The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
gkrr_vcov() computes the sandwich covariance matrix
with any member of the GKRReg-HC family of finite-sample corrections
(HC0 to HC4). The weighted leverage can be
taken from the bread matrix (leverage = "bread", the
quantity delivered by the leave-one-out expansion) or from the
kernel-weighted hat matrix (leverage = "kernel", always in
[0, 1]), and HC1/HC4 can be
calibrated by the effective sample size sum(k_ii)
(neff = "sum") or by Kish’s effective sample size
(neff = "kish").vcov() gains a type argument dispatching
to gkrr_vcov(). The default type = "HC0"
reproduces the previous behaviour.gkrr_vcov_all() returns standard errors for every
variant at once, together with diagnostics (both effective sample sizes,
the number of observations with a negative bread weight, and the range
of both leverages). Intended for simulation studies.gkrr_jackknife() refits the model with each observation
deleted in turn and compares the exact leave-one-out residual with the
one-step approximation used by HC3 and
HC4.gkrr() and sigma2_s1() gain a
seed argument. sigma_method = "s1" draws a
random sub-sample and was previously not reproducible:
repeated calls on the same data returned different estimates of
gamma^2 and hence different coefficients. The caller’s
random number stream is left unchanged.kernel_update, gkrr_vcov_all() and the
jackknife check, followed by a three-way comparison of HC0, the
corrections and the bootstrap.introduction
to gkrreg, so that vignette("gkrreg") is
unambiguous (vignette("introduction") collided with
vignettes of the same name in other packages).summary() method for "gkrr_boot"
objects now labels the printed quantiles of the bootstrap
distribution.Authors@R now uses a Unicode escape, keeping
DESCRIPTION pure ASCII, so the accented character survives documentation
generation.sigma_method = "s4": new width hyper-parameter
estimator based on the AICc-selected bandwidth from
sm::h.select() (squared to obtain a variance-scale estimate
of gamma^2). Recommended for large samples
(n >= 200). Requires the sm package
(now in Imports).
sigma_method = "auto": automatic selection among S1,
S2, S3 and S4 by out-of-bag bootstrap MSE. The number of replicates and
seed are configurable via the new auto_args argument (e.g.,
auto_args = list(B = 99, seed = 1)). A
message() is emitted informing the user of the selected
method and comparative OOB MSEs.
New auto_args argument in gkrr() for
controlling the "auto" selection bootstrap (default
B = 99).
weighted argument has been removed from
gkrr_boot(). The weighted bootstrap was found to produce
wider confidence intervals than the standard pairs bootstrap in all
tested scenarios, because the robustness of GKRReg already resides in
the kernel weights — the bootstrap itself does not need to replicate
this. The standard pairs bootstrap is the recommended and only available
option.Imports (previously not a
dependency).summary() for inference (standard
errors, confidence intervals and Wald z-tests) when no bootstrap object
is available.vcov() method added, returning the sandwich covariance
matrix.summary() gains a se_tol argument
controlling the threshold for divergence warnings between sandwich and
bootstrap standard errors.summary() emits a proactive note suggesting bootstrap
inference when small sample size (n < 50) or heavy
contamination is detected.par() settings are now properly restored in all plot
methods and vignette chunks using
oldpar <- par(no.readonly = TRUE) /
on.exit(par(oldpar)).First public release.
gkrr() fits a Gaussian Kernel Robust Regression model
via IRWLS. Three estimators for the kernel width hyper-parameter:
"s1" (Caputo), "s2" (pairwise median) and
"s3" (residual variance).gkrr_boot() runs a pairs bootstrap to produce standard
errors, confidence intervals (percentile, normal, BCa) and
p-values.boot argument in gkrr(): set
boot = TRUE to compute bootstrap inference at fit
time.summary.gkrr() prints a coefficient table modelled
after summary.lm().plot.gkrr() provides six diagnostic panels where point
size is inversely proportional to the kernel weight.plot.gkrr_boot() provides histogram and scatter-plot
matrix panels.belgium_calls,
cloud_point, kootenay, delivery,
mammals, stars_cyg.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.