`tab_model()`

creates HTML tables from regression models.

```
tab_model(
...,
transform,
show.intercept = TRUE,
show.est = TRUE,
show.ci = 0.95,
show.ci50 = FALSE,
show.se = NULL,
show.std = NULL,
show.p = TRUE,
show.stat = FALSE,
show.df = FALSE,
show.zeroinf = TRUE,
show.r2 = TRUE,
show.icc = TRUE,
show.re.var = TRUE,
show.ngroups = TRUE,
show.fstat = FALSE,
show.aic = FALSE,
show.aicc = FALSE,
show.dev = FALSE,
show.loglik = FALSE,
show.obs = TRUE,
show.reflvl = FALSE,
terms = NULL,
rm.terms = NULL,
order.terms = NULL,
title = NULL,
pred.labels = NULL,
dv.labels = NULL,
wrap.labels = 25,
bootstrap = FALSE,
iterations = 1000,
seed = NULL,
robust = FALSE,
vcov.fun = NULL,
vcov.type = c("HC3", "const", "HC", "HC0", "HC1", "HC2", "HC4", "HC4m", "HC5", "CR0",
"CR1", "CR1p", "CR1S", "CR2", "CR3"),
vcov.args = NULL,
string.pred = "Predictors",
string.est = "Estimate",
string.std = "std. Beta",
string.ci = "CI",
string.se = "std. Error",
string.std_se = "standardized std. Error",
string.std_ci = "standardized CI",
string.p = "p",
string.df = "df",
string.stat = "Statistic",
string.resp = "Response",
string.intercept = "(Intercept)",
strings = NULL,
ci.hyphen = " – ",
minus.sign = "-",
collapse.ci = FALSE,
collapse.se = FALSE,
linebreak = TRUE,
col.order = c("est", "se", "std.est", "std.se", "ci", "std.ci", "ci.inner",
"ci.outer", "stat", "p", "df.error", "response.level"),
digits = 2,
digits.p = 3,
digits.re = 2,
emph.p = TRUE,
p.val = c("wald", "kenward", "kr", "satterthwaite", "ml1", "betwithin"),
p.style = c("numeric", "stars", "numeric_stars", "scientific", "scientific_stars"),
p.threshold = c(0.05, 0.01, 0.001),
p.adjust = NULL,
case = "parsed",
auto.label = TRUE,
prefix.labels = c("none", "varname", "label"),
bpe = "median",
CSS = css_theme("regression"),
file = NULL,
use.viewer = TRUE
)
```

...

One or more regression models, including glm's or mixed models.
May also be a `list`

with fitted models. See 'Examples'.

transform

A character vector, naming a function that will be applied
on estimates and confidence intervals. By default, `transform`

will
automatically use `"exp"`

as transformation for applicable classes of
`model`

(e.g. logistic or poisson regression). Estimates of linear
models remain untransformed. Use `NULL`

if you want the raw,
non-transformed estimates.

show.intercept

Logical, if `TRUE`

, the intercepts are printed.

show.est

Logical, if `TRUE`

, the estimates are printed.

show.ci

Either logical, and if `TRUE`

, the confidence intervals
is printed to the table; if `FALSE`

, confidence intervals are
omitted. Or numeric, between 0 and 1, indicating the range of the
confidence intervals.

show.ci50

Logical, if `TRUE`

, for Bayesian models, a second
credible interval is added to the table output.

show.se

Logical, if `TRUE`

, the standard errors are
also printed. If robust standard errors are required, use arguments
`vcov.fun`

, `vcov.type`

and `vcov.args`

(see
`standard_error_robust`

and
this vignette
for details).

show.std

Indicates whether standardized beta-coefficients should also printed, and if yes, which type of standardization is done. See 'Details'.

show.p

Logical, if `TRUE`

, p-values are also printed.

show.stat

Logical, if `TRUE`

, the coefficients' test statistic
is also printed.

show.df

Logical, if `TRUE`

and `p.val = "kr"`

, the p-values
for linear mixed models are based on df with Kenward-Rogers approximation.
These df-values are printed. See `p_value`

for details.

show.zeroinf

Logical, if `TRUE`

and model has a zero-inflated
model part, this is also printed to the table.

show.r2

Logical, if `TRUE`

, the r-squared value is also printed.
Depending on the model, these might be pseudo-r-squared values, or Bayesian
r-squared etc. See `r2`

for details.

show.icc

Logical, if `TRUE`

, prints the intraclass correlation
coefficient for mixed models. See `icc`

for details.

show.re.var

Logical, if `TRUE`

, prints the random effect variances
for mixed models. See `get_variance`

for details.

show.ngroups

Logical, if `TRUE`

, shows number of random effects groups
for mixed models.

show.fstat

Logical, if `TRUE`

, the F-statistics for each model is
printed in the table summary. This option is not supported by all model types.

show.aic

Logical, if `TRUE`

, the AIC value for each model is printed
in the table summary.

show.aicc

Logical, if `TRUE`

, the second-order AIC value for each model
is printed in the table summary.

show.dev

Logical, if `TRUE`

, shows the deviance of the model.

show.loglik

Logical, if `TRUE`

, shows the log-Likelihood of the model.

show.obs

Logical, if `TRUE`

, the number of observations per model is
printed in the table summary.

show.reflvl

Logical, if `TRUE`

, an additional row is inserted to
the table before each predictor of type `factor`

, which will
indicate the reference level of the related factor.

terms

Character vector with names of those terms (variables) that should
be printed in the table. All other terms are removed from the output. If
`NULL`

, all terms are printed. Note that the term names must match
the names of the model's coefficients. For factors, this means that
the variable name is suffixed with the related factor level, and each
category counts as one term. E.g. `rm.terms = "t_name [2,3]"`

would remove the terms `"t_name2"`

and `"t_name3"`

(assuming
that the variable `t_name`

is categorical and has at least
the factor levels `2`

and `3`

). Another example for the
*iris*-dataset: `terms = "Species"`

would not work, instead
use `terms = "Species [versicolor,virginica]"`

.

rm.terms

Character vector with names that indicate which terms should
be removed from the output Counterpart to `terms`

. ```
rm.terms =
"t_name"
```

would remove the term *t_name*. Default is `NULL`

, i.e.
all terms are used. For factors, levels that should be removed from the plot
need to be explicitly indicated in square brackets, and match the model's
coefficient names, e.g. `rm.terms = "t_name [2,3]"`

would remove the terms
`"t_name2"`

and `"t_name3"`

(assuming that the variable `t_name`

was categorical and has at least the factor levels `2`

and `3`

).

order.terms

Numeric vector, indicating in which order the coefficients should be plotted. See examples in this package-vignette.

title

String, will be used as table caption.

pred.labels

Character vector with labels of predictor variables.
If not `NULL`

, `pred.labels`

will be used in the first
table column with the predictors' names. By default, if `auto.label = TRUE`

and data is labelled,
`term_labels`

is called to retrieve the labels
of the coefficients, which will be used as predictor labels. If data is
not labelled, format_parameters()
is used to create pretty labels. If `pred.labels = ""`

or `auto.label = FALSE`

, the raw
variable names as used in the model formula are used as predictor
labels. If `pred.labels`

is a named vector, predictor labels (by
default, the names of the model's coefficients) will be matched with the
names of `pred.labels`

. This ensures that labels always match the
related predictor in the table, no matter in which way the predictors
are sorted. See 'Examples'.

dv.labels

Character vector with labels of dependent variables of all fitted models. See 'Examples'.

wrap.labels

Numeric, determines how many chars of the value, variable or axis labels are displayed in one line and when a line break is inserted.

bootstrap

Logical, if `TRUE`

, returns bootstrapped estimates..

iterations

Numeric, number of bootstrap iterations (default is 1000).

seed

Numeric, the number of the seed to replicate bootstrapped estimates. If `NULL`

, uses random seed.

robust

Logical, shortcut for arguments `vcov.fun`

and `vcov.type`

.
If `TRUE`

, uses `vcov.fun = "vcovHC"`

and `vcov.type = "HC3"`

as
default, that is, `vcovHC`

with default-type is called
(see `standard_error_robust`

and
this vignette
for further details).

vcov.fun

Character vector, indicating the name of the `vcov*()`

-function
from the sandwich or clubSandwich package, e.g. `vcov.fun = "vcovCL"`

,
if robust standard errors are required.

vcov.type

vcov.args

List of named vectors, used as additional arguments that
are passed down to `vcov.fun`

.

string.pred

Character vector,used as headline for the predictor column.
Default is `"Predictors"`

.

string.est

Character vector, used for the column heading of coefficients.
Default is based on the response scale, e.g. for logistic regression models,
`"Odds Ratios"`

will be chosen, while for Poisson models it is
`"Incidence Rate Ratios"`

etc. Default if not specified is `"Estimate"`

.

string.std

Character vector, used for the column heading of standardized beta coefficients. Default is `"std. Beta"`

.

string.ci

Character vector, used for the column heading of confidence interval values. Default is `"CI"`

.

string.se

Character vector, used for the column heading of standard error values. Default is `"std. Error"`

.

string.std_se

Character vector, used for the column heading of standard error of standardized coefficients. Default is `"standardized std. Error"`

.

string.std_ci

Character vector, used for the column heading of confidence intervals of standardized coefficients. Default is `"standardized std. Error"`

.

string.p

Character vector, used for the column heading of p values. Default is `"p"`

.

string.df

Character vector, used for the column heading of degrees of freedom. Default is `"df"`

.

string.stat

Character vector, used for the test statistic. Default is `"Statistic"`

.

string.resp

Character vector, used for the column heading of of the response level for multinominal or categorical models. Default is `"Response"`

.

string.intercept

Character vector, used as name for the intercept parameter. Default is `"(Intercept)"`

.

strings

Named character vector, as alternative to arguments like `string.ci`

or `string.p`

etc. The name (lhs) must be one of the string-indicator from
the aforementioned arguments, while the value (rhs) is the string that is used
as column heading. E.g., `strings = c(ci = "Conf.Int.", se = "std. Err")`

would be equivalent to setting `string.ci = "Conf.Int.", string.se = "std. Err"`

.

ci.hyphen

Character vector, indicating the hyphen for confidence interval range. May be an HTML entity. See 'Examples'.

minus.sign

string, indicating the minus sign for negative numbers. May be an HTML entity. See 'Examples'.

collapse.ci

Logical, if `FALSE`

, the CI values are shown in
a separate table column.

collapse.se

Logical, if `FALSE`

, the SE values are shown in
a separate table column.

linebreak

Logical, if `TRUE`

and `collapse.ci = FALSE`

or
`collapse.se = FALSE`

, inserts a line break between estimate and
CI resp. SE values. If `FALSE`

, values are printed in the same line
as estimate values.

col.order

Character vector, indicating which columns should be printed
and in which order. Column names that are excluded from `col.order`

are not shown in the table output. However, column names that are included,
are only shown in the table when the related argument (like `show.est`

for `"estimate"`

) is set to `TRUE`

or another valid value.
Table columns are printed in the order as they appear in `col.order`

.

digits

Amount of decimals for estimates

digits.p

Amount of decimals for p-values

digits.re

Amount of decimals for random effects part of the summary table.

emph.p

Logical, if `TRUE`

, significant p-values are shown bold faced.

p.val

Character, for mixed models, indicates how p-values are computed.
Use `p.val = "wald"`

for a faster, but less precise computation. For
`p.val = "kenward"`

(or `p.val = "kr"`

), computation of p-values
is based on conditional F-tests with Kenward-Roger approximation for the
degrees of freedom. `p.val = "satterthwaite"`

uses Satterthwaite's
approximation and `"ml1"`

uses a "m-l-1" heuristic (see
`degrees_of_freedom`

for details). Use
`show.df = TRUE`

to show the approximated degrees of freedom
for each coefficient.

p.style

Character, indicating if p-values should be printed as
numeric value (`"numeric"`

), as 'stars' (asterisks) only (`"stars"`

),
or scientific (`"scientific"`

). Scientific and numeric style can be
combined with "stars", e.g. `"numeric_stars"`

p.threshold

Numeric vector of length 3, indicating the treshold for
annotating p-values with asterisks. Only applies if
`p.style = "asterisk"`

.

p.adjust

Character vector, if not `NULL`

, indicates the method
to adjust p-values. See `p.adjust`

for details.

case

Desired target case. Labels will automatically converted into the
specified character case. See `to_any_case`

for more
details on this argument. By default, if `case`

is not specified,
it will be set to `"parsed"`

, unless `prefix.labels`

is not
`"none"`

. If `prefix.labels`

is either `"label"`

(or
`"l"`

) or `"varname"`

(or `"v"`

) and `case`

is not
specified, it will be set to `NULL`

- this is a more convenient
default when prefixing labels.

auto.label

Logical, if `TRUE`

(the default),
and data is labelled,
`term_labels`

is called to retrieve the labels
of the coefficients, which will be used as predictor labels. If data is
not labelled, format_parameters()
is used to create pretty labels. If `auto.label = FALSE`

,
original variable names and value labels (factor levels) are used.

prefix.labels

Indicates whether the value labels of categorical variables
should be prefixed, e.g. with the variable name or variable label. See
argument `prefix`

in `term_labels`

for
details.

bpe

For **Stan**-models (fitted with the rstanarm- or
brms-package), the Bayesian point estimate is, by default, the median
of the posterior distribution. Use `bpe`

to define other functions to
calculate the Bayesian point estimate. `bpe`

needs to be a character
naming the specific function, which is passed to the `fun`

-argument in
`typical_value`

. So, `bpe = "mean"`

would
calculate the mean value of the posterior distribution.

CSS

A `list`

with user-defined style-sheet-definitions,
according to the official CSS syntax.
See 'Details' or this package-vignette.

file

Destination file, if the output should be saved as file.
If `NULL`

(default), the output will be saved as temporary file and
openend either in the IDE's viewer pane or the default web browser.

use.viewer

Logical, if `TRUE`

, the HTML table is shown in the IDE's
viewer pane. If `FALSE`

or no viewer available, the HTML table is
opened in a web browser.

Invisibly returns

the web page style sheet (

`page.style`

),the web page content (

`page.content`

),the complete html-output (

`page.complete`

) andthe html-table with inline-css for use with knitr (

`knitr`

)

for further use.

Default standardization is done by completely refitting the model on the
standardized data. Hence, this approach is equal to standardizing the
variables before fitting the model, which is particularly recommended for
complex models that include interactions or transformations (e.g., polynomial
or spline terms). When `show.std = "std2"`

, standardization of estimates
follows Gelman's (2008)
suggestion, rescaling the estimates by dividing them by two standard deviations
instead of just one. Resulting coefficients are then directly comparable for
untransformed binary predictors. For backward compatibility reasons,
`show.std`

also may be a logical value; if `TRUE`

, normal standardized
estimates are printed (same effect as `show.std = "std"`

). Use
`show.std = NULL`

(default) or `show.std = FALSE`

, if no standardization
is required.

`CSS`

-argument?With the `CSS`

-argument, the visual appearance of the tables
can be modified. To get an overview of all style-sheet-classnames
that are used in this function, see return value `page.style`

for details.
Arguments for this list have following syntax:

the class-names with

`"css."`

-prefix as argument name andeach style-definition must end with a semicolon

`css.table = 'border:2px solid red;'`

for a solid 2-pixel table border in red.`css.summary = 'font-weight:bold;'`

for a bold fontweight in the summary row.`css.lasttablerow = 'border-bottom: 1px dotted blue;'`

for a blue dotted border of the last table row.`css.colnames = '+color:green'`

to add green color formatting to column names.`css.arc = 'color:blue;'`

for a blue text color each 2nd row.`css.caption = '+color:red;'`

to add red font-color to the default table caption style.