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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 class '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 class 'gamm'
model_parameters(
  model,
  ci = 0.95,
  bootstrap = FALSE,
  iterations = 1000,
  verbose = TRUE,
  ...
)

# S3 method for class 'Gam'
model_parameters(
  model,
  es_type = NULL,
  df_error = NULL,
  type = NULL,
  table_wide = FALSE,
  verbose = TRUE,
  ...
)

# S3 method for class '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. For models with a log-transformed response variable, when exponentiate = TRUE, a one-unit increase in the predictor is associated with multiplying the outcome by that predictor's coefficient. 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().

Further non-documented arguments are:

  • digits, p_digits, ci_digits and footer_digits to set the number of digits for the output. groups can be used to group coefficients. These arguments will be passed to the print-method, or can directly be used in print(), see documentation in print.parameters_model().

  • If s_value = TRUE, the p-value will be replaced by the S-value in the output (cf. Rafi and Greenland 2020).

  • pd adds an additional column with the probability of direction (see bayestestR::p_direction() for details). Furthermore, see 'Examples' for this function.

  • For developers, whose interest mainly is to get a "tidy" data frame of model summaries, it is recommended to set pretty_names = FALSE to speed up computation of the summary table.

es_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.

Value

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

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 1.9-1. 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(384.53) |      p
#> --------------------------------------------------------------------
#> (Intercept) |        8.05 | 0.10 | [7.84, 8.25] |     78.00 | < .001
#> 
#> # Smooth Terms 
#> 
#> Parameter        |      F |   df |      p
#> -----------------------------------------
#> Smooth term (x0) |  11.51 | 3.50 | < .001
#> Smooth term (x1) | 102.91 | 2.25 | < .001
#> Smooth term (x2) |  77.72 | 7.72 | < .001
#> Smooth term (x3) |   0.08 | 1.00 | 0.776