Description Usage Arguments Value Author(s)
Generic Method for Computing and Organizing Simulated
Quantities of Interest 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.
1 2 |
obj |
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) particular computations of quantities of interest |
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 |
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
|
x1 |
the optional
|
call |
the options
selected for |
zelig.call |
the original
command and options for |
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 |
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. |
Matt Owen mowen@iq.harvard.edu, Olivia Lau and Kosuke Imai
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