Dear Stata and statistics experts,

`I am looking for a strategy to handle a large amount of panel data
``that features both truncation and gaps. In particular I would like to
``know how I might go about fitting a model to the data I have on hand.
``Important features of the data are as follows:
`

`1. It is population data generated from agent-based evolutionary
``simulations . Each trial population has a series of observations
``associated with it over the length of time that it was being run.
`

`2. To conserve memory and processing time two data collection
``shortcuts were used.
`

`2a. Summary statistics from the population were collected on the
``initial creation of the population, after running it for one
``generation, and again after the second generation. Following this the
``same statistics are collected every five generation until generation
``100 at which point the simulation of the population ends. If the
``population drops below two members then no more information is
``collected either (There is no single-agent reproduction).
`

`2b. If the population grew over 15000 members then summary statistics
``were collected in the generation in which this occurred and then the
``population was dropped.
`

`3. There are a collection of variables that need to be taken into
``account.
`

`3a. Some of these are fixed throughout the trial (These include things
``like the initial population size, the cost to live from generation to
``generation, and the cost to spawn with another agent).
`

`3b. Others change throughout the course of each simulation and are
``randomly distributed at the beginning (These are the behaviours that
``the agents exhibit under certain conditions. Over time as
``opportunities to express these behaviours present themselves agents
``with more good/useful beahviours get to spawn more, increasing the
``likelihood that these useful behaviours will become more prevalent in
``the population).
`

`In particular I have two worries. First, that as successful
``populations are truncated out, those that remain will bring down the
``mean. Second, that a combination of successful population truncating
``out and unsuccessful populations having few members with highly
``similar behaviour sets will skew any investigation into which
``behaviours are successful.
`

`Any suggestions regarding possible models or methods for handling this
``dataset or directions to possibly useful resources would be appreciated.
`
John Simpson
Department of Philosophy
University of Alberta, Canada
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