"fitted"(object, ..., type="lambda", dataonly=FALSE, new.coef=NULL, leaveoneout=FALSE, drop=FALSE, check=TRUE, repair=TRUE, dropcoef=FALSE)"ppm")
"lambda") or the trend
("trend").
TRUE, then values will only be computed
at the points of the data point pattern. If FALSE, then
values will be computed at all the points of the quadrature scheme
used to fit the model, including the points of the data point pattern.
coef(object).
TRUE the fitted value at each data
point will be computed using a leave-one-out method. See Details.
object. If there is any possibility that this object
has been restored from a dump file, or has otherwise lost track of
the environment where it was originally computed, set
check=TRUE.
object, if it is found to be damaged.
type="trend") the fitted spatial trend.Entries in this vector correspond to the quadrature points (data or
dummy points) used to fit the model. The quadrature points can be
extracted from object by union.quad(quad.ppm(object)).
object must be a fitted point process model
(object of class "ppm"). Such objects are produced by the
model-fitting algorithm ppm). This function evaluates the conditional intensity
$lambdahat(u,x)$
or spatial trend $bhat(u)$ of the fitted point process
model for certain locations $u$,
where x is the original point pattern dataset to which
the model was fitted.
The locations $u$ at which the fitted conditional intensity/trend
is evaluated, are the points of the
quadrature scheme used to fit the model in ppm.
They include the data points (the points of the original point pattern
dataset x) and other ``dummy'' points
in the window of observation.
If leaveoneout=TRUE, fitted values will be computed
for the data points only, using a ‘leave-one-out’ rule:
the fitted value at X[i] is effectively computed by
deleting this point from the data and re-fitting the model to the
reduced pattern X[-i], then predicting the value at
X[i]. (Instead of literally performing this calculation,
we apply a Taylor approximation using the leverage
computed in dfbetas.ppm.
The argument drop is explained in quad.ppm.
Use predict.ppm to compute the fitted conditional
intensity at other locations or with other values of the
explanatory variables.
ppm.object,
ppm,
predict.ppm
str <- ppm(cells ~x, Strauss(r=0.1))
lambda <- fitted(str)
# extract quadrature points in corresponding order
quadpoints <- union.quad(quad.ppm(str))
# plot conditional intensity values
# as circles centred on the quadrature points
quadmarked <- setmarks(quadpoints, lambda)
plot(quadmarked)
if(!interactive()) str <- ppm(cells ~ x)
lambdaX <- fitted(str, leaveoneout=TRUE)
Run the code above in your browser using DataLab