stdError {analogue} | R Documentation |
Computes the weighted standard deviation of the environment for the k-closest analogues for each sample. This measure is proposed as a measure of reconstruction uncertainty for MAT models.
stdError(object, ...) ## S3 method for class 'mat': stdError(object, k, ...) ## S3 method for class 'mat': stdError(object, k, ...)
object |
Object for which the uncertainty measure is to be
computed. Currently methods for mat and
predict.mat . |
k |
numeric; how many analogues to take? If missing, the default,
k is chosen using getK . |
... |
Additional arguments passed to other methods. Currently not used. |
A named numeric vector of weighted standard deviations of the
environment for the k closest analogues used to compute the MAT
predicted values.
The returned vector has attributes "k"
and "auto"
,
indicating the number of analogues used and whether this was
determined from object
or supplied by the user.
Gavin L. Simpson
minDC
, mat
,
predict.mat
.
## Imbrie and Kipp Sea Surface Temperature data(ImbrieKipp) data(SumSST) data(V12.122) ## merge training set and core samples dat <- join(ImbrieKipp, V12.122, verbose = TRUE) ## extract the merged data sets and convert to proportions ImbrieKipp <- dat[[1]] / 100 ImbrieKippCore <- dat[[2]] / 100 ## fit the MAT model using the squared chord distance measure ik.mat <- mat(ImbrieKipp, SumSST, method = "SQchord") ## standard errors stdError(ik.mat) ## reconstruct for the V12-122 core data coreV12.mat <- predict(ik.mat, V12.122, k = 3) ## standard errors stdError(coreV12.mat)