Warning: summary does not work with timeseries models after
simulation.
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 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. For example, to run the same model on all fifty states, you could
use: z.out <- zelig(y ~ x1 + x2, data = mydata, model = 'ls',
by = 'state') 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.
The name of the variable containing the time indicator. This should be passed in as a string. If this variable is not provided, Zelig will assume that the data is already ordered by time.
Name of a variable that denotes the cross-sectional element of the data, for example,
country name in a dataset with time-series across different countries. As a variable name,
this should be in quotes. If this is not provided, Zelig will assume that all observations
come from the same unit over time, and should be pooled, but if provided, individual models will
be run in each cross-section.
If cs is given as an argument, ts must also be provided. Additionally, by
must be NULL.
A vector of length 3 passed in as c(p,d,q) where p represents the order of the
autoregressive model, d represents the number of differences taken in the model, and q represents
the order of the moving average model.
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.
Currently only the Reference class syntax for time series. This model does not accept Bootstraps or weights.
Vignette: http://docs.zeligproject.org/articles/zelig_ar.html
# NOT RUN {
data(seatshare)
subset <- seatshare[seatshare$country == "UNITED KINGDOM",]
ts.out <- zelig(formula = unemp ~ leftseat, model = "ar", ts = "year", data = subset)
summary(ts.out)
# }
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