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 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)

[Package analogue version 0.6-6 Index]