parameters (version 0.19.0)

print.parameters_model: Print model parameters

Description

A print()-method for objects from model_parameters().

Usage

# S3 method for parameters_model
print(
  x,
  pretty_names = TRUE,
  split_components = TRUE,
  select = NULL,
  caption = NULL,
  footer = NULL,
  digits = 2,
  ci_digits = 2,
  p_digits = 3,
  footer_digits = 3,
  show_sigma = FALSE,
  show_formula = FALSE,
  zap_small = FALSE,
  groups = NULL,
  column_width = NULL,
  ci_brackets = c("[", "]"),
  ...
)
# S3 method for parameters_model
summary(object, ...)

Value

Invisibly returns the original input object.

Arguments

x, object

An object returned by model_parameters().

pretty_names

Can be TRUE, which will return "pretty" (i.e. more human readable) parameter names. Or "labels", in which case value and variable labels will be used as parameters names. The latter only works for "labelled" data, i.e. if the data used to fit the model had "label" and "labels" attributes. See also section Global Options to Customize Messages when Printing.

split_components

Logical, if TRUE (default), For models with multiple components (zero-inflation, smooth terms, ...), each component is printed in a separate table. If FALSE, model parameters are printed in a single table and a Component column is added to the output.

select

Character vector (or numeric index) of column names that should be printed. If NULL (default), all columns are printed. The shortcut select = "minimal" prints coefficient, confidence intervals and p-values, while select = "short" prints coefficient, standard errors and p-values.

caption

Table caption as string. If NULL, depending on the model, either a default caption or no table caption is printed. Use caption = "" to suppress the table caption.

footer

Can either be FALSE or an empty string (i.e. "") to suppress the footer, NULL to print the default footer, or a string. The latter will combine the string value with the default footer.

digits, ci_digits, p_digits

Number of digits for rounding or significant figures. May also be "signif" to return significant figures or "scientific" to return scientific notation. Control the number of digits by adding the value as suffix, e.g. digits = "scientific4" to have scientific notation with 4 decimal places, or digits = "signif5" for 5 significant figures (see also signif()).

footer_digits

Number of decimal places for values in the footer summary.

show_sigma

Logical, if TRUE, adds information about the residual standard deviation.

show_formula

Logical, if TRUE, adds the model formula to the output.

zap_small

Logical, if TRUE, small values are rounded after digits decimal places. If FALSE, values with more decimal places than digits are printed in scientific notation.

groups

Named list, can be used to group parameters in the printed output. List elements may either be character vectors that match the name of those parameters that belong to one group, or list elements can be row numbers of those parameter rows that should belong to one group. The names of the list elements will be used as group names, which will be inserted as "header row". A possible use case might be to emphasize focal predictors and control variables, see 'Examples'. Parameters will be re-ordered according to the order used in groups, while all non-matching parameters will be added to the end.

column_width

Width of table columns. Can be either NULL, a named numeric vector, or "fixed". If NULL, the width for each table column is adjusted to the minimum required width. If a named numeric vector, value names are matched against column names, and for each match, the specified width is used. If "fixed", and table is split into multiple components, columns across all table components are adjusted to have the same width.

ci_brackets

Logical, if TRUE (default), CI-values are encompassed in square brackets (else in parentheses).

...

Arguments passed to or from other methods.

Global Options to Customize Messages when Printing

The verbose argument can be used to display or silence messages and warnings for the different functions in the parameters package. However, some messages providing additional information can be displayed or suppressed using options():

• parameters_summary: options(parameters_summary = TRUE) will override the summary argument in model_parameters() and always show the model summary for non-mixed models.

• parameters_mixed_summary: options(parameters_mixed_summary = TRUE) will override the summary argument in model_parameters() for mixed models, and will then always show the model summary.

• parameters_cimethod: options(parameters_cimethod = TRUE) will show the additional information about the approximation method used to calculate confidence intervals and p-values. Set to FALSE to hide this message when printing model_parameters() objects.

• parameters_exponentiate: options(parameters_exponentiate = TRUE) will show the additional information on how to interpret coefficients of models with log-transformed response variables or with log-/logit-links when the exponentiate argument in model_parameters() is not TRUE. Set this option to FALSE to hide this message when printing model_parameters() objects.

• parameters_labels: options(parameters_labels = TRUE) will use variable and value labels for pretty names, if data is labelled. If no labels available, default pretty names are used.

• parameters_select: options(parameters_select = <value>) will set the default for the select argument. See argument's documentation for available options.

Interpretation of Interaction Terms

Note that the interpretation of interaction terms depends on many characteristics of the model. The number of parameters, and overall performance of the model, can differ or not between a * b a : b, and a / b, suggesting that sometimes interaction terms give different parameterizations of the same model, but other times it gives completely different models (depending on a or b being factors of covariates, included as main effects or not, etc.). Their interpretation depends of the full context of the model, which should not be inferred from the parameters table alone - rather, we recommend to use packages that calculate estimated marginal means or marginal effects, such as modelbased, emmeans, ggeffects, or marginaleffects. To raise awareness for this issue, you may use print(...,show_formula=TRUE) to add the model-specification to the output of the print() method for model_parameters().

Labeling the Degrees of Freedom

Throughout the parameters package, we decided to label the residual degrees of freedom df_error. The reason for this is that these degrees of freedom not always refer to the residuals. For certain models, they refer to the estimate error - in a linear model these are the same, but in - for instance - any mixed effects model, this isn't strictly true. Hence, we think that df_error is the most generic label for these degrees of freedom.

Details

summary() is a convenient shortcut for print(object, select = "minimal", show_sigma = TRUE, show_formula = TRUE).

See Also

There is a dedicated method to use inside rmarkdown files, print_md().

Examples

# \donttest{
library(parameters)
if (require("glmmTMB", quietly = TRUE)) {
  model <- glmmTMB(
    count ~ spp + mined + (1 | site),
    ziformula = ~mined,
    family = poisson(),
    data = Salamanders
  )
  mp <- model_parameters(model)

  print(mp, pretty_names = FALSE)

  print(mp, split_components = FALSE)

  print(mp, select = c("Parameter", "Coefficient", "SE"))

  print(mp, select = "minimal")
}


# group parameters ------

data(iris)
model <- lm(
  Sepal.Width ~ Sepal.Length + Species + Petal.Length,
  data = iris
)
# don't select "Intercept" parameter
mp <- model_parameters(model, parameters = "^(?!\\(Intercept)")
groups <- list(
  "Focal Predictors" = c("Speciesversicolor", "Speciesvirginica"),
  "Controls" = c("Sepal.Length", "Petal.Length")
)
print(mp, groups = groups)

# or use row indices
print(mp, groups = list(
  "Focal Predictors" = c(1, 4),
  "Controls" = c(2, 3)
))

# only show coefficients, CI and p,
# put non-matched parameters to the end

data(mtcars)
mtcars$cyl <- as.factor(mtcars$cyl)
mtcars$gear <- as.factor(mtcars$gear)
model <- lm(mpg ~ hp + gear * vs + cyl + drat, data = mtcars)

# don't select "Intercept" parameter
mp <- model_parameters(model, parameters = "^(?!\\(Intercept)")
print(mp, groups = list(
  "Engine" = c("cyl6", "cyl8", "vs", "hp"),
  "Interactions" = c("gear4:vs", "gear5:vs")
))
# }