If an experiment generates a set of n observations that have been ordered such that
x1 =< x2 =< x3 ... =< xn
then a histogram consists of the frequencies with which the observations accumulate within m cells defined by m sets of lower limits Li and upper limits Ui where
L1 =< x1, ..., xn =< Um
L1 < U1 = L2 < U2 = L3 ... < Um.
This program simulates data for such histograms in order to study the fitting of probability models to histogram data types.
In particular it simulates flow cytometry data in order to explore use of the program CSAFIT, which uses spline smoothing then nonlinear regression to fit flow cytometry profiles according to the linear shift and stretch model. This is how to use the program.
The model used in this program is a normal distribution with parameters mu and sigma. The error added is a % constant relative error, that is a a constant coefficient of variation. Default settings are provided (to help you get started) and you should try beginning with these set values. For details see
Bardsley et al J. Immunol. Meth. 153, 235-247 (1992) and
Bardsley and Kyprianou J. Math. Biol.(1996) 34:271-296.