glocvd.fit | R Documentation |
Maximum-likelihood fitting for the generalized logistic distribution,
including generalized linear modelling of each parameter. The function differs from
glod.fit
because it uses a different parametrisation of the distribution based on
the \tau
, the ratio of the scale parameter and the location parameter, which is a monotonic
function of the coefficient of variation. This means that when regression models are applied for the location,
these also affect the scale.
The function allows any parameter to be kept fixed and to not be estimated.
glocvd.fit(
xdat,
ydat = NULL,
mul = NULL,
taul = NULL,
shl = NULL,
mulink = identity,
taulink = identity,
shlink = identity,
muinit = NULL,
tauinit = NULL,
shinit = NULL,
show = TRUE,
method = "Nelder-Mead",
optimPars = NULL,
maxit = 10000,
fixedPars = list(mu = NULL, tau = NULL, sh = NULL),
...
)
xdat |
A numeric vector of data to be fitted |
ydat |
A matrix of covariates for generalized linear modelling of the parameters (or NULL (the default) for stationary fitting). The number of rows should be the same as the length of xdat |
mul |
Numeric vectors of integers, giving the columns of ydat that contain covariates for generalized linear modelling of the location parameter (or NULL (the default) if the corresponding parameter is stationary) |
taul |
As |
shl |
As |
mulink |
the link function for the location parameter - default to identity |
taulink |
the link function for the scale parameter - default to identity |
shlink |
the link function for the shape parameter - default to identity |
muinit |
initial values for the location parameter |
tauinit |
initial values for the tau parameter |
shinit |
initial values for the shape parameter |
show |
Logical; if |
method |
The optimization method (see |
optimPars |
A string with other parameters to pass into |
maxit |
The maximum number of iterations |
fixedPars |
a named list to fix any of the distribution parameter to a given value. When the named parameter is set to |
... |
Other control parameters for the optimization. These are passed to components of the control argument of optim. |
The distribution is discussed in the Hosking and Wallis book and is used as the default distribution for flood frequency estimation in the UK
An object of the glocd.fit
class, similar to glo.fit
.
#'
In the output the vals
matrix gives the location and scale values obtained as scale = \tau
* location.
set.seed(12)
x <- runif(500)
y <- rglo(500,loc = 40+4*x, scale = 0.2*(40+4*x), sh = 0.15)
fit1 <- glocvd.fit(y, show=FALSE)
fit1
## now add a regression model for the location
fit2 <- glocvd.fit(y, ydat = cbind(x), mul=1, show=FALSE)
fit2
## now a fit with a fixed tau parameter
fitf <- glocvd.fit(y, ydat = cbind(x), mul=1, show=FALSE, fixedPars = list(tau = 0.2))
fitf ## only two parameters are estimated (location and shape)
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