        1.26
#> SD (Residual)        |        2.91
# \donttest{
if (require("glmmTMB")) {
data(Salamanders)
model <- glmmTMB(
count ~ spp + mined + (1 | site),
ziformula = ~mined,
family = poisson(),
data = Salamanders
)
model_parameters(model, effects = "all")
}
#> Warning: 'giveCsparse' has been deprecated; setting 'repr = "T"' for you
#> Warning: 'giveCsparse' has been deprecated; setting 'repr = "T"' for you
#> Warning: 'giveCsparse' has been deprecated; setting 'repr = "T"' for you
#> # Fixed Effects (Count Model)
#>
#> Parameter   | Log-Mean |   SE |         95% CI |     z |      p
#> ---------------------------------------------------------------
#> (Intercept) |    -0.36 | 0.28 | [-0.90,  0.18] | -1.30 | 0.194
#> spp [PR]    |    -1.27 | 0.24 | [-1.74, -0.80] | -5.27 | < .001
#> spp [DM]    |     0.27 | 0.14 | [ 0.00,  0.54] |  1.95 | 0.051
#> spp [EC-A]  |    -0.57 | 0.21 | [-0.97, -0.16] | -2.75 | 0.006
#> spp [EC-L]  |     0.67 | 0.13 | [ 0.41,  0.92] |  5.20 | < .001
#> spp [DES-L] |     0.63 | 0.13 | [ 0.38,  0.87] |  4.96 | < .001
#> spp [DF]    |     0.12 | 0.15 | [-0.17,  0.40] |  0.78 | 0.435
#> mined [no]  |     1.27 | 0.27 | [ 0.74,  1.80] |  4.72 | < .001
#>
#> # Fixed Effects (Zero-Inflated Model)
#>
#> Parameter   | Log-Odds |   SE |         95% CI |     z |      p
#> ---------------------------------------------------------------
#> (Intercept) |     0.79 | 0.27 | [ 0.26,  1.32] |  2.90 | 0.004
#> mined [no]  |    -1.84 | 0.31 | [-2.46, -1.23] | -5.87 | < .001
#>
#> # Random Effects Variances
#>
#> Parameter            | Coefficient
#> ----------------------------------
#> SD (Intercept: site) |        0.33
#> SD (Residual)        |        1.00
#>
#> # Random Effects (Zero-Inflated Model)
#>
#> Parameter     | Coefficient
#> ---------------------------
#> SD (Residual) |        1.00

if (require("lme4")) {
model <- lmer(mpg ~ wt + (1 | gear), data = mtcars)
model_parameters(model, bootstrap = TRUE, iterations = 50)
}
#> Warning: Bootstrapping only returns fixed effects of the mixed model.
#> # Fixed Effects
#>
#> Parameter   | Coefficient |         95% CI |     p
#> --------------------------------------------------
#> (Intercept) |       36.24 | [30.58, 40.06] | 0.020
#> wt          |       -5.05 | [-6.11, -3.29] | 0.020
# }