summary.bootstrap.mat {analogue}

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)