sim {Zelig} | R Documentation |
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.
s.out <- sim(object, x, x1 = NULL, num = c(1000, 100), prev = NULL, bootstrap = FALSE, bootfn = NULL, ...)
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 ,
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.) |
... |
additional optional arguments passed to
boot . |
The output stored in s.out
varies by model. Use the
names
command to view the output stored in s.out
.
Common elements include: normal-bracket109bracket-normal
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. |
normal-bracket109bracket-normal
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.
Kosuke Imai <kimai@princeton.edu>; Gary King <king@harvard.edu>; Olivia Lau <olau@fas.harvard.edu>
The full Zelig at http://gking.harvard.edu/zelig, and boot
.