rioja (version 0.9-6)

MLRC: Palaeoenvironmental reconstruction using Maximum Likelihood Response Surfaces

Description

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

Usage

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=TRUE, ...)

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

## S3 method for class 'MLRC':
crossval(object, cv.method="loo", verbose=TRUE, ngroups=10,
      nboot=100, h.cutoff=0, h.dist=NULL, \dots)

## S3 method for class 'MLRC':
performance(object, \dots)

## S3 method for class 'MLRC':
print(x, \dots)

## S3 method for class 'MLRC':
summary(object, full=FALSE, \dots)

## S3 method for class 'MLRC':
plot(x, resid=FALSE, xval=FALSE, xlab="", ylab="", 
      ylim=NULL, xlim=NULL, add.ref=TRUE, add.smooth=FALSE, \dots)

## S3 method for class 'MLRC':
residuals(object, cv=FALSE, \dots)

## S3 method for class 'MLRC':
coef(object, \dots)

## S3 method for class 'MLRC':
fitted(object, \dots)

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.

use.glm

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

newdata

new biological data to be predicted.

max.iter

maximum iterations of the logit regression algorithm.

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.

resid

logical to plot residuals instead of fitted values.

xval

logical to plot cross-validation estimates.

xlab, ylab, xlim, ylim

additional graphical arguments to plot.wa.

add.ref

add 1:1 line on plot.

add.smooth

add loess smooth to plot.

cv.method

cross-validation method, either "loo", "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, or a vector contain leave-out group menbership.

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.

Value

• 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
  predicted values of each training set sample under cross-validation.
• residuals.cv
  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:
• fit
  predicted values for newdata.
• if sample specific errors were generated the list will also include:
• fit.boot
  mean of the bootstrap estimates of newdata.
• v1
  squared standard error of the bootstrap estimates for each new sample.
• v2
  mean squared error for the training set samples, across all bootstrap samples.
• SEP
  standard error of prediction, calculated as the square root of v1 + v2.
• Function performance returns a matrix of performance statistics for the MLRC model. See performance, for a description of the summary.

Details

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.

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.

Examples

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")

# 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)