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 mat . |
newdata |
data frame; required only if predictions for some new
data are required. Mst have the same number of columns, in same
order, as x in mat . See example below or
join for how to do this. If newdata not
provided, the fitted values are returned. |
k |
number of analogues to use. If missing, k is chosen
automatically as the k that achieves lowest RMSE. |
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 calculated-ignored if newdata is
missing. |
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:
mat
model are returned if newdata
is missing.newdata
is supplied. Summary model
statistics and estimated values for the training set are also
returned.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 non-bootstrapped
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 bootstrap-derived 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 k-closest analogues. |
auto |
logical; whether "k" was choosen automatically or
user-selected. |
n.boot |
numeric; the number of bootstrap samples taken. |
predictions |
a list containing the model and
bootstrap-derived estimates for the new data, with the following
components:
|
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.
## continue the RLGH and SWAP example from ?join example(join) ## fit the MAT model using the squared chord distance measure swap.mat <- mat(swapdiat, swappH, method = "SQchord") ## predict for RLGH data predict(swap.mat, rlgh)