pelp {lmom} | R Documentation |
Computes the parameters of a probability distribution as a function of the L-moments.
pelp(lmom, pfunc, start, bounds = c(-Inf, Inf), type = c("n", "s", "ls", "lss"), method=c("nlm", "uniroot", "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN"), acc = 1e-5, ...) pelq(lmom, qfunc, start, type = c("n", "s", "ls", "lss"), method=c("nlm", "uniroot", "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN"), acc = 1e-5, ...)
lmom |
Numeric vector containing the L-moments of the distribution or of a data sample. |
pfunc |
Cumulative distribution function of the distribution. |
qfunc |
Quantile function of the distribution. |
start |
Vector of starting values for the parameters. |
bounds |
Either a vector of length 2, containing the lower and upper bounds of the distribution, or a function that calculates these bounds given the distribution parameters as inputs. |
type |
Type of distribution, i.e. how it is parametrized.
Must be one of the following:
For more details, see the “Distribution type” section below. |
method |
Method used to estimate the shape parameters
(i.e. all parameters other than the location and scale parameters, if any).
Valid values are "nlm" (the default), "uniroot"
(which is valid only if the distribution has at most one shape parameter),
and any of the values of the method argument of function optim .
See the “Details” section below.
|
acc |
Requested accuracy for the estimated parameters. This will be absolute accuracy for shape parameters, relative accuracy for a scale parameter, and absolute accuracy of the location parameter divided by the scale parameter for a location parameter. |
... |
Further arguments will be passed to the optimization function
(nlm , uniroot , or optim ). |
For shape parameters, numerical optimization is used to
find parameter values for which the population L-moments or L-moment ratios
are equal to the values supplied in lmom
.
Numerical optimization uses R functions nlm
(if method="nlm"
),
uniroot
(if method="uniroot"
), or
optim
with the specified method (for the other values of method
).
Function uniroot
uses one-dimensional root-finding,
while functions nlm
and optim
try to minimize
a criterion function that is the sum of squared differences between the
population L-moments or L-moment ratios and the values supplied in lmom
.
Location and scale parameters are then estimated noniteratively.
In all cases, the calculation of population L-moments and
L-moment ratios is performed by function lmrp
or lmrq
(when using pelp
or pelq
respectively).
This approach is very crude. Nonetheless, it is often effective in practice. As in all numerical optimizations, success may depend on the way that the distribution is parametrized and on the particular choice of starting values for the parameters.
A list with components:
para |
Numeric vector containing the estimated parameters of the distribution. |
code |
An integer indicating the result of the numerical optimization
used to estimate the shape parameters. It is 0 if there
are no shape parameters. In general, values 1 and 2
indicate successful convergence of the iterative procedure,
a value of 3 indicates that the iteration may not have converged,
and values of 4 or more indicate that the iteration did not converge.
Specifically, code is:
For method "nlm" , the code component
of the return value from nlm .
For method "uniroot" , 1 if the estimated
precision of the shape parameter is less than or equal to acc ,
and 4 otherwise.
For the other methods, the convergence component
of the return value from optim .
|
The length of lmom
should be (at least) the highest
order of L-moment used in the estimation procedure. For a distribution
with r parameters this is
2r-2 if type="lss"
and r otherwise.
pfunc
and qfunc
can be either the standard R form of
cumulative distribution function or quantile function
(i.e. for a distribution with r parameters, the first argument is the
variate x or the probability p and the next r arguments
are the parameters of the distribution) or the cdf...
or
qua...
forms used throughout the lmom package
(i.e. the first argument is the variate x or probability p
and the second argument is a vector containing the parameter values).
Even for the R form, however, starting values for the parameters
are supplied as a vector start
.
If bounds
is a function, its arguments must match
the distribution parameter arguments of pfunc
:
either a single vector, or a separate argument for each parameter.
It is assumed that location and scale parameters come first in
the set of parameters of the distribution. Specifically:
if type="ls"
or type="lss"
, it is assumed
that the first parameter is the location parameter and
that the second parameter is the scale parameter;
if type="s"
it is assumed
that the first parameter is the scale parameter.
It is important that the length of start
be equal to the number
of parameters of the distribution. Starting values for
location and scale parameters should be included in start
,
even though they are not used.
If start
has the wrong length, it is possible that
meaningless results will be returned without any warning being issued.
The type
argument affects estimation as follows.
We assume that location and scale parameters are xi and α
respectively, and that the shape parameters (if there are any)
are collectively designated by theta.
If type="ls"
, then the L-moment ratios tau_3, tau_4, ...
depend only on the shape parameters. If there are any shape parameters,
they are estimated by equating the sample L-moment ratios of orders
3, 4, etc., to the population L-moment ratios
and solving the resulting equations for the shape parameters
(using as many equations as there are shape parameters).
The L-moment λ_2 is a multiple of α, the multiplier
being a function only of theta.
α is estimated by dividing the second sample L-moment
by the multiplier function evaluated at the estimated value of theta.
The L-moment λ_1 is xi plus
a function of α and theta.
xi is estimated by subtracting from the first sample L-moment
the function evaluated at the estimated values of
α and theta.
If type="lss"
, then
the L-moment ratios of odd order, tau_3, tau_5, ..., are zero and
the L-moment ratios of even order, tau_4, tau_6, ...,
depend only on the shape parameters. If there are any shape parameters,
they are estimated by equating the sample L-moment ratios of orders
4, 6, etc., to the population L-moment ratios
and solving the resulting equations for the shape parameters
(using as many equations as there are shape parameters).
Parameters α and xi are estimated as in case when type="ls"
.
If type="s"
, then the L-moments divided by λ_1,
i.e. λ_2/λ_1, λ_3/λ_1, ...,
depend only on the shape parameters. If there are any shape parameters,
they are estimated by equating the sample L-moments
(divided by the first sample L-moment) of orders 2, 3, etc.,
to the corresponding population L-moments
(divided by the first population L-moment)
and solving the resulting equations
(as many equations as there are shape parameters).
The L-moment λ_1 is a multiple of α, the multiplier
being a function only of theta.
α is estimated by dividing the first sample L-moment
by the multiplier function evaluated at the estimated value of theta.
If type="n"
, then all parameters are shape parameters.
They are estimated by equating the sample L-moments of orders
1, 2, etc., to the population L-moments
and solving the resulting equations for the parameters
(using as many equations as there are parameters).
J. R. M. Hosking hosking@watson.ibm.com
pelexp
for parameter estimation of specific distributions.
## Gamma distribution -- rewritten so that its first parameter ## is a scale parameter my.pgamma <- function(x, scale, shape) pgamma(x, shape=shape, scale=scale) pelp(c(5,2), my.pgamma, start=c(1,1), bounds=c(0,Inf), type="s") # We can also do the estimation suppressing our knowledge # that one parameter is a shape parameter. pelp(c(5,2), my.pgamma, start=c(1,1), bounds=c(0,Inf), type="n") rm(my.pgamma) ## Kappa distribution -- has location, scale and 2 shape parameters # Estimate via pelq pel.out <- pelq(c(10,5,0.3,0.15), quakap, start=c(0,1,0,0), type="ls") pel.out # Check that L-moments of estimated distribution agree with the # L-moments input to pelq() lmrkap(pel.out$para) # Compare with the distribution-specific routine pelkap pelkap(c(10,5,0.3,0.15)) rm(pel.out) # Similar results -- what's the advantage of the specific routine? system.time(pelq(c(10,5,0.3,0.15), quakap, start=c(0,1,0,0), type="ls")) system.time(pelkap(c(10,5,0.3,0.15))) # Caution -- pelq() will not check that estimates are reasonable lmom <- c(10,5,0.2,0.25) pel.out <- pelq(lmom, quakap, start=c(0,1,0,0), type="ls") pel.out lmrkap(pel.out$para) # should be close to lmom, but tau_3 and tau_4 are not # What happened? pelkap will tell us try(pelkap(lmom)) rm(lmom, pel.out) ## Inverse Gaussian -- don't have explicit estimators for this ## distribution, but can use numerical methods # # CDF of inverse gaussian distribution pig <- function(x, mu, lambda) { temp <- suppressWarnings(sqrt(lambda/x)) xx <- pnorm(temp*(x/mu-1))+exp(2*lambda/mu+pnorm(temp*(x/mu+1), lower.tail=FALSE, log.p=TRUE)) out <- ifelse(x<=0, 0, xx) out } # Fit to ozone data data(airquality) (lmom<-samlmu(airquality$Ozone)) pel.out <- pelp(lmom[1:2], pig, start=c(10,10), bounds=c(0,Inf)) pel.out # First four L-moments of fitted distribution, # for comparison with sample L-moments lmrp(pig, pel.out$para[1], pel.out$para[2], bounds=c(0,Inf)) rm(pel.out) ## A Student t distribution with location and scale parameters # qstu <- function(p, xi, alpha, df) xi + alpha * qt(p, df) # Estimate parameters. Distribution is symmetric: use type="lss" pelq(c(3,5,0,0.2345), qstu, start=c(0,1,10), type="lss") # Doesn't converge (at least on the author's system) -- # try a different parametrization qstu2 <- function(p, xi, alpha, shape) xi + alpha * qt(p, 1/shape) # Now it converges pelq(c(3,5,0,0.2345), qstu2, start=c(0,1,0.1), type="lss") # Or try a different optimization method pelq(c(3,5,0,0.2345), qstu, start=c(0,1,10), type="lss", method="uniroot", lower=2, upper=100) rm(qstu,qstu2)