Log-Normal Regression for Duration Dependent Variables
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.
defaults to FALSE. If TRUE, zelig() computes robust standard errors based on sandwich estimators (see and ) based on the options in cluster.
if robust = TRUE, you may select a variable to define groups of correlated observations. Let x3 be a variable that consists of either discrete numeric values, character strings, or factors that define strata. Then means that the observations can be correlated within the strata defined by the variable x3, and that robust standard errors should be calculated according to those clusters. If robust = TRUE but cluster is not specified, zelig() assumes that each observation falls into its own cluster.
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.
zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)The zelig function estimates a variety of statistical models
Additional parameters avaialable to many models 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.
bootstrap: logical or numeric. If FALSE don't use bootstraps to
robustly estimate uncertainty around model parameters due to sampling error.
If an integer is supplied, the number of boostraps to run.
For more information see:
http://docs.zeligproject.org/articles/bootstraps.html.
Vignette: http://docs.zeligproject.org/articles/zelig_lognorm.html
# NOT RUN {
library(Zelig)
data(coalition)
z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model ="lognorm", data = coalition)
summary(z.out)
# }
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