residuals.slrm | R Documentation |
Given a spatial logistic regression model fitted to a point pattern, compute the residuals for each pixel.
## S3 method for class 'slrm' residuals(object, type=c("raw", "deviance", "pearson", "working", "response", "partial", "score"), ...)
object |
The fitted point process model (an object of class |
type |
String (partially matched) indicating the type of residuals to be calculated. |
... |
Ignored. |
This function computes several kinds of residuals for the fit of a spatial logistic regression model to a spatial point pattern dataset.
The argument object
must be a fitted spatial logistic
regression model (object of class "slrm"
). Such objects are
created by the fitting algorithm slrm
.
The residuals are computed for each pixel that was used to fit the original model. The residuals are returned as a pixel image (if the residual values are scalar), or a list of pixel images (if the residual values are vectors).
The type of residual is chosen by the argument type
.
For a given pixel, suppose p is the fitted probability of presence of a point, and y is the presence indicator (equal to 1 if the pixel contains any data points, and equal to 0 otherwise). Then
type="raw"
or type="response"
specifies
the response residual
r = y - p
type="pearson"
is the Pearson residual
rP = (y-p)/sqrt(p * (1-p))
type="deviance"
is the deviance residual
rD = (-1)^(y+1) (-2(y log p + (1-y) log(1-p)))^(1/2)
type="score"
specifies the score residuals
rS = (y-p) x
where x
is the vector of canonical covariate values
for the pixel
type="working"
specifies the working residuals
as defined in residuals.glm
type="partial"
specifies the partial residuals
as defined in residuals.glm
A pixel image (if the residual values are scalar), or a list of pixel images (if the residual values are vectors).
residuals.glm
,
residuals.ppm
d <- if(interactive()) 128 else 32 H <- unmark(humberside) fit <- slrm(H ~ x + y, dimyx=d) plot(residuals(fit)) plot(residuals(fit, type="score"))
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