mat {analogue} | R Documentation |
Modern Analogue Technique (MAT) transfer function models for palaeoecology. The fitted values are the, possibly weighted, averages of the environment for the k-closest modern analogues. MAT is a k-NN method.
mat(x, ...) ## Default S3 method: mat(x, y, method = c("euclidean", "SQeuclidean", "chord", "SQchord", "bray", "chi.square", "SQchi.square", "information", "chi.distance", "manhattan", "kendall", "gower", "alt.gower", "mixed"), kmax, ...) ## S3 method for class 'formula' mat(formula, data, subset, na.action, method = c("euclidean", "SQeuclidean", "chord", "SQchord", "bray", "chi.square", "SQchi.square", "information", "chi.distance", "manhattan", "kendall", "gower", "alt.gower", "mixed"), model = FALSE, ...) ## S3 method for class 'mat' fitted(object, k, weighted = FALSE, ...) ## S3 method for class 'mat' residuals(object, k, weighted = FALSE, ...)
x |
a data frame containing the training set data, usually species data. |
y |
a vector containing the response variable, usually
environmental data to be predicted from |
formula |
a symbolic description of the model to be fit. The details of model specification are given below. |
data |
an optional data frame, list or environment (or object
coercible by |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
na.action |
a function which indicates what should happen when
the data contain |
method |
a character string indicating the dissimilarity (distance) coefficient to be used to define modern analogues. See Details, below. |
model |
logical; If |
kmax |
numeric; limit the maximum number of analogues considered
during fitting. By default, |
object |
an object of class |
k |
numeric; the k-closest analogue models' for which fitted values and residuals are returned. Overides the default stored in the object. |
weighted |
logical; should weighted averages be used instead of simple averages? |
... |
arguments can be passed to |
The Modern Analogue Technique (MAT) is perhaps the simplest of the transfer function models used in palaeoecology. An estimate of the environment, x, for the response for a fossil sample, y, is the, possibly weighted, mean of that variable across the k-closest modern analogues selected from a modern training set of samples. If used, weights are the reciprocal of the dissimilarity between the fossil sample and each modern analogue.
A typical model has the form response ~ terms
where
response
is the (numeric) response data frame and terms
is a series of terms which specifies a linear predictor for
response
. A typical form for terms
is .
,
which is shorthand for "all variables" in data
. If .
is
used, data
must also be provided. If specific species
(variables) are required then terms
should take the form
spp1 + spp2 + spp3
.
Pairwise sample dissimilarity is defined by dissimilarity or
distance coefficients. A variety of coefficients are supported — see
distance
for details of the supported coefficients.
k is chosen by the user. The simplest choice for k is to evaluate the RMSE of the difference between the predicted and observed values of the environmental variable of interest for the training set samples for a sequence of models with increasing k. The number of analogues chosen is the value of k that has lowest RMSE. However, it should be noted that this value is biased as the data used to build the model are also used to test the predictive power.
An alternative approach is to employ an optimisation data set on which to evaluate the size of k that provides the lowest RMSEP. This may be impractical with smaller sample sizes.
A third option is to bootstrap re-sample the training set many times. At
each bootstrap sample, predictions for samples in the bootstrap test
set can be made for k = 1, ..., n, where n is the
number of samples in the training set. k can be chosen from the
model with the lowest RMSEP. See function bootstrap.mat
for
further details on choosing k.
The output from summary.mat
can be used to choose
k in the first case above. For predictions on an optimsation or
test set see predict.mat
. For bootstrap resampling of
mat
models, see bootstrap.mat.
The fitted values are for the training set and are taken as the, possibly weighted, mean of the environmental variable in question across the k-closest analogues. The fitted value for each sample does not include a contribution from itself — it is the closest analogue, having zero dissimilarity. This spurious distance is ignored and analogues are ordered in terms of the non-zero distances to other samples in the training set, with the k-closest contributing to the fitted value.
Typical usages for residuals.mat
are:
resid(object, k, weighted = FALSE, \dots)
mat
returns an object of class mat
with the following
components:
standard |
list; the model statistics based on simple averages of k-closest analogues. See below. |
weighted |
list; the model statistics based on weighted of k-closest analogues. See below. |
Dij |
matrix of pairwise sample dissimilarities for the training
set |
orig.x |
the original training set data. |
orig.y |
the original environmental data or response, |
call |
the matched function call. |
method |
the dissimilarity coefficient used. |
If model = TRUE
then additional components "terms"
and
"model"
are returned containing the terms
object
and model frame used.
fitted.mat
returns a list with the following components:
estimatednumeric; a vector of fitted values.
knumeric; this is the k-closest analogue model with lowest apparent RMSE.
weightedlogical; are the fitted values the weighted averages
of the environment for the k-closest analogues. If
FALSE
, the fitted values are the average of the environment
for the k-closest analogues.
The object returned by mat
contains lists "standard"
and
"weighted"
both with the following elements:
est
a matrix of estimated values for the training set
samples for models using k analogues, where k = 1,
..., n. n is the number of smaples in the training
set. Rows contain the values for each model of size k, with
colums containing the estimates for each training set sample.
resid
matrix; as for "est"
, but containing the
model residuals.
rmsep
vector; containing the leave-one-out root mean square
error or prediction.
avg.bias
vector; contains the average bias (mean of
residuals) for models using k analogues, where k = 1,
..., n. n is the number of smaples in the training set.
max.bias
vector; as for "avg.bias"
, but
containing the maximum bias statistics.
r.squared
vector; as for "avg.bias"
, but
containing the R^2 statistics.
Gavin L. Simpson
Gavin, D.G., Oswald, W.W., Wahl, E.R. and Williams, J.W. (2003) A statistical approach to evaluating distance metrics and analog assignments for pollen records. Quaternary Research 60, 356–367.
Overpeck, J.T., Webb III, T. and Prentice I.C. (1985) Quantitative interpretation of fossil pollen spectra: dissimilarity coefficients and the method of modern analogues. Quaternary Research 23, 87–108.
Prell, W.L. (1985) The stability of low-latitude sea-surface temperatures: an evaluation of the CLIMAP reconstruction with emphasis on the positive SST anomalies, Report TR 025. U.S. Department of Energy, Washington, D.C.
Sawada, M., Viau, A.E., Vettoretti, G., Peltier, W.R. and Gajewski, K. (2004) Comparison of North-American pollen-based temperature and global lake-status with CCCma AGCM2 output at 6 ka. Quaternary Science Reviews 23, 87–108.
summary.mat
, bootstrap.mat
for boostrap
resampling of MAT models, predict.mat
for making
predictions from MAT models, fitted.mat
and
resid.mat
for extraction of fitted values and residuals
from MAT models respectively. plot.mat
provides a
plot.lm
-like plotting tool for MAT models.
## Imbrie and Kipp Sea Surface Temperature data(ImbrieKipp) data(SumSST) data(V12.122) ## merge training set and core samples dat <- join(ImbrieKipp, V12.122, verbose = TRUE) ## extract the merged data sets and convert to proportions ImbrieKipp <- dat[[1]] / 100 ImbrieKippCore <- dat[[2]] / 100 ## fit the MAT model using the squared chord distance measure ik.mat <- mat(ImbrieKipp, SumSST, method = "chord") ik.mat ## model summary summary(ik.mat) ## fitted values fitted(ik.mat) ## model residuals resid(ik.mat) ## draw summary plots of the model par(mfrow = c(2,2)) plot(ik.mat) par(mfrow = c(1,1)) ## reconstruct for the V12.122 core data coreV12.mat <- predict(ik.mat, V12.122, k = 3) coreV12.mat summary(coreV12.mat) ## draw the reconstruction reconPlot(coreV12.mat, use.labels = TRUE, display.error = "bars", xlab = "Depth", ylab = "SumSST") ## fit the MAT model using the squared chord distance measure ## and restrict the number of analogues we fit models for to 1:20 ik.mat2 <- mat(ImbrieKipp, SumSST, method = "chord", kmax = 20) ik.mat2