@param formula a symbolic representation of the model to be
estimated, in the form y ~ x1 + x2, where y is the
dependent variable and x1 and x2 are the explanatory
variables, and y, x1, and x2 are contained in the
same dataset. (You may include more than two explanatory variables,
of course.) The + symbol means ``inclusion'' not
``addition.'' You may also include interaction terms and main
effects in the form x1*x2 without computing them in prior
steps; I(x1*x2) to include only the interaction term and
exclude the main effects; and quadratic terms in the form
I(x1^2).
the name of a statistical model to estimate. For a list of other supported models and their documentation see: http://docs.zeligproject.org/articles/.
the name of a data frame containing the variables
referenced in the formula or a list of multiply imputed data frames
each having the same variable names and row numbers (created by
Amelia or to_zelig_mi).
additional arguments passed to zelig,
relevant for the model to be estimated.
a factor variable contained in data. If supplied,
zelig will subset
the data frame based on the levels in the by variable, and
estimate a model for each subset. This can save a considerable amount of
effort. You may also use by to run models using MatchIt
subclasses.
If is set to 'TRUE' (default), the model citation will be printed to the console.
point at which the dependent variable is censored from below.
If the dependent variable is only censored from above, set below = -Inf.
The default value is 0.
point at which the dependent variable is censored from above.
If the dependent variable is only censored from below, set above = Inf.
The default value is Inf.
Depending on the class of model selected, zelig will return
an object with elements including coefficients, residuals,
and formula which may be summarized using
summary(z.out) or individually extracted using, for example,
coef(z.out). See
http://docs.zeligproject.org/articles/getters.html for a list of
functions to extract model components. You can also extract whole fitted
model objects using from_zelig_model.
Additional parameters avaialable to this model include:
weights: vector of weight values or a name of a variable in the dataset by which to weight the model. For more information see: http://docs.zeligproject.org/articles/weights.html.
burnin: number of the initial MCMC iterations to be discarded (defaults to 1,000).
mcmc: number of the MCMC iterations after burnin (defaults to 10,000).
thin: thinning interval for the Markov chain. Only every thin-th draw from the Markov chain is kept. The value of mcmc must be divisible by this value. The default value is 1.
verbose: defaults to FALSE. If TRUE, the progress of the sampler (every 10%) is printed to the screen.
seed: seed for the random number generator. The default is NA which
corresponds to a random seed of 12345.
beta.start: starting values for the Markov chain, either a scalar or
vector with length equal to the number of estimated coefficients. The default is
NA, such that the maximum likelihood estimates are used as the starting values.
Use the following parameters to specify the model's priors:
b0: prior mean for the coefficients, either a numeric vector or a scalar. If a scalar value, that value will be the prior mean for all the coefficients. The default is 0.
B0: prior precision parameter for the coefficients, either a square matrix (with the dimensions equal to the number of the coefficients) or a scalar. If a scalar value, that value times an identity matrix will be the prior precision parameter. The default is 0, which leads to an improper prior.
c0: c0/2 is the shape parameter for the Inverse Gamma prior on the variance of the disturbance terms.
d0: d0/2 is the scale parameter for the Inverse Gamma prior on the variance of the disturbance terms.
Vignette: http://docs.zeligproject.org/articles/zelig_probitsurvey.html
# NOT RUN {
data(api, package="survey")
z.out1 <- zelig(enroll ~ api99 + yr.rnd ,
model = "poisson.survey", data = apistrat)
summary(z.out1)
x.low <- setx(z.out1, api99= quantile(apistrat$api99, 0.2))
x.high <- setx(z.out1, api99= quantile(apistrat$api99, 0.8))
s.out1 <- sim(z.out1, x=x.low, x1=x.high)
summary(s.out1)
plot(s.out1)
# }
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