MLRC {rioja} | R Documentation |
Functions for reconstructing (predicting) environmental values from biological assemblages using Maximum Likelihood response Surfaces.
MLRC(y, x, check.data=TRUE, lean=FALSE, ...) MLRC.fit(y, x, n.cut=2, use.glm=FALSE, max.iter=50, lean=FALSE, ...) ## S3 method for class 'MLRC': predict (object, newdata=NULL, sse=FALSE, nboot=100, match.data=TRUE, verbose=TRUE, ...) ## S3 method for class 'MLRC': crossval(object, cv.method="loo", verbose=TRUE, ngroups=10, nboot=100, ...) ## S3 method for class 'MLRC': performance(object, ...) ## S3 method for class 'MLRC': print(x, ...) ## S3 method for class 'MLRC': summary(object, full=FALSE, ...) ## S3 method for class 'MLRC': plot(x, resid=FALSE, xval=FALSE, xlab="", ylab="", ylim=NULL, xlim=NULL, add.ref=TRUE, add.smooth=FALSE, ...) ## S3 method for class 'MLRC': residuals(object, ...) ## S3 method for class 'MLRC': coef(object, ...) ## S3 method for class 'MLRC': fitted(object, ...)
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 occurences will be excluded for 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 or integer to show feedback during cross-validaton. If TRUE print feedback every 50 cycles, if integer, use this value. |
nboot |
number of bootstrap samples. |
ngroups |
number of groups in leave-group-out cross-validation, or a vector contain leave-out group menbership. |
sse |
logical indicating that sample specific errors should be calculated. |
... |
additional arguments. |
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.
Function MLRC
returns an object of class MLRC
with the following named elements:
To do
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. |
fit |
predicted values for newdata . |
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.
Steve Juggins
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") ## Not run: # 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) ## End(Not run)