Case Deletion Effect Measure of Fitted Model
Computes the case deletion effect measure DFFIT
for a fitted model.
dffit(object, ...) ## S3 method for class 'ppm' dffit(object, ..., collapse = FALSE, dfb = NULL)
object |
A fitted model, such as a point process model
(object of class |
... |
Additional arguments passed to |
collapse |
Logical value specifying whether to collapse the vector-valued measure to a scalar-valued measure by adding all the components. |
dfb |
Optional. The result of |
The case deletion effect measure DFFIT is a model diagnostic
traditionally used for regression models. In that context,
DFFIT[i,j] is the negative change, in the value of the
jth term in the linear predictor, that would occur if the ith
data value was deleted. It is closely related to the
diagnostic DFBETA.
For a spatial point process model, dffit computes
the analogous spatial case deletion diagnostic, described in
Baddeley, Rubak and Turner (2019).
A measure (object of class "msr").
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
Baddeley, A., Rubak, E. and Turner, R. (2019) Leverage and influence diagnostics for Gibbs spatial point processes. Spatial Statistics 29, 15–48.
X <- rpoispp(function(x,y) { exp(3+3*x) })
fit <- ppm(X ~x+y)
plot(dffit(fit))
plot(dffit(fit, collapse=TRUE))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.