Functions for reconstructing (predicting) environmental values from biological assemblages using Maximum Likelihood response Surfaces.
MLRC(y, x, check.data=TRUE, lean=FALSE, n.cut=5, verbose=TRUE, ...)MLRC.fit(y, x, n.cut=2, use.glm=FALSE, max.iter=50, lean=FALSE, verbose=FALSE, ...)
# S3 method for MLRC
predict (object, newdata=NULL, sse=FALSE, nboot=100,
match.data=TRUE, verbose=TRUE, ...)
# S3 method for MLRC
crossval(object, cv.method="loo", verbose=TRUE, ngroups=10,
nboot=100, h.cutoff=0, h.dist=NULL, ...)
# S3 method for MLRC
performance(object, ...)
# S3 method for MLRC
print(x, ...)
# S3 method for MLRC
summary(object, full=FALSE, ...)
# S3 method for MLRC
plot(x, resid=FALSE, xval=FALSE, xlab="", ylab="",
ylim=NULL, xlim=NULL, add.ref=TRUE, add.smooth=FALSE, ...)
# S3 method for MLRC
residuals(object, cv=FALSE, ...)
# S3 method for MLRC
coef(object, ...)
# S3 method for MLRC
fitted(object, ...)
Function MLRC returns an object of class MLRC with the following named elements:
Function crossval also returns an object of class MLRC and adds the following named elements:
predicted values of each training set sample under cross-validation.
prediction residuals.
If function predict is called with newdata=NULL it returns the fitted values of the original model, otherwise it returns a list with the following named elements:
predicted values for newdata.
If sample specific errors were requested the list will also include:
mean of the bootstrap estimates of newdata.
standard error of the bootstrap estimates for each new sample.
root mean squared error for the training set samples, across all bootstram samples.
standard error of prediction, calculated as the square root of v1^2 + v2^2.
Function performance returns a matrix of performance statistics for the MLRC model. See performance, for a description of the summary.
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.
logical to use glm to fit responses rather than internal code. Defaults to FALSE.
new biological data to be predicted.
maximum iterations of the logit regression algorithm.
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 plot residuals instead of fitted values.
logical to plot cross-validation estimates.
additional graphical arguments to plot.wa.
add 1:1 line on plot.
add loess smooth to plot.
cross-validation method, either "loo", "lgo", "bootstrap" or "h-block".
logical to show feedback during cross-validation.
number of bootstrap samples.
number of groups in leave-group-out cross-validation, or a vector contain leave-out group menbership.
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 MLRC Maximim likelihood reconstruction using response curves.
Function predict predicts values of the environemntal variable for newdata or returns the fitted (predicted) values from the original modern dataset if newdata is NULL. Variables are matched between training and newdata by column name (if match.data is TRUE). Use compare.datasets to assess conformity of two species datasets and identify possible no-analogue samples.
MLRC has methods fitted and rediduals that return the fitted values (estimates) and residuals for the training set, performance, which returns summary performance statistics (see below), coef which returns the species coefficients, and print and summary to summarise the output. MLRC also has a plot method that produces scatter plots of predicted vs observed measurements for the training set.
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.
data(IK)
spec <- IK$spec / 100
SumSST <- IK$env$SumSST
core <- IK$core / 100
fit <- MLRC(spec, SumSST)
fit
#predict the core
pred <- predict(fit, core)
#plot predictions - depths are in rownames
depth <- as.numeric(rownames(core))
plot(depth, pred$fit[, 1], type="b")
if (FALSE) {
# this is slow!
# cross-validate model
fit.cv <- crossval(fit, cv.method="loo", verbose=5)
# predictions with sample specific errors
pred <- predict(fit, core, sse=TRUE, nboot=1000, verbose=5)
}
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