Quantile regression summaries with options

summ() prints output for a regression model in a fashion similar to summary(), but formatted differently with more options.

Usage

# S3 method for class '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

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:Matrix’:
#> 
#>     det
#> 
#> 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
#> --------------------------------------------------