Description Usage Arguments Value See Also Examples
This function implements a limited-information maximum likelihood estimator for Poisson regression models. The estimator was described by Lambert, Brown, and Florax (2010).
1 |
formula |
A symbolic description of the model to be fit. The details of
model specification are given for |
data |
An optional data frame containing the variables in the model. By default the variables are taken from the environment which the function is called. |
listw |
A listw object created for example by |
method |
The method to be used for fitting the regression equation.
Defaults to "liml", a limited-information maximum likelihood. Other
options are "fiml" (full-information maximum likelihood), "model.matrix" to
return a model matrix, and "non-spatial", which will execute a non-spatial
Poisson regression (identical to |
... |
Further arguments passed to nlm. |
A list of class 'sarpoisson' containing the following components:
The estimated coefficient values.
The estimated mean of the poisson distribution.
Difference between estimated mean and observed value.
Degrees of freedom remaining in residuals.
Degrees of freedom in the null model.
Numerical log likelihood.
Rank of the model.
Model estimation call.
Complete results from nlm.
Fischer information matrix, obtained as inverse of Hessian.
The method used in maximum likelihood estimation.
Terms of the model frame
1 2 3 4 | summary(sarpoisson(crime_i ~ income + home_value, data = columbus_crime,
method = "non-spatial"))
summary(sarpoisson(crime_i ~ income + home_value, data = columbus_crime,
listw = columbus_neighbors, method = "fiml"))
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