bootstrap {analogue} | R Documentation |
Function to calculate bootstrap statistics for transfer function models such as bootstrap estimates, model RMSEP, sample specific errors for predictions and summary statistics such as bias and R^2 between oberved and estimated environment.
residuals
method for objects of class
"bootstrap.mat"
.
bootstrap(object, ...) ## Default S3 method: bootstrap(object, ...) ## S3 method for class 'mat': bootstrap(object, newdata, newenv, k, weighted = FALSE, n.boot = 1000, ...) ## S3 method for class 'bootstrap.mat': fitted(object, k, ...) ## S3 method for class 'bootstrap.mat': residuals(object, which = c("model", "bootstrap"), ...)
object |
an R object of class "mat" for which bootstrap
statistics are to be generated, or an object of class
"bootstrap.mat" from which fitted values or residuals are
extracted. |
newdata |
a data frame containing samples for which bootstrap
predictions and sample specific errors are to be generated. May be
missing — See Details. "newdata" must have the same number
of columns as the training set data. |
newenv |
a vector containing environmental data for samples
in "newdata" . Used to calculate full suite of errors for new
data such as a test set with known environmental values. May be
missing — See Details. "newenv" must have the same number
of rows as "newdata" . |
k |
numeric; how many modern analogues to use to generate the bootstrap statistics (and, if requested, the predictions), fitted values or residuals. |
weighted |
logical; should the weighted mean of the environment
for the "k" modern analogues be used instead of the mean? |
n.boot |
Number of bootstrap samples to take. |
which |
character; which set of residuals to return, the model residuals or the residuals of the bootstrap-derived estimates? |
... |
arguments passed to other methods. |
bootstrap
is a fairly flexible function, and can be called with
or without arguments newdata
and newenv
.
If called with only object
specified, then bootstrap estimates
for the training set data are returned. In this case, the returned
object will not include component predictions
.
If called with both object
and newdata
, then in addition
to the above, bootstrap estimates for the new samples are also
calculated and returned. In this case, component predictions
will contain the apparent and bootstrap derived predictions and
sample-specific errors for the new samples.
If called with object
, newdata
and newenv
, then
the full bootstrap
object is returned (as described in the
Value section below). With environmental data now available for the
new samples, residuals, RMSE(P) and R^2 and bias statistics can
be calculated.
The individual components of predictions
are the same as those
described in the components relating to the training set data. For
example, returned.object$predictions$bootstrap
contains the
components as returned.object$bootstrap
.
It is not usual for environmental data to be available for the new
samples for which predictions are required. In normal
palaeolimnological studies, it is more likely that newenv
will
not be available as we are dealing with sediment core samples from the
past for which environmental data are not available. However, if
sufficient training set samples are available to justify producing a
training and a test set, then newenv
will be available, and
bootstrap
can accomodate this extra information and calculate
apparent and bootstrap estimates for the test set, allowing an
independent assessment of the RMSEP of the model to be performed.
Typical usage of residuals
is
resid(object, which = c("model", "bootstrap"), ...)
For bootstrap.mat
an object of class "bootstrap.mat"
is
returned. This is a complex object with many components and is
described in bootstrapObject
.
For residuals
, a list containg the requested residuals and
metadata, with the following components:
model |
Leave one out residuals for the MAT-estimated model. |
bootstrap |
residuals for the bootstrapped MAT model. |
k |
numeric; indicating the size of model used in estimates and predictions. |
n.boot |
numeric; the number of bootstrap samples taken. |
auto |
logical; whether "k" was choosen automatically or
user-selected. |
weighted |
logical; whether the weighted mean was used instead of the mean of the environment for k-closest analogues. |
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.
mat
, plot.mat
, summary.bootstrap.mat
,
residuals
## continue the ImbrieKipp example from ?join example(join) ## Imbrie and Kipp foraminfera sea-surface temperature ## fit the MAT model using the squared chord distance measure ik.mat <- mat(ImbrieKipp, SumSST, method = "SQchord") ## bootstrap training set ik.boot <- bootstrap(ik.mat, n.boot = 100) ik.boot summary(ik.boot) ## Bootstrap fitted values for training set fitted(ik.boot) ## residuals resid(ik.boot) # uses abbreviated form