Functions for reconstructing (predicting) environmental values from biological assemblages using weighted averaging (LWR) regression and calibration.
LWR(y, x, FUN=WA, dist.method="sq.chord", k=30, lean=TRUE,
fit.model=TRUE, check.data=TRUE, verbose=TRUE, ...)# S3 method for LWR
predict (object, newdata=NULL, k = object$k, sse=FALSE,
nboot=100, match.data=TRUE, verbose=TRUE, lean=TRUE, ...)
# S3 method for LWR
crossval(object, k=object$k, cv.method="lgo", verbose=TRUE,
ngroups=10, nboot=100, h.cutoff=0, h.dist=NULL, ...)
# S3 method for LWR
performance(object, ...)
# S3 method for LWR
print(x, ...)
# S3 method for LWR
summary(object, full=FALSE, ...)
# S3 method for LWR
residuals(object, cv=FALSE, ...)
# S3 method for LWR
fitted(object, ...)
Function LWR returns an object of class LWR with the following named elements:
a data frame or matrix of biological abundance data.
a vector of environmental values to be modelled or an object of class LWR.
distance measure used to derfine closest analogues.
number of close analogues to use in calibration function.
calibration function (e.g. WA, WAPLS etc).
new biological data to be predicted.
TRUE fits model to training set. FALSE omist this step and builds a LWR object than can be used for prediction.
logical to perform simple checks on the input data.
logical to show head and tail of output in summaries.
logical indicate the function will match two species datasets by their column names. You should only set this to FALSE if you are sure the column names match exactly.
logical to exclude some output from the resulting models (used when cross-validating to speed calculations).
cross-validation method, either "lgo" or "bootstrap".
logical to show feedback during cross-validaton.
number of bootstrap samples.
number of groups in leave-group-out cross-validation.
cutoff for h-block cross-validation. Only training samples greater than h.cutoff from each test sample will be used.
distance matrix for use in h-block cross-validation. Usually a matrix of geographical distances between samples.
logical indicating that sample specific errors should be calculated.
logical to indicate model or cross-validation residuals.
additional arguments.
Steve Juggins
Function LWR performs ... To do.
WAPLS, MAT, and compare.datasets for diagnostics.