Bayesian Logit Regression
a symbolic representation of the model to be
estimated, in the form
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
additional arguments passed to
a factor variable contained in
If is set to 'TRUE' (default), the model citation will be printed to the console.
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:
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
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
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.
Use the following arguments to specify optional output for the model:
bayes.resid: defaults to FALSE. If TRUE, the latent
Bayesian residuals for all observations are returned. Alternatively,
users can specify a vector of observations for which the latent residuals should be returned.
Depending on the class of model selected,
zelig will return
an object with elements including
formula which may be summarized using
summary(z.out) or individually extracted using, for example,
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
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