Description Usage Arguments Details Value Author(s) References See Also Examples
Summary method for R object of class "loggammarob"
and
print
method for the summary object.
1 2 3 4 5 |
object |
an R object of class |
p |
numeric. Values in the interval [0,1]. Quantile orders for which point estimation and confidence interval are require. |
conf.level |
numeric. A scalar or a vector of length |
prob |
numeric. Value in the interval [0,1]. This is used to determine the interval of numerical integration in the evaluation of the asymptotic variance and covariance matrix. See details below. |
x |
an R object of class |
digits |
number of digits for printing, see |
... |
potentially more arguments passed to methods. |
The prob
argument determines the interval of the numerical
integration in the evaluation of the asymptotic variance and
covariance matrix with the following code
qloggamma(p=prob/2,lambda=lambda)
for the lower limit and
qloggamma(p=1-prob/2,lambda=lambda)
for the upper limit. Here
lambda
is the estimate.
An object of class summary.loggammarob
. A list that includes the
following components:
muse |
standard error for the |
sigmase |
standard error for the |
lambdase |
standard error for the |
etase |
standard error for the |
muconf.int |
confidence iterval for the |
sigmaconf.int |
confidence iterval for the |
lambdaconf.int |
confidence iterval for the |
etaconf.int |
confidence iterval for the |
If p
is not NULL
then
q |
quantiles estimate. |
qse |
standard error for the require quantiles estimate. |
qconf.int |
confidence iterval for the require quantiles parameter. |
C. Agostinelli, A. Marazzi, V.J. Yohai and A. Randriamiharisoa
C. Agostinelli, A. Marazzi and V.J. Yohai (2015). Robust estimates of the generalized loggamma distribution. Technometrics, Volume 56, Issue 1, 2014. doi:10.1080/00401706.2013.818578
Agostinelli C., Marazzi A., Yohai V.J., Randriamiharisoa A. (2016). Robust Estimation of the Generalized Loggamma Model: The R Package robustloggamma. Journal of Statistical Software, 70(7), 1-21. doi:10.18637/jss.v070.i07
1 2 3 4 | set.seed(1234)
x <- rloggamma(n=50, lambda=1)
res <- loggammarob(x, control=loggammarob.control(lower=0, upper=2, n=30))
summary(res)
|
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