This function allows users to use the features of
`summ()`

(e.g., standardization, robust standard errors)
in the context of shareable HTML, LaTeX, and
Microsoft Word tables. It relies heavily on `huxtable::huxreg()`

to do the table formatting. This is particularly useful for putting
the results of multiple models into a single table.

## Usage

```
export_summs(
...,
error_format = "({std.error})",
error_pos = c("below", "right", "same"),
ci_level = 0.95,
statistics = NULL,
model.names = NULL,
coefs = NULL,
to.file = NULL,
file.name = NULL
)
```

## Arguments

- ...
At minimum, a regression object(s). See details for more arguments.

- error_format
Which of standard error, confidence intervals, test statistics, or p values should be used to express uncertainty of estimates for regression coefficients? See details for more info. Default:

`"({std.error})"`

- error_pos
Where should the error statistic defined in

`error_style`

be placed relative to the coefficient estimate? Default: "below"- ci_level
If reporting confidence intervals, what should the confidence level be? By default, it is .95 if confidence intervals are requested in

`error_format`

.- statistics
Which model summary statistics should be included? See

`huxreg`

for more on usage. The default for this function depends on the model type. See details for more on the defaults by model type.- model.names
If you want to give your model(s) names at the top of each column, provide them here as a character vector. Otherwise, they will just be labeled by number. Default: NULL

- coefs
If you want to include only a subset of the coefficients in the table, specify them here in a character vector. If you want the table to show different names for the coefficients, give a named vector where the names are the preferred coefficient names. See details for more.

- to.file
Export the table to a Microsoft Word, PDF, or HTML document? This functionality relies on

`huxtable`

's`quick_`

functions (`huxtable::quick_docx()`

,`huxtable::quick_pdf()`

,`huxtable::quick_html()`

,`huxtable::quick_xlsx()`

). Acceptable arguments are "Word" or "docx" (equivalent), "pdf", "html", or "xlsx". All are case insensitive. Default is NULL, meaning the table is not saved.- file.name
File name with (optionally) file path to save the file. Ignored if

`to.file`

is FALSE. Default: NULL

## Details

There are many optional parameters not documented above. Any
argument that you would want to pass to `summ()`

, for instance,
will be used. Of particular interest may be the robust and scale
arguments. Note that some `summ`

arguments may not have any bearing
on the table output.

The default model summary statistics reporting follows this logic:

summ.lm =

`c(N = "nobs", R2 = "r.squared")`

,summ.glm =

`c(N = "nobs", AIC = "AIC", BIC = "BIC", `Pseudo R2` = "pseudo.r.squared")`

,summ.svyglm =

`c(N = "nobs", R2 = "r.squared")`

,summ.merMod =

`c(N = "nobs", AIC = "AIC", BIC = "BIC", `R2 (fixed)` = "r.squared.fixed", `R2 (total)` = "r.squared")`

summ.rq =

`c(N = "nobs", tau = "tau", R1 = "r.1", AIC = "AIC", BIC = "BIC")`

Be sure to look at the `summ()`

documentation for more on the calculation
of these and other statistics, especially for mixed models.

If you set `statistics = "all"`

, then the statistics argument
passed to `huxreg`

will be `NULL`

, which reports whichever
model statistics are available via `glance`

. If you want no
model summary statistics, set the argument to `character(0)`

.

You have a few options for the `error_format`

argument.
You can include anything returned by `broom::tidy()`

(see also `tidy.summ()`

). For the most part, you will
be interested in `std.error`

(standard error), `statistic`

(test statistic, e.g. t-value or z-value), `p.value`

, or
`conf.high`

and `conf.low`

, which correspond to the
upper and lower bounds of the confidence interval for the estimate.
Note that the default `ci_level`

argument is .95, but you
can alter that as desired.

To format the error statistics, simply put the statistics desired in
curly braces wherever you want them in a character string. For example,
if you want the standard error in parentheses, the argument would be
`"({std.error})"`

, which is the default. Some other ideas:

`"({statistic})"`

, which gives you the test statistic in parentheses.`"({statistic}, p = {p.value})"`

, which gives the test statistic followed by a "p =" p value all in parentheses. Note that you'll have to pay special attention to rounding if you do this to keep cells sufficiently narrow.`"[{conf.low}, {conf.high}]"`

, which gives the confidence interval in the standard bracket notation. You could also explicitly write the confidence level, e.g.,`"CI [{conf.low}, {conf.high}]"`

.

For `coefs`

, the argument is slightly different than what is default
in `huxreg`

. If you provide a named vector of coefficients, then
the table will refer to the selected coefficients by the names of the
vector rather than the coefficient names. For instance, if I want to
include only the coefficients for the `hp`

and `mpg`

but have
the table refer to them as "Horsepower" and "Miles/gallon", I'd provide
the argument like this:
`c("Horsepower" = "hp", "Miles/gallon" = "mpg")`

You can also pass any argument accepted by the
`huxtable::huxreg()`

function. A few that are likely to be
oft-used are documented above, but visit `huxreg`

's documentation
for more info.

For info on converting the `huxtable::huxtable()`

object to
HTML or LaTeX, see `huxtable`

's documentation.

## Examples

```
states <- as.data.frame(state.x77)
fit1 <- lm(Income ~ Frost, data = states)
fit2 <- lm(Income ~ Frost + Illiteracy, data = states)
fit3 <- lm(Income ~ Frost + Illiteracy + Murder, data = states)
if (requireNamespace("huxtable")) {
# Export all 3 regressions with "Model #" labels,
# standardized coefficients, and robust standard errors
export_summs(fit1, fit2, fit3,
model.names = c("Model 1","Model 2","Model 3"),
coefs = c("Frost Days" = "Frost",
"% Illiterate" = "Illiteracy",
"Murder Rate" = "Murder"),
scale = TRUE, robust = TRUE)
}
#> ─────────────────────────────────────────────────────────────────────────────
#> Model 1 Model 2 Model 3
#> ──────────────────────────────────────────────────────────
#> Frost Days 139.04 -75.52 -65.19
#> (94.89) (138.74) (149.01)
#> % Illiterate -319.31 * -372.25 **
#> (124.83) (120.00)
#> Murder Rate 85.18
#> (136.02)
#> ──────────────────────────────────────────────────────────
#> N 50 50 50
#> R2 0.05 0.20 0.21
#> ─────────────────────────────────────────────────────────────────────────────
#> All continuous predictors are mean-centered and scaled by 1
#> standard deviation. The outcome variable is in its original
#> units. Standard errors are heteroskedasticity robust. *** p <
#> 0.001; ** p < 0.01; * p < 0.05.
#>
#> Column names: names, Model 1, Model 2, Model 3
```