Variance-Covariance Matrix for a Fitted Point Process Model
Returns the variance-covariance matrix of the estimates of the parameters of a fitted point process model.
## S3 method for class 'ppm': vcov(object, \dots, what = "vcov", verbose = TRUE)
- A fitted point process model (an object of class
- Character string (partially-matched)
that specifies what matrix is returned.
"vcov"for the variance-covariance matrix,
"corr"for the correlation matrix, and
- Logical. If
TRUE, a message will be printed if various minor problems are encountered.
This function computes the asymptotic variance-covariance
matrix of the estimates of the canonical parameters in the
point process model
object. It is a method for the
object should be an object of class
ppm. The current implementation only works
for Poisson point processes.
The canonical parameters of the fitted model
are the quantities returned by
vcov calculates the variance-covariance matrix
for these parameters.
what provides three options:
[object Object],[object Object],[object Object]
The calculations are based on standard asymptotic theory for the maximum
In all cases, the observed Fisher information matrix of the fitted model
object is first computed, by
summing over the Berman-Turner quadrature points in the fitted model.
The asymptotic variance-covariance matrix is calculated as the inverse of the
observed Fisher information. The correlation matrix is then obtained
In all three cases, the result is a square matrix.
The rows and columns of the matrix correspond to the canonical
parameters given by
coef.ppm(object). The row and column
names of the matrix are also identical to the names in
verbose makes it possible to suppress some
- A square matrix.
X <- rpoispp(42) fit <- ppm(X, ~ x + y) vcov(fit) vcov(fit, what="Fish")