Zelig (version 3.5.1)

sim: 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

sim(object, x = NULL, ...)
## S3 method for class 'default':
sim(object, x=NULL, 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

prev

a previous setx object to use to simulate

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, sim will sample

cond.data

specify conditional data

...

additional optional arguments passed to boot.

Value

• The output stored in s.out varies by model. Use the names command to view the output stored in s.out. Common elements include:
• x
  the setx values for the explanatory variables, used to calculate the quantities of interest (expected values, predicted values, etc.).
• x1
  the optional setx object used to simulate first differences, and other model-specific quantities of interest, such as risk-ratios.
• call
  the options selected for sim, used to replicate quantities of interest.
• zelig.call
  the original command and options for zelig, used to replicate analyses.
• num
  the number of simulations requested.
• par
  the parameters (coefficients, and additional model-specific parameters). You may wish to use the same set of simulated parameters to calculate quantities of interest rather than simulating another set.
• qi$ev
  simulations of the expected values given the model and x.
• qi$pr
  simulations of the predicted values given by the fitted values.
• qi$fd
  simulations of the first differences (or risk difference for binary models) for the given x and x1. The difference is calculated by subtracting the expected values given x from the expected values given x1. (If do not specify x1, you will not get first differences or risk ratios.)
• qi$rr
  simulations of the risk ratios for binary and multinomial models. See specific models for details.
• qi$ate.ev
  simulations of the average expected treatment effect for the treatment group, using conditional prediction. Let $t_i$ be a binary explanatory variable defining the treatment ($t_i=1$) and control ($t_i=0$) groups. Then the average expected treatment effect for the treatment group is $$\frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) - E[Y_i(t_i=0)] \mid t_i=1 \,],$$ where $Y_i(t_i=1)$ is the value of the dependent variable for observation $i$ in the treatment group. Variation in the simulations are due to uncertainty in simulating $E[Y_i(t_i=0)]$, the counterfactual expected value of $Y_i$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_i=0$.
• qi$ate.pr
  simulations of the average predicted treatment effect for the treatment group, using conditional prediction. Let $t_i$ be a binary explanatory variable defining the treatment ($t_i=1$) and control ($t_i=0$) groups. Then the average predicted treatment effect for the treatment group is $$\frac{1}{n}\sum_{i=1}^n [ \, Y_i(t_i=1) - \widehat{Y_i(t_i=0)} \mid t_i=1 \,],$$ where $Y_i(t_i=1)$ is the value of the dependent variable for observation $i$ in the treatment group. Variation in the simulations are due to uncertainty in simulating $\widehat{Y_i(t_i=0)}$, the counterfactual predicted value of $Y_i$ for observations in the treatment group, under the assumption that everything stays the same except that the treatment indicator is switched to $t_i=0$.
• In the case of censored $Y$ in the exponential, Weibull, and lognormal models, sim first imputes the uncensored values for $Y$ before calculating the ATE.

  You may use the $ operator to extract any of the above from s.out. For example, s.out$qi$ev extracts the simulated expected values.

See Also

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