analogue (version 0.4-0)

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

Description

summary method for class "bootstrap.mat".

Usage

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

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.}

See Also

bootstrap.mat, mat, summary.

Examples

## 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)