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.-
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help for ^gllasim^
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.-
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Simulate command for gllamm
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---------------------------
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^gllasim^ varname [^if^ exp] [^in^ range] [, ^y^ ^u^ ^fac^ ^li^npred
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^mu^ ^out^come^(^#^)^ ^ab^ove^(^#^,^...^,^#^)^
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^adapt^ ^fsample^ ^nooff^set ^adoonly^ ^fr^om^(^matrix^)^
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^us(^varname^)^ ]
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where only one of ^u^ ^fac^ ^li^npred ^mu^ may be used at a time.
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^y^ can be specified in addition to one of the above.
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Description
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-----------
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^gllasim^ is a post-estimation command for @gllamm@. It simulates
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the responses from to the model just estimated.
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By default responses are simulated for the estimation sample.
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Use ^fsample^ to simulate responses for the full sample.
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If the data were collapsed and the ^weight()^ option used in
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^gllamm^, it does not make sense to simulate responses unless
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the data are first expanded. This is because each record
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in the collapsed dataset represents several units who happened
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to have the same response in the data and it would not
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make sense to simulate the same response for all these units.
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Options
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--------
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^y^ the simulated resonses are returned in "varname". This
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option is only necessary if ^u^, ^fac^, ^linpred^ or ^mu^
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are also specified.
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^u^ the simulated latent variables or random effects are
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returned in "varname"p1, "varname"p2, etc., where the
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order of the latent variables is the same as in the
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call to gllamm (in the order of the equations in the eqs()
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option). If the gllamm model includes equations for the
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latent variables (geqs and/or bmatrix), the simulated
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disturbances are returned.
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^fac^ If the gllamm model includes equations for the latent
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variables (^geqs()^ and/or ^bmatrix()^ options in ^gllamm^),
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^fac^ causes the simulated latent variables (e.g. factors)
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to be returned in "varname"p1, "varname"p2, etc. instead of
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the disturbances, that is, the latent variables on the
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left-hand side of the structural model.
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^linpred^ returns the linear predictor including the fixed
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and simulated random parts in "varname"p. The offset
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is included (if there is one in the gllamm model)
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unless the nooffset option is specified.
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^mu^ returns the expected value of the response conditional
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on the simulated values for the latent variables, e.g.
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a probability if the responses are dichotmous.
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^outcome(^#^)^ specifies the outcome for which the predicted
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probability should be returned (^mu^ option) if there
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is a nominal response and the ^expanded()^ option has not
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been used in ^gllamm^ (with the ^expanded()^ option, predicted
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probabilities are returned for all outcomes).
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^above(^#^,^...^,^#^)^ specifies the events for which the
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predicted probabilities should be returned (^mu^ option)
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if there are ordinal responses. The probability of
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a value higher than that specified is returned for each
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ordinal response. A single number can be given for all
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ordinal responses.
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^nooffset^ can be used together with the ^linpred^ and ^mu^
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options to exclude the offset from the simulated value.
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It will only make a difference if the ^offset()^ option
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was used in gllamm.
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^fsample^ causes gllasim to simulate values for the
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full sample (except observations exluded due to the
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if and in options), not just the estimation sample.
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^adoonly^ causes all gllamm to use only ado-code.
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This option is not necessary if ^gllamm^ was run with the
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adoonly option.
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^from(^matrix^)^ specifies a matrix of parameters for which
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the predictions should be made. The column and equation
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names will be ignored. Without this option, the parameter
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estimates from the last gllamm model will be used.
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^us(^varname^)^ specifies that, instead of simulating the
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latent variables, gllasim should use the variables in
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"varname"1, "varname"2, etc.
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Examples
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--------
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Estimate parameters of a three level logistic regression model:
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. ^gllamm resp x, i(id school) adapt trace family(binom)^
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Simulate the random intercepts
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. ^gllasim int, u^
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Simulate the responses
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. ^gllasim y^
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Simulate responses when the latent variables are equal to the values
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previously simulated (note that ^gllasim int, u^ above stored the
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random intercepts in intp1 and intp2):
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. ^gllasim y1, us(intp)^
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Simulate predicted probabilities, i.e. predicted probabilities for
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simulated values of the latent variables:
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. ^gllasim prob, mu^
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Author
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------
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Sophia Rabe-Hesketh (sophiarh@@berkeley.edu)
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as part of joint work with Andrew Pickles and Anders Skrondal.
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Web-page
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--------
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http://www.gllamm.org
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References
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----------
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Rabe-Hesketh, S., Skrondal, A. and Pickles, A. (2004). GLLAMM Manual.
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U.C. Berkeley Division of Biostatistics Working Paper Series.
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Working Paper 160.
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Also see
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--------
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On-line: help for @gllamm@, @gllapred@
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