MLRC2 {rioja}

Palaeoenvironmental reconstruction using Maximum Likelihood Response Surfaces

Description

Functions for reconstructing (predicting) environmental values from biological assemblages using Maximum Likelihood response Surfaces.

Usage

MLRC2(y, x, n.out=100, expand.grad=0.1, use.gam=FALSE, check.data=TRUE, 
       lean=FALSE, n.cut=5, verbose=TRUE, ...)

MLRC2.fit(y, x, n.out=100, expand.grad=0.1, use.gam=FALSE, check.data=TRUE, 
       lean=FALSE, n.cut=5, verbose=TRUE, ...)

## S3 method for class 'MLRC2'
 predict(object, newdata=NULL, sse=FALSE, nboot=100,
      match.data=TRUE, verbose=TRUE, ...)

## S3 method for class 'MLRC2'
performance(object, ...)

## S3 method for class 'MLRC2'
print(x, ...)

## S3 method for class 'MLRC2'
summary(object, full=FALSE, ...)

## S3 method for class 'MLRC2'
residuals(object, cv=FALSE, ...)

## S3 method for class 'MLRC2'
coef(object, ...)

## S3 method for class 'MLRC2'
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 wa.

n.cut

cutoff value for number of occurrences. Species with fewer than n.cut occurrences will be excluded from the analysis.

n.out

to do

expand.grad

to do

use.gam

logical to use gam to fit responses rather than internal code. Defaults to FALSE.

newdata

new biological data to be predicted.

check.data

logical to perform simple checks on the input data.

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).

full

logical to show head and tail of output in summaries.

verbose

logical to show feedback during cross-validation.

nboot

number of bootstrap samples.

sse

logical indicating that sample specific errors should be calculated.

cv

logical to indicate model or cross-validation residuals.

...

additional arguments.

Details

Function MLRC2 Maximim likelihood reconstruction using 2D response curves.

Value

Function MLRC2 returns an object of class MLRC2 with the following named elements:

Author(s)

Steve Juggins

References

Birks, H.J.B., Line, J.M., Juggins, S., Stevenson, A.C., & ter Braak, C.J.F. (1990) Diatoms and pH reconstruction. Philosophical Transactions of the Royal Society of London, B, 327, 263-278.

Juggins, S. (1992) Diatoms in the Thames Estuary, England: Ecology, Palaeoecology, and Salinity Transfer Function. Bibliotheca Diatomologica, Band 25, 216pp.

Oksanen, J., Laara, E., Huttunen, P., & Merilainen, J. (1990) Maximum likelihood prediction of lake acidity based on sedimented diatoms. Journal of Vegetation Science, 1, 49-56.

ter Braak, C.J.F. & van Dam, H. (1989) Inferring pH from diatoms: a comparison of old and new calibration methods. Hydrobiologia, 178, 209-223.

See Also

WA, MAT, performance, and compare.datasets for diagnostics.