R/tabularise.lm.R
tabularise_glance.lm.Rd
Create a rich-formatted table with the 'glance' information from an lm object.
# S3 method for class 'lm'
tabularise_glance(
data,
header = TRUE,
title = header,
equation = header,
auto.labs = TRUE,
origdata = NULL,
labs = NULL,
lang = getOption("SciViews_lang", "en"),
...,
kind = "ft"
)
An lm object
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.
If 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).
Logical or character. Controls whether an equation is added to the table header and how parameters are used. Accepted values are:
TRUE
(by 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.
If TRUE
(by default), use labels (and units) automatically
from data or origdata=
.
The original data set this model was fitted to. By default it
is NULL
and no label is used (only the name of the variables).
Labels to change the names of elements in the term
column of
the table. By default it is NULL
and nothing is changed.
The natural language to use. The default value can be set with,
e.g., options(SciViews_lang = "fr")
for French.
Additional arguments passed to equatiomatic::equation()
The kind of table to produce: "tt" for tinytable, or "ft" for flextable (default).
A flextable object that you can print in different form or rearrange with the {flextable} functions.
iris_lm <- lm(data = iris, Petal.Length ~ Sepal.Length)
tabularise::tabularise$glance(iris_lm)
Linear model
R2
Adj.R2
RSE
t value
p value
Model df
Log-likelihood
AIC
BIC
Deviance
Residuals df
N
0.76
0.758
0.868
469
< 2·10-16
1
-191
387
396
111
148
150