summ()
prints output for a regression model in a fashion
similar to summary()
, but formatted differently with more options.
# S3 method for rq summ( model, scale = FALSE, confint = getOption("summconfint", FALSE), ci.width = getOption("summci.width", 0.95), se = c("nid", "rank", "iid", "ker", "boot"), boot.sims = 1000, boot.method = "xy", vifs = getOption("summvifs", FALSE), digits = getOption("jtoolsdigits", 2), pvals = getOption("summpvals", TRUE), n.sd = 1, center = FALSE, transform.response = FALSE, data = NULL, model.info = getOption("summmodel.info", TRUE), model.fit = getOption("summmodel.fit", TRUE), which.cols = NULL, ... )
model  A 

scale  If 
confint  Show confidence intervals instead of standard errors? Default
is 
ci.width  A number between 0 and 1 that signifies the width of the
desired confidence interval. Default is 
se  One of "nid", "rank", "iid", "ker", or "boot". "nid" is default.
See 
boot.sims  If 
boot.method  If 
vifs  If 
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

pvals  Show p values? If 
n.sd  If 
center  If you want coefficients for meancentered variables but don't
want to standardize, set this to 
transform.response  Should scaling/centering apply to response
variable? Default is 
data  If you provide the data used to fit the model here, that data
frame is used to refit the model (if 
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. 
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 
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 Rsquared in OLS regression. While you could calculate Rsquared 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.
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
Other summ:
summ.glm()
,
summ.lm()
,
summ.merMod()
,
summ.svyglm()
if (requireNamespace("quantreg")) { library(quantreg) data(engel) fitrq < rq(income ~ foodexp, data = engel, tau = 0.5) summ(fitrq) }#>#>#> #>#>#> #>#> #>#>#> #>#> MODEL INFO: #> Observations: 235 #> Dependent Variable: income #> Type: Quantile regression #> Quantile (tau): 0.5 #> Method: BarrodaleRoberts #> #> 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 #> 