predict.mat {analogue}  R Documentation 
Predicted values based on a MAT model object.
## S3 method for class 'mat'
predict(object, newdata, k, weighted = FALSE,
bootstrap = FALSE, n.boot = 1000,
probs = c(0.01, 0.025, 0.05, 0.1), ...)
object 
an object of 
newdata 
data frame; required only if predictions for some new
data are required. Mst have the same number of columns, in same
order, as 
k 
number of analogues to use. If missing, 
weighted 
logical; should the analysis use the weighted mean of environmental data of analogues as predicted values? 
bootstrap 
logical; should bootstrap derived estimates and
sample specific errors be calculatedignored if 
n.boot 
numeric; the number of bootstrap samples to take. 
probs 
numeric; vector of probabilities with values in [0,1]. 
... 
arguments passed to of from other methods. 
This function returns one or more of three sets of results depending on the supplied arguments:
the fitted values of the mat
model are returned if newdata
is missing.
the predicted values for some new samples
are returned if newdata
is supplied. Summary model
statistics and estimated values for the training set are also
returned.
if newdata
is supplied and bootstrap = TRUE
, the predicted values for
newdata
plus bootstrap estimates and standard errors for the
new samples and the training set are returned.
The latter is simply a wrapper for bootstrap(model, newdata,
...)
 see bootstrap.mat
.
A object of class predict.mat
is returned if newdata
is
supplied, otherwise an object of fitted.mat
is
returned. If bootstrap = FALSE
then not all components will be
returned.
observed 
vector of observed environmental values. 
model 
a list containing the model or nonbootstrapped estimates for the training set. With the following components:

bootstrap 
a list containing the bootstrap estimates for the training set. With the following components:

sample.errors 
a list containing the bootstrapderived sample specific errors for the training set. With the following components:

weighted 
logical; whether the weighted mean was used instead of the mean of the environment for kclosest analogues. 
auto 
logical; whether 
n.boot 
numeric; the number of bootstrap samples taken. 
predictions 
a list containing the model and bootstrapderived estimates for the new data, with the following components:

method 
the dissimilarity measure used to fit the underlying MAT models. 
quantiles 
probability quantiles of the pairwise dissimilarities computed from the training set. 
minDC 
smallest distances between each sample in 
Dij 
dissimilarities between 
Gavin L. Simpson
Birks, H.J.B., Line, J.M., Juggins, S., Stevenson, A.C. and ter Braak, C.J.F. (1990). Diatoms and pH reconstruction. Philosophical Transactions of the Royal Society of London; Series B, 327; 263–278.
## Imbrie and Kipp example
## load the example data
data(ImbrieKipp)
data(SumSST)
data(V12.122)
## merge training and test set on columns
dat < join(ImbrieKipp, V12.122, verbose = TRUE)
## extract the merged data sets and convert to proportions
ImbrieKipp < dat[[1]] / 100
V12.122 < dat[[2]] / 100
## fit the MAT model using the chord distance measure
(ik.mat < mat(ImbrieKipp, SumSST, method = "chord"))
## predict for V12.122 data
predict(ik.mat, V12.122)