| 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)