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, ...)

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 |

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.