summ.rq | R Documentation |
summ()
prints output for a regression model in a fashion
similar to summary()
, but formatted differently with more options.
## 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,
...
)
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 mean-centered 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 re-fit 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. |
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 |
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.
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)
}
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