`tab_model()`

creates HTML tables from regression models.

## Usage

```
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,
keep = NULL,
drop = 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 = NULL,
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.std.p = "std. p",
string.df = "df",
string.stat = "Statistic",
string.std.stat = "std. 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", "std.stat", "p", "std.p", "df.error", "response.level"),
digits = 2,
digits.p = 3,
digits.rsq = 3,
digits.re = 2,
emph.p = TRUE,
p.val = NULL,
df.method = NULL,
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,
encoding = "UTF-8"
)
```

## Arguments

- ...
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`

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.

- keep, drop
Character containing a regular expression pattern that describes the parameters that should be included (for

`keep`

) or excluded (for`drop`

) in the returned data frame.`keep`

may also be a named list of regular expressions. All non-matching parameters will be removed from the output. If`keep`

has more than one element, these will be merged with an`OR`

operator into a regular expression pattern like this:`"(one|two|three)"`

. See further details in`?parameters::model_parameters`

.- 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. If

`dv.labels = ""`

, the row with names of dependent variables is omitted from the table.- 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
Deprecated. Please use

`vcov.fun`

directly to specify the estimation of the variance-covariance matrix.- vcov.fun
Variance-covariance matrix used to compute uncertainty estimates (e.g., for robust standard errors). This argument accepts a covariance matrix, a function which returns a covariance matrix, or a string which identifies the function to be used to compute the covariance matrix. See

`model_parameters()`

.- vcov.type
Deprecated. The

`type`

-argument is now included in`vcov.args`

.- vcov.args
List of arguments to be passed to the function identified by the

`vcov.fun`

argument. This function is typically supplied by the sandwich or clubSandwich packages. Please refer to their documentation (e.g.,`?sandwich::vcovHAC`

) to see the list of available arguments.- 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.std.p
Character vector, used for the column heading of p values. Default is

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

`"std. 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.rsq
Amount of decimals for r-squared 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.- df.method, p.val
Method for computing degrees of freedom for p-values, standard errors and confidence intervals (CI). Only applies to mixed models. Use

`df.method = "wald"`

for a faster, but less precise computation. This will use the residual degrees of freedom (as returned by`df.residual()`

) for linear mixed models, and`Inf`

degrees if freedom for all other model families.`df.method = "kenward"`

(or`df.method = "kr"`

) uses Kenward-Roger approximation for the degrees of freedom.`df.method = "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

`snakecase::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 opened 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.- encoding
Character vector, indicating the charset encoding used for variable and value labels. Default is

`"UTF-8"`

. For Windows Systems,`encoding = "Windows-1252"`

might be necessary for proper display of special characters.

## Value

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.

## Details

### Standardized Estimates

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.

### How do I use `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

You can add style information to the default styles by using a + (plus-sign) as initial character for the argument attributes. Examples:

`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.

## Note

The HTML tables can either be saved as file and manually opened (use argument `file`

) or
they can be saved as temporary files and will be displayed in the RStudio Viewer pane (if working with RStudio)
or opened with the default web browser. Displaying resp. opening a temporary file is the
default behaviour (i.e. `file = NULL`

).

Examples are shown in these three vignettes:
Summary of Regression Models as HTML Table,
Summary of Mixed Models as HTML Table and
Summary of Bayesian Models as HTML Table.