| vcov.mppm {spatstat} | R Documentation |
Given a fitted multiple point process model, calculate the variance-covariance matrix of the parameter estimates.
## S3 method for class 'mppm' vcov(object, ..., what="vcov", err="fatal")
object |
A multiple point process model (object of class |
... |
Ignored. |
what |
Character string indicating which quantity should be calculated.
Either |
err |
Character string indicating what action to take if an error occurs.
Either |
This is a method for the generic function vcov.
The argument object should be a fitted multiple point process
model (object of class "mppm") generated by mppm.
The model must be a Poisson point process.
The variance-covariance matrix of the parameter estimates is computed using asymptotic theory for maximum likelihood.
If what="vcov" (the default), the variance-covariance matrix
is returned.
If what="corr", the variance-covariance matrix is normalised
to yield a correlation matrix, and this is returned.
If what="fisher", the Fisher information matrix is returned instead.
In all three cases, the rows and columns of the matrix correspond
to the parameters (coefficients) in the same order as in
coef{model}.
These calculations are not available if the model is not Poisson,
or if it was computed using gam. In such cases, the
argument err determines what will happen. If
err="fatal" an error will occur. If err="warn"
a warning will be issued and NA will be returned.
If err="null", no warning is issued, but NULL is returned.
A numeric matrix (or NA or NULL).
data(waterstriders) fit <- mppm(Wat ~x, data=hyperframe(Wat=waterstriders)) vcov(fit)