mpsedist: Maximum Product of Spacing Fit of Univariate Distributions.
In BMT: The BMT Distribution

Description Usage Arguments Details Value Author(s) Source References See Also Examples

Fit of univariate distributions for non-censored data using maximum product of spacing estimation (mpse), also called maximum spacing estimation.

mpsedist(data, distr, start = NULL, fix.arg = NULL,
  optim.method = "default", lower = -Inf, upper = Inf,
  custom.optim = NULL, weights = NULL, silent = TRUE, gradient = NULL,
  ...)

`data`	A numeric vector with the observed values for non-censored data.
`distr`	A character string `"name"` naming a distribution for which the corresponding density function `dname` and the corresponding distribution function `pname` must be classically defined.
`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"` (see details) or an optimization method to pass to `optim`.
`lower`	Left bounds on the parameters for the `"L-BFGS-B"` method (see `optim`) or the `constrOptim` function (as an equivalent linear constraint).
`upper`	Right bounds on the parameters for the `"L-BFGS-B"` method (see `optim`) or the `constrOptim` function (as an equivalent linear constraint).
`custom.optim`	A function carrying the optimization (see details).
`weights`	An optional vector of weights to be used in the fitting process. Should be `NULL` or a numeric vector with strictly positive numbers. If non-`NULL`, weighted mpse is used, otherwise ordinary mpse.
`silent`	A logical to remove or show warnings when bootstraping.
`gradient`	A function to return the gradient of the optimization objective function for the `"BFGS"`, `"CG"` and `"L-BFGS-B"` methods. If it is `NULL`, a finite-difference approximation will be used, see `optim`.
`...`	Further arguments passed to the `optim`, `constrOptim` or `custom.optim` function.

The mpsedist function carries out the maximum product of spacing estimation numerically, by maximization of the arithmetic mean of \log(F(k) - F(k-1)).

The optimization process is the same as mledist, see the 'details' section of that function.

Optionally, a vector of weights can be used in the fitting process. By default (when weigths=NULL), ordinary mpse is carried out, otherwise the specified weights are used to compute a weighted arithmetic mean.

We believe this function should be added to the package fitdistrplus. Until it is accepted and incorporated into that package, it will remain in the package BMT. This function is internally called in BMTfit.mpse.

mpsedist 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 value of the optimization objective function at the solution found.
`loglik`	the log-likelihood.
`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`	the name of the optimization function used.
`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.
`optim.method`	when `optim` is used, the name of the algorithm used, `NULL` otherwise.
`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 optimization 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.

Camilo Jose Torres-Jimenez [aut,cre] [email protected]

Based on the function mledist of the R package: fitdistrplus

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

Functions checkparam and start.arg.default are needed and were copied from the same package (fitdistrplus version: 1.0-9).

Cheng, R. and N. Amin (1983). Estimating parameters in continuous univariate distributions with a shifted origin. Journal of the Royal Statistical Society. Series B (Methodological), 394-403.

Ranneby, B. (1984). The maximum spacing method. an estimation method related to the maximum likelihood method. Scandinavian Journal of Statistics, 93-112.

mqdedist, mledist, mmedist, qmedist, mgedist, and optim.

# (1) basic fit of a normal distribution 
set.seed(1234)
x1 <- rnorm(n = 100)
mean(x1); sd(x1)
mpse1 <- mpsedist(x1, "norm")
mpse1$estimate

# (2) defining your own distribution functions, here for the Gumbel 
# distribution for other distributions, see the CRAN task view dedicated 
# to probability distributions
dgumbel <- function(x, a, b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b))
pgumbel <- function(q, a, b) exp(-exp((a-q)/b))
qgumbel <- function(p, a, b) a-b*log(-log(p))
mpse1 <- mpsedist(x1, "gumbel", start = list(a = 10, b = 5))
mpse1$estimate

# (3) fit a discrete distribution (Poisson)
set.seed(1234)
x2 <- rpois(n = 30, lambda = 2)
mpse2 <- mpsedist(x2, "pois")
mpse2$estimate

# (4) fit a finite-support distribution (beta)
set.seed(1234)
x3 <- rbeta(n = 100, shape1 = 5, shape2 = 10)
mpse3 <- mpsedist(x3, "beta")
mpse3$estimate

# (5) fit frequency distributions on USArrests dataset.
x4 <- USArrests$Assault
mpse4pois <- mpsedist(x4, "pois")
mpse4pois$estimate
mpse4nbinom <- mpsedist(x4, "nbinom")
mpse4nbinom$estimate

# (6) weighted fit of a normal distribution 
set.seed(1234)
w1 <- runif(101)
mpse1 <- mpsedist(x1, "norm", weights = w1)
mpse1$estimate