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Printing modelbased-objects

Source: R/print.R
print.estimate_contrasts.Rd

print() method for modelbased objects. Can be used to tweak the output of tables.

Usage

# S3 method for class 'estimate_contrasts'
print(
  x,
  select = getOption("modelbased_select", NULL),
  include_grid = getOption("modelbased_include_grid", FALSE),
  full_labels = getOption("modelbased_full_labels", TRUE),
  ...
)

Arguments

x

An object returned by the different estimate_*() functions.

select

Determines which columns are printed and the table layout. There are two options for this argument:

  • A string expression with layout pattern

    select is a string with "tokens" enclosed in braces. These tokens will be replaced by their associated columns, where the selected columns will be collapsed into one column. Following tokens are replaced by the related coefficients or statistics: {estimate}, {se}, {ci} (or {ci_low} and {ci_high}), {p}, {pd} and {stars}. The token {ci} will be replaced by {ci_low}, {ci_high}. Example: select = "{estimate}{stars} ({ci})"

    It is possible to create multiple columns as well. A | separates values into new cells/columns. Example: select = "{estimate} ({ci})|{p}".

  • A string indicating a pre-defined layout

    select can be one of the following string values, to create one of the following pre-defined column layouts:

    • "minimal": Estimates, confidence intervals and numeric p-values, in two columns. This is equivalent to select = "{estimate} ({ci})|{p}".

    • "short": Estimate, standard errors and numeric p-values, in two columns. This is equivalent to select = "{estimate} ({se})|{p}".

    • "ci": Estimates and confidence intervals, no asterisks for p-values. This is equivalent to select = "{estimate} ({ci})".

    • "se": Estimates and standard errors, no asterisks for p-values. This is equivalent to select = "{estimate} ({se})".

    • "ci_p": Estimates, confidence intervals and asterisks for p-values. This is equivalent to select = "{estimate}{stars} ({ci})".

    • "se_p": Estimates, standard errors and asterisks for p-values. This is equivalent to select = "{estimate}{stars} ({se})"..

Using select to define columns will re-order columns and remove all columns related to uncertainty (standard errors, confidence intervals), test statistics, and p-values (and similar, like pd or BF for Bayesian models), because these are assumed to be included or intentionally excluded when using select. The new column order will be: Parameter columns first, followed by the "glue" columns, followed by all remaining columns. If further columns should also be placed first, add those as focal_terms attributes to x. I.e., following columns are considers as "parameter columns" and placed first: c(easystats_columns("parameter"), attributes(x)$focal_terms).

Note: glue-like syntax is still experimental in the case of more complex models (like mixed models) and may not return expected results.

include_grid

Logical, if TRUE, the data grid is included in the table output. Only applies to prediction-functions like estimate_relation() or estimate_link().

full_labels

Logical, if TRUE (default), all labels for focal terms are shown. If FALSE, redundant (duplicated) labels are removed from rows.

...

Arguments passed to insight::format_table() or insight::export_table().

Value

Invisibly returns x.

Note

Use print_html() and print_md() to create tables in HTML or markdown format, respectively.

Global Options to Customize Tables when Printing

Columns and table layout can be customized using options():

  • modelbased_select: options(modelbased_select = <string>) will set a default value for the select argument and can be used to define a custom default layout for printing.

  • modelbased_include_grid: options(modelbased_include_grid = TRUE) will set a default value for the include_grid argument and can be used to include data grids in the output by default or not.

  • modelbased_full_labels: options(modelbased_full_labels = FALSE) will remove redundant (duplicated) labels from rows.

Examples

model <- lm(Petal.Length ~ Species, data = iris)
out <- estimate_means(model, "Species")

# default
print(out)
#> Estimated Marginal Means
#> 
#> Species    | Mean |   SE |       95% CI | t(147)
#> ------------------------------------------------
#> setosa     | 1.46 | 0.06 | [1.34, 1.58] |  24.02
#> versicolor | 4.26 | 0.06 | [4.14, 4.38] |  70.00
#> virginica  | 5.55 | 0.06 | [5.43, 5.67] |  91.23
#> 
#> Variable predicted: Petal.Length
#> Predictors modulated: Species
#> 

# smaller set of columns
print(out, select = "minimal")
#> Estimated Marginal Means
#> 
#> Species    |         Mean (CI)
#> ------------------------------
#> setosa     | 1.46 (1.34, 1.58)
#> versicolor | 4.26 (4.14, 4.38)
#> virginica  | 5.55 (5.43, 5.67)
#> 
#> Variable predicted: Petal.Length
#> Predictors modulated: Species
#> 

# remove redundant labels
data(efc, package = "modelbased")
efc <- datawizard::to_factor(efc, c("c161sex", "c172code", "e16sex"))
levels(efc$c172code) <- c("low", "mid", "high")
fit <- lm(neg_c_7 ~ c161sex * c172code * e16sex, data = efc)
out <- estimate_means(fit, c("c161sex", "c172code", "e16sex"))
print(out, full_labels = FALSE, select = "{estimate} ({se})")
#> Estimated Marginal Means
#> 
#> c161sex | c172code | e16sex |    Mean (SE)
#> ------------------------------------------
#> Male    | low      | male   |  9.47 (1.00)
#> Female  |          |        | 12.13 (0.48)
#> Male    | mid      |        | 12.16 (0.68)
#> Female  |          |        | 12.48 (0.35)
#> Male    | high     |        | 12.31 (1.07)
#> Female  |          |        | 12.37 (0.71)
#> Male    | low      | female | 11.92 (0.76)
#> Female  |          |        | 12.11 (0.46)
#> Male    | mid      |        | 10.93 (0.43)
#> Female  |          |        | 11.57 (0.24)
#> Male    | high     |        | 11.42 (0.67)
#> Female  |          |        | 12.74 (0.44)
#> 
#> Variable predicted: neg_c_7
#> Predictors modulated: c161sex, c172code, e16sex
#>