Functions for reconstructing (predicting) environmental values from biological assemblages using Maximum Likelihood response Surfaces.
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 MLRC2
predict (object, newdata=NULL, sse=FALSE, nboot=100,
match.data=TRUE, verbose=TRUE, ...)
# S3 method for MLRC2
performance(object, ...)
# S3 method for MLRC2
print(x, ...)
# S3 method for MLRC2
summary(object, full=FALSE, ...)
# S3 method for MLRC2
residuals(object, cv=FALSE, ...)
# S3 method for MLRC2
coef(object, ...)
# S3 method for MLRC2
fitted(object, ...)
Function MLRC2 returns an object of class MLRC2 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 wa.
cutoff value for number of occurrences. Species with fewer than n.cut occurrences will be excluded from the analysis.
to do
to do
logical to use gam to fit responses rather than internal code. Defaults to FALSE.
new biological data to be predicted.
logical to perform simple checks on the input 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.
logical to exclude some output from the resulting models (used when cross-validating to speed calculations).
logical to show head and tail of output in summaries.
logical to show feedback during cross-validation.
number of bootstrap samples.
logical indicating that sample specific errors should be calculated.
logical to indicate model or cross-validation residuals.
additional arguments.
Steve Juggins
Function MLRC2 Maximim likelihood reconstruction using 2D response curves.
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.
WA, MAT, performance, and compare.datasets for diagnostics.