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.
sim(
obj,
x,
x1,
y = NULL,
num = 1000,
bootstrap = F,
bootfn = NULL,
cond.data = NULL,
...
)
output object from zelig
values of explanatory variables used for simulation,
generated by setx
. Not if ommitted, then sim
will look for
values in the reference class object
optional values of explanatory variables (generated by a
second call of setx
)
particular computations of quantities of interest
a parameter reserved for the computation of particular quantities of interest (average treatment effects). Few models currently support this parameter
an integer specifying the number of simulations to compute
currently unsupported
currently unsupported
currently unsupported
arguments reserved future versions of Zelig
The output stored in s.out
varies by model. Use the
names
function to view the output stored in s.out
.
Common elements include:
the setx
values for the explanatory variables,
used to calculate the quantities of interest (expected values,
predicted values, etc.).
the optional setx
object used to simulate
first differences, and other model-specific quantities of
interest, such as risk-ratios.
the options selected for sim
, used to
replicate quantities of interest.
the original function and options for
zelig
, used to replicate analyses.
the number of simulations requested.
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.
simulations of the expected values given the
model and x
.
simulations of the predicted values given by the fitted values.
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.)
simulations of the risk ratios for binary and multinomial models. See specific models for details.
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 \(t_i=0\).
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 \(t_i=0\).
This documentation describes the sim
Zelig 4 compatibility wrapper
function.