Last chance! 50% off unlimited learning
Sale ends in
Given a point process model fitted to multiple point patterns, compute residuals for each pattern.
# S3 method for mppm
residuals(object, type = "raw", ...,
fittedvalues = fitted.mppm(object))Fitted point process model (object of class "mppm").
Ignored.
Type of residuals: either "raw", "pearson"
or "inverse". Partially matched.
Advanced use only. Fitted values of the model to be used in the calculation.
A list of signed measures (objects of class "msr")
giving the residual measure for each of the original point patterns.
See Details.
Baddeley et al (2005) defined residuals for the fit of
a point process model to spatial point pattern data.
For an explanation of these residuals, see the help file for
residuals.ppm.
This function computes the residuals
for a point process model fitted to multiple point patterns.
The object should be an object of class "mppm" obtained
from mppm.
The return value is a list.
The number of entries in the list equals the
number of point patterns in the original data. Each entry in the list
has the same format as the output of
residuals.ppm.
That is, each entry in the list is a signed measure (object of
class "msr") giving the residual measure for the corresponding
point pattern.
Baddeley, A., Turner, R., Moller, J. and Hazelton, M. (2005) Residual analysis for spatial point processes. Journal of the Royal Statistical Society, Series B 67, 617--666.
Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. London: Chapman and Hall/CRC Press.
# NOT RUN {
fit <- mppm(Bugs ~ x, hyperframe(Bugs=waterstriders))
r <- residuals(fit)
# compute total residual for each point pattern
rtot <- sapply(r, integral.msr)
# standardise the total residuals
areas <- sapply(windows.mppm(fit), area.owin)
rtot/sqrt(areas)
# }
Run the code above in your browser using DataLab