Leverage and Influence Measures for Spatial Point Process Model
Calculates all the leverage and
influence measures described in influence.ppm,
leverage.ppm and dfbetas.ppm.
ppmInfluence(fit,
what = c("leverage", "influence", "dfbetas"),
...,
iScore = NULL, iHessian = NULL, iArgs = NULL,
drop = FALSE,
fitname = NULL)fit |
A fitted point process model of class |
what |
Character vector specifying which quantities are to be calculated. Default is to calculate all quantities. |
... |
Ignored. |
iScore,iHessian |
Components of the score vector and Hessian matrix for the irregular parameters, if required. See Details. |
iArgs |
List of extra arguments for the functions |
drop |
Logical. Whether to include ( |
fitname |
Optional character string name for the fitted model |
This function calculates all the
leverage and influence measures
described in influence.ppm, leverage.ppm
and dfbetas.ppm.
When analysing large datasets, the user can
call ppmInfluence to perform the calculations efficiently,
then extract the leverage and influence values as desired.
For example the leverage can be extracted either as
result$leverage or leverage(result).
If the point process model trend has irregular parameters that were
fitted (using ippm)
then the influence calculation requires the first and second
derivatives of the log trend with respect to the irregular parameters.
The argument iScore should be a list,
with one entry for each irregular parameter, of R functions that compute the
partial derivatives of the log trend (i.e. log intensity or
log conditional intensity) with respect to each irregular
parameter. The argument iHessian should be a list,
with p^2 entries where p is the number of irregular
parameters, of R functions that compute the second order
partial derivatives of the
log trend with respect to each pair of irregular parameters.
A list containing the leverage and influence measures specified by
what. The result also belongs to the class "ppmInfluence".
Adrian Baddeley Adrian.Baddeley@curtin.edu.au
X <- rpoispp(function(x,y) { exp(3+3*x) })
fit <- ppm(X ~ x+y)
fI <- ppmInfluence(fit)
fitlev <- fI$leverage
fitlev <- leverage(fI)
fitinf <- fI$influence
fitinf <- influence(fI)
fitdfb <- fI$dfbetas
fitdfb <- dfbetas(fI)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.