sim {Zelig}R Documentation

Simulating Quantities of Interest

Description

Simulate quantities of interest from the estimated model output from zelig() given specified values of explanatory variables established in setx(). For classical maximum likelihood models, sim() uses asymptotic normal approximation to the log-likelihood. For Bayesian models, Zelig simulates quantities of interest from the posterior density, whenever possible. For robust Bayesian models, simulations are drawn from the identified class of Bayesian posteriors. Alternatively, you may generate quantities of interest using bootstrapped parameters.

Usage

s.out <- sim(object, x, x1 = NULL, num = c(1000, 100), prev = NULL, 
             bootstrap = FALSE,  bootfn = NULL, cond.data = NULL, ...)

Arguments

object the output object from zelig.
x values of explanatory variables used for simulation, generated by setx.
x1 optional values of explanatory variables (generated by a second call of setx), used to simulate first differences and risk ratios. (Not available for conditional prediction.)
num the number of simulations, i.e., posterior draws. If the num argument is omitted, sim draws 1,000 simulations by if bootstrap = FALSE (the default), or 100 simulations if bootstrap = TRUE. You may increase this value to improve accuracy. (Not available for conditional prediction.)
bootstrap a logical value indicating if parameters should be generated by re-fitting the model for bootstrapped data, rather than from the likelihood or posterior. (Not available for conditional prediction.)
bootfn a function which governs how the data is sampled, re-fits the model, and returns the bootstrapped model parameters. If bootstrap = TRUE and bootfn = NULL, \lnk{sim} will sample observations from the original data (with replacement) until it creates a sampled dataset with the same number of observations as the original data. Alternative bootstrap methods include sampling the residuals rather than the observations, weighted sampling, and parametric bootstrapping. (Not available for conditional prediction.)
cond.data a data frame, identical to the data argument in setx. Required for conditional prediction with the exponential, Weibull, and lognormal models.
... additional optional arguments passed to boot.

Author(s)

Kosuke Imai <kimai@princeton.edu>; Gary King <king@harvard.edu>; Olivia Lau <olau@fas.harvard.edu>

See Also

The full Zelig at http://gking.harvard.edu/zelig, and boot.


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