Create a tidy version of the glm object as a rich-formatted table

Turn the tidy of glm object into a rich-formatted table with {flextable}. The table can be printed in different formats (HTML, LaTeX, Word, PowerPoint), or rearranged later on.

# S3 method for class 'glm'
tabularise_tidy(
  data,
  header = TRUE,
  title = NULL,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  lang = getOption("SciViews_lang", default = Sys.getenv("LANGUAGE", unset = "en")),
  show.signif.stars = getOption("show.signif.stars", TRUE),
  ...,
  kind = "ft",
  env = parent.frame()
)

Arguments

data

A glm object

header

Logical. If TRUE (TRUE by default), a header is added to the table. The header includes both the title and the equation (if applicable). If set to FALSE, neither the title nor the equation will be displayed in the table header, even if the title or equation parameters are provided.

title

If TRUE (TRUE by default), add a title to the table header. Default to the same value than header, except outside of a chunk where it is FALSE if a table caption is detected (tbl-cap YAML entry).

equation

Logical or character. Controls whether an equation is added to the table header and how parameters are used. Accepted values are:

TRUE (default) : The equation is generated and added to the table header. Its parameters are also used in the "Term" column.
FALSE: No equation is generated or displayed, and its parameters are not used in the "Term" column.
NA: The equation is generated but not displayed in the table header. Its parameters are used in the "Term" column.
Character string: A custom equation is provided directly and added to the table header.

auto.labs

If TRUE (by default), use labels (and units) automatically from data or origdata=.

origdata

The original data set this model was fitted to. By default it is NULL and no label is used.

labs

Labels to change the names of elements in the term column of the table. By default it is NULL and nothing is changed.

conf.int

If TRUE, add the confidence interval. The default is FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. The default is 0.95.

lang

The natural language to use. The default value can be set with, e.g., options(SciViews_lang = "fr") for French.

show.signif.stars

If TRUE, add the significance stars to the table. The default is getOption("show.signif.stars")

...

Additional arguments

kind

The kind of table to produce: "tt" for tinytable, or "ft" for flextable (default).

env

The environment where to evaluate formulas (you probably do not need to change the default).

Value

A flextable object is returned. You can print it in different formats (HTML, LaTeX, Word, PowerPoint), or rearrange it with the {flextable} functions.

Examples

#' # If the 'iris' dataset has labels and units, they can be used to enhance
# the output table
iris <- svBase::labelise(iris, self = FALSE, label = list(
    Sepal.Length = "Length of the sepals",
    Petal.Length = "Length of the petals",
    Species = "Species"), units = c(rep("cm", 4), NA))
iris_glm <- glm(data = iris, Petal.Length ~ Sepal.Length)

tabularise::tabularise$tidy(iris_glm)
Generalized Linear Model
 $E( \operatorname{Length of the petals [cm]} ) = \alpha + \beta_{}(\operatorname{Length\ of\ the\ sepals\ \ [cm]})$ 
Term Estimate Standard Error t value p value 
 $\alpha$ -7.10 0.5067 -14.0 < 2·10-16 ***
 $\beta_{}$  1.86 0.0859  21.6 < 2·10-16 ***
0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05
tabularise::tabularise$tidy(iris_glm, conf.int = TRUE)
Generalized Linear Model
 $E( \operatorname{Length of the petals [cm]} ) = \alpha + \beta_{}(\operatorname{Length\ of\ the\ sepals\ \ [cm]})$ 
Term Estimate Lower bound (CI) Upper bound (CI) Standard Error t value p value 
 $\alpha$ -7.10 -8.09 -6.11 0.5067 -14.0 < 2·10-16 ***
 $\beta_{}$  1.86  1.69  2.03 0.0859  21.6 < 2·10-16 ***
0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05
tabularise::tabularise$tidy(iris_glm, conf.int = TRUE, equation = NA)
Generalized Linear Model
Term Estimate Lower bound (CI) Upper bound (CI) Standard Error t value p value 
 $\alpha$ -7.10 -8.09 -6.11 0.5067 -14.0 < 2·10-16 ***
 $\beta_{}$  1.86  1.69  2.03 0.0859  21.6 < 2·10-16 ***
0 <= '***' < 0.001 < '**' < 0.01 < '*' < 0.05

Generalized Linear Model
$E( \operatorname{Length of the petals [cm]} ) = \alpha + \beta_{}(\operatorname{Length\ of\ the\ sepals\ \ [cm]})$
Term	Estimate	Standard Error	t value	p value
$\alpha$	-7.10	0.5067	-14.0	< 2·10-16	***
$\beta_{}$	1.86	0.0859	21.6	< 2·10-16	***
0 <= '*' < 0.001 < '' < 0.01 < '*' < 0.05

Generalized Linear Model
Term	Estimate	Lower bound (CI)	Upper bound (CI)	Standard Error	t value	p value
$\alpha$	-7.10	-8.09	-6.11	0.5067	-14.0	< 2·10-16	***
$\beta_{}$	1.86	1.69	2.03	0.0859	21.6	< 2·10-16	***
0 <= '*' < 0.001 < '' < 0.01 < '*' < 0.05