LWR: Weighted averaging (LWR) regression and calibration

In rioja: Analysis of Quaternary Science Data

Weighted averaging (LWR) regression and calibration

Description

Functions for reconstructing (predicting) environmental values from biological assemblages using weighted averaging (LWR) regression and calibration.

Usage

LWR(y, x, FUN=WA, dist.method="sq.chord", k=30, lean=TRUE, 
       fit.model=TRUE, check.data=TRUE, verbose=TRUE, ...)

## S3 method for class 'LWR'
 predict(object, newdata=NULL, k = object$k, sse=FALSE, 
      nboot=100, match.data=TRUE, verbose=TRUE, lean=TRUE, ...)

## S3 method for class 'LWR'
crossval(object, k=object$k, cv.method="lgo", verbose=TRUE, 
      ngroups=10, nboot=100, h.cutoff=0, h.dist=NULL, ...)

## S3 method for class 'LWR'
performance(object, ...)

## S3 method for class 'LWR'
print(x, ...)

## S3 method for class 'LWR'
summary(object, full=FALSE, ...)

## S3 method for class 'LWR'
residuals(object, cv=FALSE, ...)

## S3 method for class 'LWR'
fitted(object, ...)

Arguments

y	a data frame or matrix of biological abundance data.
x, object	a vector of environmental values to be modelled or an object of class LWR.
dist.method	distance measure used to derfine closest analogues.
k	number of close analogues to use in calibration function.
FUN	calibration function (e.g. WA, WAPLS etc).
newdata	new biological data to be predicted.
fit.model	TRUE fits model to training set. FALSE omist this step and builds a LWR object than can be used for prediction.
check.data	logical to perform simple checks on the input data.
full	logical to show head and tail of output in summaries.
match.data	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.
lean	logical to exclude some output from the resulting models (used when cross-validating to speed calculations).
cv.method	cross-validation method, either "lgo" or "bootstrap".
verbose	logical to show feedback during cross-validaton.
nboot	number of bootstrap samples.
ngroups	number of groups in leave-group-out cross-validation.
h.cutoff	cutoff for h-block cross-validation. Only training samples greater than h.cutoff from each test sample will be used.
h.dist	distance matrix for use in h-block cross-validation. Usually a matrix of geographical distances between samples.
sse	logical indicating that sample specific errors should be calculated.
cv	logical to indicate model or cross-validation residuals.
...	additional arguments.

Details

Function LWR performs ... To do.

Value

Function LWR returns an object of class LWR with the following named elements:

Author(s)

Steve Juggins

See Also

WAPLS, MAT, and compare.datasets for diagnostics.

rioja documentation built on Sept. 26, 2024, 5:07 p.m.