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,
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 = 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.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 = "&nbsp;&ndash;&nbsp;",
minus.sign = "&#45;",
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),
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_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.

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

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

Character vector, specifying the estimation type for the robust covariance matrix estimation (see vcovHC() or clubSandwich::vcovCR() for details).

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.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".

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) and

• the 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:

1. the class-names with "css."-prefix as argument name and

2. each 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.