GSLIB Help: HISTSMTH

GSLIB Help Page: HISTSMTH

Description:

Creates a smooth distribution model constrained to a mean, variance, quantiles, and smoothmess
Parameters:

datafl: the data file with the raw (perhaps declustered) data.
icolvr and icolwt: the column location for the variable and the declustering weight (0 if none available).
tmin and tmax: all values strictly less than tmin and strictly greater than tmax are ignored.
title: a 40-character title for the top of the PostScript plot.
psfl: name for the PostScript output file.
nhist: number of histogram classes for the PostScript output file. nhist is typically set less than nz (see below) to obtain a reasonable histogram display superimposed on the smoothed distribution.
outfl: output file containing the smoothed distribution (evenly spaced z values and variable p values).
nz, zmin and zmax: the number N of evenly spaced z values for the smoothed histogram and the limits for the evenly spaced z values
ilog: =0 then an arithmetic scaling is used, =1 then a logarithmic scaling (base 10) is used.
maxpert, report, omin and seed: after maxpert x nz perturbations the program is stopped. After report x nz perturbations the program reports on the current objective function(s). When the normalized objective function reaches omin the program is stopped. The random number seed seed should be a large odd integer.
imean, ivari, ismth and iquan: flags for whether closeness to a target mean, closeness to a target variance, smoothness, and closeness to specified quantiles will be considered (1 = yes, 0 = no).
sclmean, sclvari, sclsmth and sclquan: user imposed weights which scale the weights that the program automatically calculates.
nsmooth: half of the smoothing window size.
mean and variance: target mean and variance (if set less than -999 then they will be calculated from the input data).
ndq: number of quantiles defined from the data (evenly spaced cumulative probability values).
nuq: number of quantiles defined by the user (nuq lines must follow with a cdf and a z value). The user-defined quantiles allows the user more control over ``peaks'' and ``valleys'' in the smoothed distribution. The user should choose these quantiles to be consistent with the quantiles defined from the data.