bootstrap(object, ...)## S3 method for class 'default':
bootstrap(object, \dots)
## S3 method for class 'mat':
bootstrap(object, newdata, newenv, k,
weighted = FALSE, n.boot = 1000, \dots)
"mat" currently supported."newdata" must have the same number
of columns as the training set data."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 ha"k" modern analogues be used instead of the mean?newdata and newenv are supplied or not.estimated"y", the
environment.}
residualsr.squared"y".}
avg.biasmax.biasrmsekestimated"y".}
residuals"y".}
r.squared"y".}
avg.biasmax.biasrmseps1s2krmseps1s2"k" was choosen automatically or
user-selected.observednewenv is provided.}
modelapparent, above.
}
bootstrapbootstrap, above.}
sample.errorssample.errors, above.}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.
mat, plot.mat, summary.bootstrap.mat## 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")
## bootstrap training set
swap.boot <- bootstrap(swap.mat, n.boot = 100)
swap.boot
summary(swap.boot)Run the code above in your browser using DataLab