# Parameters from Generalized Additive (Mixed) Models

Source:`R/methods_cgam.R`

, `R/methods_mgcv.R`

, `R/methods_other.R`

, and 1 more
`model_parameters.cgam.Rd`

Extract and compute indices and measures to describe parameters of generalized additive models (GAM(M)s).

## Usage

```
# S3 method for cgam
model_parameters(
model,
ci = 0.95,
ci_method = "residual",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
# S3 method for gamm
model_parameters(
model,
ci = 0.95,
bootstrap = FALSE,
iterations = 1000,
verbose = TRUE,
...
)
# S3 method for Gam
model_parameters(
model,
effectsize_type = NULL,
df_error = NULL,
type = NULL,
table_wide = FALSE,
verbose = TRUE,
...
)
# S3 method for scam
model_parameters(
model,
ci = 0.95,
ci_method = "residual",
bootstrap = FALSE,
iterations = 1000,
standardize = NULL,
exponentiate = FALSE,
p_adjust = NULL,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
```

## Arguments

- model
A gam/gamm model.

- ci
Confidence Interval (CI) level. Default to

`0.95`

(`95%`

).- ci_method
Method for computing degrees of freedom for confidence intervals (CI) and the related p-values. Allowed are following options (which vary depending on the model class):

`"residual"`

,`"normal"`

,`"likelihood"`

,`"satterthwaite"`

,`"kenward"`

,`"wald"`

,`"profile"`

,`"boot"`

,`"uniroot"`

,`"ml1"`

,`"betwithin"`

,`"hdi"`

,`"quantile"`

,`"ci"`

,`"eti"`

,`"si"`

,`"bci"`

, or`"bcai"`

. See section*Confidence intervals and approximation of degrees of freedom*in`model_parameters()`

for further details. When`ci_method=NULL`

, in most cases`"wald"`

is used then.- bootstrap
Should estimates be based on bootstrapped model? If

`TRUE`

, then arguments of Bayesian regressions apply (see also`bootstrap_parameters()`

).- iterations
The number of bootstrap replicates. This only apply in the case of bootstrapped frequentist models.

- standardize
The method used for standardizing the parameters. Can be

`NULL`

(default; no standardization),`"refit"`

(for re-fitting the model on standardized data) or one of`"basic"`

,`"posthoc"`

,`"smart"`

,`"pseudo"`

. See 'Details' in`standardize_parameters()`

.**Importantly**:The

`"refit"`

method does*not*standardize categorical predictors (i.e. factors), which may be a different behaviour compared to other R packages (such as**lm.beta**) or other software packages (like SPSS). to mimic such behaviours, either use`standardize="basic"`

or standardize the data with`datawizard::standardize(force=TRUE)`

*before*fitting the model.For mixed models, when using methods other than

`"refit"`

, only the fixed effects will be standardized.Robust estimation (i.e.,

`vcov`

set to a value other than`NULL`

) of standardized parameters only works when`standardize="refit"`

.

- exponentiate
Logical, indicating whether or not to exponentiate the coefficients (and related confidence intervals). This is typical for logistic regression, or more generally speaking, for models with log or logit links. It is also recommended to use

`exponentiate = TRUE`

for models with log-transformed response values.**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.- p_adjust
Character vector, if not

`NULL`

, indicates the method to adjust p-values. See`stats::p.adjust()`

for details. Further possible adjustment methods are`"tukey"`

,`"scheffe"`

,`"sidak"`

and`"none"`

to explicitly disable adjustment for`emmGrid`

objects (from**emmeans**).- keep
Character containing a regular expression pattern that describes the parameters that should be included (for

`keep`

) or excluded (for`drop`

) in the returned data frame.`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`keep`

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.- drop
See

`keep`

.- verbose
Toggle warnings and messages.

- ...
Arguments passed to or from other methods. For instance, when

`bootstrap = TRUE`

, arguments like`type`

or`parallel`

are passed down to`bootstrap_model()`

.- effectsize_type
The effect size of interest. Not that possibly not all effect sizes are applicable to the model object. See 'Details'. For Anova models, can also be a character vector with multiple effect size names.

- df_error
Denominator degrees of freedom (or degrees of freedom of the error estimate, i.e., the residuals). This is used to compute effect sizes for ANOVA-tables from mixed models. See 'Examples'. (Ignored for

`afex_aov`

.)- type
Numeric, type of sums of squares. May be 1, 2 or 3. If 2 or 3, ANOVA-tables using

`car::Anova()`

will be returned. (Ignored for`afex_aov`

.)- table_wide
Logical that decides whether the ANOVA table should be in wide format, i.e. should the numerator and denominator degrees of freedom be in the same row. Default:

`FALSE`

.

## Details

The reporting of degrees of freedom *for the spline terms*
slightly differs from the output of `summary(model)`

, for example in the
case of `mgcv::gam()`

. The *estimated degrees of freedom*, column
`edf`

in the summary-output, is named `df`

in the returned data
frame, while the column `df_error`

in the returned data frame refers to
the residual degrees of freedom that are returned by `df.residual()`

.
Hence, the values in the the column `df_error`

differ from the column
`Ref.df`

from the summary, which is intentional, as these reference
degrees of freedom “is not very interpretable”
(web).

## See also

`insight::standardize_names()`

to rename
columns into a consistent, standardized naming scheme.

## Examples

```
library(parameters)
if (require("mgcv")) {
dat <- gamSim(1, n = 400, dist = "normal", scale = 2)
model <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
model_parameters(model)
}
#> Loading required package: mgcv
#> Loading required package: nlme
#>
#> Attaching package: ‘nlme’
#> The following object is masked from ‘package:lme4’:
#>
#> lmList
#> This is mgcv 1.9-0. For overview type 'help("mgcv-package")'.
#>
#> Attaching package: ‘mgcv’
#> The following object is masked from ‘package:mclust’:
#>
#> mvn
#> Gu & Wahba 4 term additive model
#> # Fixed Effects
#>
#> Parameter | Coefficient | SE | 95% CI | t(384.42) | p
#> --------------------------------------------------------------------
#> (Intercept) | 7.78 | 0.10 | [7.58, 7.97] | 79.05 | < .001
#>
#> # Smooth Terms
#>
#> Parameter | F | df | p
#> ----------------------------------------
#> Smooth term (x0) | 4.83 | 2.61 | 0.002
#> Smooth term (x1) | 86.71 | 3.03 | < .001
#> Smooth term (x2) | 75.26 | 7.95 | < .001
#> Smooth term (x3) | 0.04 | 1.00 | 0.839
```