Glance version of the nls object into a flextable object

Extract the information contained in an nls object in a table as it could be obtained by broom::glance(). Here, the table is nicely formatted as an (almost) publication-ready form (good for informal reports, notebooks, etc).

# S3 method for class 'nls'
tabularise_glance(
  data,
  header = TRUE,
  title = header,
  equation = header,
  auto.labs = TRUE,
  origdata = NULL,
  labs = NULL,
  lang = getOption("SciViews_lang", "en"),
  ...,
  kind = "ft"
)

Arguments

data: An nls object.
header: If TRUE (by default), add a title to the table.
title: If TRUE, 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: Add equation of the model to the table. If TRUE, equation() is used. The equation can also be passed in the form of a character string (LaTeX).
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 (only the name of the variables).
labs: Labels to change the names of elements in the term column of the table. By default it is NULL and nothing is changed.
lang: The language to use. The default value can be set with, e.g., options(SciViews_lang = "fr") for French.
...: Not used
kind: The kind of table to produce: "tt" for tinytable, or "ft" for flextable (default).

Value

A flextable object that you can print in different forms or rearrange with the {flextable} functions.

Examples

data("ChickWeight", package = "datasets")
chick1 <- ChickWeight[ChickWeight$Chick == 1, ]

# Adjust a logistic curve
chick1_logis <- nls(data = chick1, weight ~ SSlogis(Time, Asym, xmid, scal))

tabularise::tabularise$glance(chick1_logis)
Nonlinear least squares  logistic model
 $\begin{aligned} \operatorname{weight} = \frac{Asym}{1 + e^{(xmid - \operatorname{Time}) /scal}} + \epsilon \end{aligned}$ 
Relative standard error Convergence tolerance Log-Likelihood AIC BIC Deviance df N
2.92 7.34·10-06 -28.2 64.3 66.2 76.7 9 12
tabularise::tabularise$glance(chick1_logis, lang = "fr")
Nonlinear least squares  logistic model
 $\begin{aligned} \operatorname{weight} = \frac{Asym}{1 + e^{(xmid - \operatorname{Time}) /scal}} + \epsilon \end{aligned}$ 
Relative standard error Convergence tolerance Log-Likelihood AIC BIC Deviance df N
2.92 7.34·10-06 -28.2 64.3 66.2 76.7 9 12

Nonlinear least squares logistic model
$\begin{aligned} \operatorname{weight} = \frac{Asym}{1 + e^{(xmid - \operatorname{Time}) /scal}} + \epsilon \end{aligned}$
Relative standard error	Convergence tolerance	Log-Likelihood	AIC	BIC	Deviance	df	N
2.92	7.34·10-06	-28.2	64.3	66.2	76.7	9	12

Arguments

Value

See also

Examples