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 |

`newdata` |
a data frame containing samples for which bootstrap
predictions and sample specific errors are to be generated. May be
missing — See Details. |

`newenv` |
a vector containing environmental data for samples
in |

`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 |

`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"), \dots)

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 |

`weighted` |
logical; whether the weighted mean was used instead of
the mean of the environment for |

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`

## 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 ## 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 ## IGNORE_RDIFF_BEGIN ik.boot <- bootstrap(ik.mat, n.boot = 100) ik.boot summary(ik.boot) ## IGNORE_RDIFF_END ## Bootstrap fitted values for training set ## IGNORE_RDIFF_BEGIN fitted(ik.boot) ## IGNORE_RDIFF_END ## residuals resid(ik.boot) # uses abbreviated form

[Package *analogue* version 0.17-6 Index]