Description Usage Arguments Details Value Author(s)
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 loglikelihood. 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.
1 2 3 4 5 6 7 8 9 10 11 
obj 
output object from 
x 
values of explanatory variables used for simulation,
generated by 
x1 
optional values of explanatory variables (generated by a
second call of 
y 
a parameter reserved for the computation of particular quantities of interest (average treatment effects). Few models currently support this parameter 
num 
an integer specifying the number of simulations to compute 
bootstrap 
currently unsupported 
bootfn 
currently unsupported 
cond.data 
currently unsupported 
... 
arguments reserved future versions of Zelig 
This documentation describes the sim
Zelig 4 compatibility wrapper
function.
The output stored in s.out
varies by model. Use the
names
function to view the output stored in s.out
.
Common elements include:
x 
the 
x1 
the optional 
call 
the options selected for 
zelig.call 
the original function and options for

num 
the number of simulations requested. 
par 
the parameters (coefficients, and additional modelspecific 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 
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 
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}∑_{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}∑_{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. 
Christopher Gandrud, Matt Owen, Olivia Lau and Kosuke Imai
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