mmedist: Matching moment fit of univariate distributions In fitdistrplus: Help to Fit of a Parametric Distribution to Non-Censored or Censored Data

 mmedist R Documentation

Matching moment fit of univariate distributions

Description

Fit of univariate distributions by matching moments (raw or centered) for non censored data.

Usage

``````mmedist(data, distr, order, memp, start = NULL, fix.arg = NULL, optim.method = "default",
lower = -Inf, upper = Inf, custom.optim = NULL, weights = NULL, silent = TRUE,

``````

Arguments

 `data` A numeric vector for non censored data. `distr` A character string `"name"` naming a distribution (see 'details'). `order` A numeric vector for the moment order(s). The length of this vector must be equal to the number of parameters to estimate. `memp` A function implementing empirical moments, raw or centered but has to be consistent with `distr` argument (and `weights` argument). See details below. `start` A named list giving the initial values of parameters of the named distribution or a function of data computing initial values and returning a named list. This argument may be omitted (default) for some distributions for which reasonable starting values are computed (see the 'details' section of `mledist`). `fix.arg` An optional named list giving the values of fixed parameters of the named distribution or a function of data computing (fixed) parameter values and returning a named list. Parameters with fixed value are thus NOT estimated. `optim.method` `"default"` or optimization method to pass to `optim`. `lower` Left bounds on the parameters for the `"L-BFGS-B"` method (see `optim`). `upper` Right bounds on the parameters for the `"L-BFGS-B"` method (see `optim`). `custom.optim` a function carrying the optimization . `weights` an optional vector of weights to be used in the fitting process. Should be `NULL` or a numeric vector with strictly positive integers (typically the number of occurences of each observation). If non-`NULL`, weighted MME is used, otherwise ordinary MME. `silent` A logical to remove or show warnings when bootstraping. `gradient` A function to return the gradient of the squared difference for the `"BFGS"`, `"CG"` and `"L-BFGS-B"` methods. If it is `NULL`, a finite-difference approximation will be used, see details. `checkstartfix` A logical to test starting and fixed values. Do not change it. `...` further arguments passed to the `optim`, `constrOptim` or `custom.optim` function.

Details

The argument `distr` can be one of the base R distributions: `"norm"`, `"lnorm"`, `"exp"` and `"pois"`, `"gamma"`, `"logis"`, `"nbinom"` , `"geom"`, `"beta"` and `"unif"`. In that case, no other arguments than `data` and `distr` are required, because the estimate is computed by a closed-form formula. For distributions characterized by one parameter (`"geom"`, `"pois"` and `"exp"`), this parameter is simply estimated by matching theoretical and observed means, and for distributions characterized by two parameters, these parameters are estimated by matching theoretical and observed means and variances (Vose, 2000). Note that for these closed-form formula, `fix.arg` cannot be used and `start` is ignored.

The argument `distr` can also be the distribution name as long as a corresponding `mdistr` function exists, e.g. `"pareto"` if `"mpareto"` exists. In that case arguments arguments `order` and `memp` have to be supplied in order to carry out the matching numerically, by minimization of the sum of squared differences between observed and theoretical moments. Optionnally other arguments can be supplied to control optimization (see the 'details' section of `mledist` for details about arguments for the control of optimization). In that case, `fix.arg` can be used and `start` is taken into account.

For non closed-form estimators, `memp` must be provided to compute empirical moments. When `weights=NULL`, this function must have two arguments `x, order`: `x` the numeric vector of the data and `order` the order of the moment. When `weights!=NULL`, this function must have three arguments `x, order, weights`: `x` the numeric vector of the data, `order` the order of the moment, `weights` the numeric vector of weights. See examples below.

Optionally, a vector of `weights` can be used in the fitting process. By default (when `weigths=NULL`), ordinary MME is carried out, otherwise the specified weights are used to compute (raw or centered) weighted moments. For closed-form estimators, weighted mean and variance are computed by `wtdmean` and `wtdvar` from the `Hmisc` package. When a numerical minimization is used, weighted are expected to be computed by the `memp` function. It is not yet possible to take into account weighths in functions `plotdist`, `plotdistcens`, `plot.fitdist`, `plot.fitdistcens`, `cdfcomp`, `cdfcompcens`, `denscomp`, `ppcomp`, `qqcomp`, `gofstat` and `descdist` (developments planned in the future).

This function is not intended to be called directly but is internally called in `fitdist` and `bootdist` when used with the matching moments method.

Value

`mmedist` returns a list with following components,

 `estimate` the parameter estimates. `convergence` an integer code for the convergence of `optim` defined as below or defined by the user in the user-supplied optimization function. `0` indicates successful convergence. `1` indicates that the iteration limit of `optim` has been reached. `10` indicates degeneracy of the Nealder-Mead simplex. `100` indicates that `optim` encountered an internal error. `value` the minimal value reached for the criterion to minimize. `hessian` a symmetric matrix computed by `optim` as an estimate of the Hessian at the solution found or computed in the user-supplied optimization function. `optim.function` (if appropriate) the name of the optimization function used for maximum likelihood. `optim.method` (if appropriate) when `optim` is used, the name of the algorithm used, the field `method` of the `custom.optim` function otherwise. `fix.arg` the named list giving the values of parameters of the named distribution that must kept fixed rather than estimated by maximum likelihood or `NULL` if there are no such parameters. `fix.arg.fun` the function used to set the value of `fix.arg` or `NULL`. `weights` the vector of weigths used in the estimation process or `NULL`. `counts` A two-element integer vector giving the number of calls to the log-likelihood function and its gradient respectively. This excludes those calls needed to compute the Hessian, if requested, and any calls to log-likelihood function to compute a finite-difference approximation to the gradient. `counts` is returned by `optim` or the user-supplied function or set to `NULL`. `optim.message` A character string giving any additional information returned by the optimizer, or `NULL`. To understand exactly the message, see the source code. `loglik` the log-likelihood value. `method` either `"closed formula"` or the name of the optimization method. `order` the order of the moment(s) matched. `memp` the empirical moment function.

Author(s)

Marie-Laure Delignette-Muller and Christophe Dutang.

References

Evans M, Hastings N and Peacock B (2000), Statistical distributions. John Wiley and Sons Inc.

Vose D (2000), Risk analysis, a quantitative guide. John Wiley & Sons Ltd, Chischester, England, pp. 99-143.

Delignette-Muller ML and Dutang C (2015), fitdistrplus: An R Package for Fitting Distributions. Journal of Statistical Software, 64(4), 1-34.

`mmedist`, `qmedist`, `mgedist`, `fitdist`,`fitdistcens`, `optim`, `bootdistcens` and `bootdist`.

Examples

``````
# (1) basic fit of a normal distribution with moment matching estimation
#

set.seed(1234)
n <- 100
x1 <- rnorm(n=n)
mmedist(x1, "norm")

#weighted
w <- c(rep(1, n/2), rep(10, n/2))
mmedist(x1, "norm", weights=w)\$estimate

# (2) fit a discrete distribution (Poisson)
#

set.seed(1234)
x2 <- rpois(n=30,lambda = 2)
mmedist(x2, "pois")

# (3) fit a finite-support distribution (beta)
#

set.seed(1234)
x3 <- rbeta(n=100,shape1=5, shape2=10)
mmedist(x3, "beta")

# (4) fit a Pareto distribution
#

require(actuar)
#simulate a sample
x4  <-  rpareto(1000, 6, 2)

#empirical raw moment
memp  <-  function(x, order) mean(x^order)
memp2 <- function(x, order, weights) sum(x^order * weights)/sum(weights)

#fit by MME
mmedist(x4, "pareto", order=c(1, 2), memp=memp,
start=list(shape=10, scale=10), lower=1, upper=Inf)
#fit by weighted MME
w <- rep(1, length(x4))
w[x4 < 1] <- 2
mmedist(x4, "pareto", order=c(1, 2), memp=memp2, weights=w,
start=list(shape=10, scale=10), lower=1, upper=Inf)

``````

fitdistrplus documentation built on April 25, 2023, 5:09 p.m.