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 40character 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 userdefined 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.