summary.bootstrap.mat: Summarise bootstrap resampling for MAT models

In analogue: Analogue and Weighted Averaging Methods for Palaeoecology

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 the response 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 the response residuals: Bootstrap residuals for the response r.squared: Bootstrap derived R^2 between observed and estimated values of the response 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)

analogue documentation built on Sept. 30, 2024, 9:41 a.m.