summary.bootstrap.mat {analogue}R Documentation

Summarise bootstrap resampling for MAT models

Description

summary method for class "bootstrap.mat".

Usage

## S3 method for class 'bootstrap.mat':
summary(object, ...)

Arguments

object an object of class "bootstrap.mat", usually the result of a call to bootstrap.mat.
... arguments passed to or from other methods.

Value

A data frame with the following components:

observed vector of observed environmental values.
model a list containing the apparent or non-bootstrapped estimates for the training set. With the following components:
estimated
estimated values for "y", the environment.
residuals
model residuals.
r.squared
Apparent R^2 between observed and estimated values of "y".
avg.bias
Average bias of the model residuals.
max.bias
Maximum bias of the model residuals.
rmse
Apparent error (RMSE) for the model.
k
numeric; indicating the size of model used in estimates and predictions.
bootstrap a list containing the bootstrap estimates for the training set. With the following components:
estimated
Bootstrap estimates for "y".
residuals
Bootstrap residuals for "y".
r.squared
Bootstrap derived R^2 between observed and estimated values of "y".
avg.bias
Average bias of the bootstrap derived model residuals.
max.bias
Maximum bias of the bootstrap derived model residuals.
rmsep
Bootstrap derived RMSEP for the model.
s1
Bootstrap derived S1 error component for the model.
s2
Bootstrap derived S2 error component for the model.
k
numeric; indicating the size of model used in estimates and predictions.
sample.errors a list containing the bootstrap-derived sample specific errors for the training set. With the following components:
rmsep
Bootstrap derived RMSEP for the training set samples.
s1
Bootstrap derived S1 error component for training set samples.
s2
Bootstrap derived S2 error component for training set samples.
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.
call the matched call.
call model type.
predictions a list containing the apparent and bootstrap-derived estimates for the new data, with the following components:
observed
the observed values for the new samples — only if newenv is provided.
model
a list containing the apparent or non-bootstrapped estimates for the new samples. A list with the same components as apparent, above.
bootstrap
a list containing the bootstrap estimates for the new samples, with some or all of the same components as bootstrap, above.
sample.errors
a list containing the bootstrap-derived sample specific errors for the new samples, with some or all of the same components as sample.errors, above.

Author(s)

Gavin L. Simpson

See Also

bootstrap.mat, mat, summary.

Examples

## Not run: 
## continue the RLGH 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, k = 10, n.boot = 100)
swap.boot
summary(swap.boot)
## End(Not run)

[Package analogue version 0.5-2 Index]