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253 lines
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253 lines
9.5 KiB
Plaintext
.-
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help for ^gllapred^
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.-
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Predict command for gllamm
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---------------------------
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^gllapred^ varname [^if^ exp] [^in^ range] [,^u^ ^fac^ ^p^ ^xb^
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^ustd^ ^co^oksd ^li^npred ^mu^ ^ma^rginal ^us(^varname^)^
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^out^come^(^#^)^ ^ab^ove^(^#^,^...^,^#^)^
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^pe^arson ^d^eviance ^a^nscombe
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^s^ ^ll^ ^fsample^ ^nooff^set
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^adapt^ ^adoonly^ ^fr^om^(^matrix^)^ ]
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where only one of ^xb^ ^u^ ^fac^ ^p^ ^li^npred ^mu^
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^pe^arson ^d^eviance ^a^nscombe ^s^ ^ll^
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may be specified at a time.
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Description
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-----------
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^gllapred^ is a prediction command for @gllamm@. It computes
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-- Posterior means (empirical Bayes predictions) and standard
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deviations of the latent variables or random effects for models
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estimated using gllamm (see ^u^ and ^fac^ options).
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-- Posterior probabilities for two level models with discrete
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latent variables or random effects (see ^p^ option).
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-- The fixed part of the linear predictor (^xb^ option) or the
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entire linear predictor (^linpred^ option) with empirical Bayes
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estimates substituted for the latent variables or random effects.
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-- The expectation of the response (see ^mu^ option). By default,
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the expectation is with respect to the posterior distribution
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of the latent variables, but the ^marginal^ option gives the
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expectation with respect to the prior distribution.
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The ^us()^ option can be used to get the conditional expectation
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for specified values of the latent variables.
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--- Pearson, Deviance or Anscombe residuals. By default,
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the posterior expectation, but the ^us^ option gives the
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residuals for specified values of the latent variables.
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-- The level 1 standard deviation (see ^s^ option).
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-- Log-likelihood contributions of the highest level clusters
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(see ^ll^ option).
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In some cases the log-likelihood is also returned.
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By default prediction is restricted to the estimation sample.
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In this case (and if the ^if^ and ^in^ options are not specified),
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the log-likelihood returned by gllapred should be the same
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as that previously returned by gllamm.
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Options
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--------
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^u^ the posterior means and standard deviation of the latent
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variables or random effects are returned in "varname"m1,
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"varname"m2, etc., and "varname"s1, "varname"s2, etc.,
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respectively, where the order of the latent variables is
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the same as in the call to gllamm (in the order of the
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equations in the eqs() option). In the case of continuos
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latent variables, the number of quadrature points used
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is also the same as in the previous call to gllamm. If
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the gllamm model includes equations for the latent variables
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(geqs and/or bmatrix), the posterior means and standard
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deviations of the disturbances are returned.
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^corr^ the posterior correlations of the random effects
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or latent variables are returned in "varname"c21, etc.
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This option only works together with the ^u^ option. If
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the model includes equations for the latent variables,
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posterior correlations of the disturbances are calculated.
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^fac^ If the gllamm model includes equations for the latent
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variables (^geqs()^ and/or ^bmatrix()^), ^fac^ causes
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predictions of the latent variables (e.g. factors) to be
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returned in "varname"m1, "varname"m2, etc. instead of the
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disturbances. In other words, predictions of the latent
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variables on the left-hand side of the equations are returned.
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^p^ can only be used for two-level models estimated using the
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ip(f) option. gllapred returns the posterior probabilities
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in "varname"1, "varname"2, etc., giving the probabilities
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of classes 1,2, etc. gllapred also prints out the (prior)
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probability and location matrices to help interpret the
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posterior probabilities.
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^xb^ the linear predictor for the fixed effects is returned. This
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includes the offset (if there is one in the gllamm model)
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unless the ^nooffset^ option is specified.
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^ustd^ standardized posterior mean - approximate sampling
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standard deviation is used, sqrt(prior var. - posterior var.)
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^cooksd^ Cook's distances for the top-level units.
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^linpred^ returns the linear predictor including both the fixed
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and random parts where posterior means are substituted
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for the latent variables or random effects in the random
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part. The offset is included (if there is one in the
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gllamm model) unless the ^nooffset^ option is specified.
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^mu^ returns the expecation of the response, for example
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the predicted probability in the case of dichotomous
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responses. By default, the expectation is with respect
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to the posterior distribution of the latent variables,
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but see ^marginal^ and ^us()^ options. The offset is included
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(if there is one in the gllamm model) unless the
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^nooffset^ option is specified.
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^marginal^ together with the ^mu^ option gives the
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expectation of the response with respect to the prior
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distribution of the latent variables. This is useful
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for looking at the 'marginal' or population average
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effects of covariates.
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^us(^varname)^ can be used to specify values for the latent
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variables to calculate conditional quantities, such as
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the conditional mean of the responses (^mu^ option)
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given the values of the latent variables. Here varname
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specifies the stub-name (prefex) for the variables and
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^gllapred^ will look for "varname"1 "varname"2, etc.
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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. This option is not necessary if the
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^expanded()^ option was used in ^gllamm^ since in this
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case predicted 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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^pearson^ returns Pearson residuals. By default, the posterior
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expectation with respect to the latent variables is
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returned. The ^us()^ option can be used to obtain the
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conditional residual when certain values are substituted
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for the latent variables.
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^deviance^ returns deviance residuals. By default, the posterior
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expectation with respect to the latent variables is
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returned. The ^us()^ option can be used to obtain the
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conditional residual when certain values are substituted
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for the latent variables.
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^anscombe^ returns Anscombe residuals. By default, the posterior
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expectation with respect to the latent variables is
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returned. The ^us()^ option can be used to obtain the
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conditional residual when certain values are substituted
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for the latent variables.
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^s^ returns the scale or standard deviation. This is useful
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if the ^s()^ option was used in gllamm to specify level 1
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heteroscedasticity.
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^ll^ returns the log-likelihood contributions of the highest
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level (level L) units.
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^adapt^ if the gllamm command did not use the adapt option,
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gllapred will use ordinary quadrature for computing the
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posterior means and standard deviations unless the adapt
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option is used in the gllapred command.
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^fsample^ causes gllapred to return predictions 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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The returned log-likelihood may be missing since
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gllapred will not exclude observations with missing
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values on any of the variables used in the likelihood
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calculation. It is up to the user to exclude these
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observations using if or in.
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^nooffset^ can be used together with the ^xb^, ^linpred^ or
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^mu^ options to exclude the offset from the prediction.
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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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^adoonly^ causes all gllamm to use only ado-code. This option
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is not necessary if gllamm was run with the 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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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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Predict random intercepts using empirical Bayes:
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. ^gllapred int, u^
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Predict marginal probability that resp=1 (with respect to random effects):
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. ^gllapred prob, mu marginal^
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Predict conditional probability that resp==1 if random intercepts are 0:
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. ^gen z1 = 0^
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. ^gen z2 = 0^
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. ^gllapred prob, us(z)^
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Predict posterior mean of Pearson residual
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. ^gllapred res, pearson^
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Predict Pearson residual when random effects are equal to their posterior
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means (note that ^gllapred int, u^ above produced empirical Bayes
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predictions in intm1 intm2):
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. ^gllapred res, pearson us(intm)^
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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@, @gllasim@
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