`summ()`

prints output for a regression model in a fashion
similar to `summary()`

, but formatted differently with more options.

## Usage

```
# S3 method for rq
summ(
model,
scale = FALSE,
confint = getOption("summ-confint", FALSE),
ci.width = getOption("summ-ci.width", 0.95),
se = c("nid", "rank", "iid", "ker", "boot"),
boot.sims = 1000,
boot.method = "xy",
vifs = getOption("summ-vifs", FALSE),
digits = getOption("jtools-digits", 2),
pvals = getOption("summ-pvals", TRUE),
n.sd = 1,
center = FALSE,
transform.response = FALSE,
data = NULL,
model.info = getOption("summ-model.info", TRUE),
model.fit = getOption("summ-model.fit", TRUE),
model.coefs = getOption("summ-model.coefs", TRUE),
which.cols = NULL,
...
)
```

## Arguments

- model
A

`rq`

model. At this time,`rqs`

models (multiple`tau`

parameters) are not supported.- scale
If

`TRUE`

, reports standardized regression coefficients by scaling and mean-centering input data (the latter can be changed via the`scale.only`

argument). Default is`FALSE`

.- confint
Show confidence intervals instead of standard errors? Default is

`FALSE`

.- ci.width
A number between 0 and 1 that signifies the width of the desired confidence interval. Default is

`.95`

, which corresponds to a 95% confidence interval. Ignored if`confint = FALSE`

.- se
One of "nid", "rank", "iid", "ker", or "boot". "nid" is default. See

`quantreg::summary.rq()`

documentation for more about these options.- boot.sims
If

`se = "boot"`

, the number of bootstrap replications to perform. This is passed as the`R`

argument to`boot.rq`

- boot.method
If

`se = "boot"`

, the type of bootstrapping method to use. Default is "xy", but see`quantreg::boot.rq()`

for more options.- vifs
If

`TRUE`

, adds a column to output with variance inflation factors (VIF). Default is`FALSE`

.- digits
An integer specifying the number of digits past the decimal to report in the output. Default is 2. You can change the default number of digits for all jtools functions with

`options("jtools-digits" = digits)`

where digits is the desired number.- pvals
Show p values? If

`FALSE`

, these are not printed. Default is`TRUE`

.- n.sd
If

`scale = TRUE`

, how many standard deviations should predictors be divided by? Default is 1, though some suggest 2.- center
If you want coefficients for mean-centered variables but don't want to standardize, set this to

`TRUE`

. Note that setting this to false does not affect whether`scale`

mean-centers variables. Use`scale.only`

for that.- transform.response
Should scaling/centering apply to response variable? Default is

`FALSE`

.- data
If you provide the data used to fit the model here, that data frame is used to re-fit the model (if

`scale`

is`TRUE`

) instead of the`stats::model.frame()`

of the model. This is particularly useful if you have variable transformations or polynomial terms specified in the formula.- model.info
Toggles printing of basic information on sample size, name of DV, and number of predictors.

- model.fit
Toggles printing of model fit statistics.

- model.coefs
Toggles printing of model coefficents.

- which.cols
Developmental feature. By providing columns by name, you can add/remove/reorder requested columns in the output. Not fully supported, for now.

- ...
Among other things, arguments are passed to

`scale_mod()`

or`center_mod()`

when`center`

or`scale`

is`TRUE`

.

## Details

This method implements most of the things I think most users would
asking `summary.rq`

for. `hs`

, `U`

, and `gamma`

are ignored.

Note that when using `se = "rank"`

, there are no standard errors,
test statistics, or p values calculated.

About the R1 fit statistic: Described in Koenker & Machado (1999), this offers an interpretation similar to R-squared in OLS regression. While you could calculate R-squared for these models, it goes against the underlying theoretical rationale for them. Koenker himself is not a big fan of R1 either, but it's something. See Koenker & Machado (1999) for more info.

## References

Koenker, R., & Machado, J. A. F. (1999). Goodness of fit and related
inference processes for quantile regression.
*Journal of the American Statistical Association*, *94*, 1296–1310.
https://doi.org/10.1080/01621459.1999.10473882

## See also

Other summ:
`summ.glm()`

,
`summ.lm()`

,
`summ.merMod()`

,
`summ.svyglm()`

## Examples

```
if (requireNamespace("quantreg")) {
library(quantreg)
data(engel)
fitrq <- rq(income ~ foodexp, data = engel, tau = 0.5)
summ(fitrq)
}
#> Loading required package: SparseM
#>
#> Attaching package: ‘SparseM’
#> The following object is masked from ‘package:base’:
#>
#> backsolve
#>
#> Attaching package: ‘quantreg’
#> The following object is masked from ‘package:survival’:
#>
#> untangle.specials
#> MODEL INFO:
#> Observations: 235
#> Dependent Variable: income
#> Type: Quantile regression
#> Quantile (tau): 0.5
#> Method: Barrodale-Roberts
#>
#> MODEL FIT:
#> R¹(0.5) = 0.64
#>
#> Standard errors: Sandwich (Huber)
#> --------------------------------------------------
#> Est. S.E. t val. p
#> ----------------- -------- ------- -------- ------
#> (Intercept) -14.96 28.69 -0.52 0.60
#> foodexp 1.55 0.06 26.66 0.00
#> --------------------------------------------------
```