analogue (version 0.3-3)

predict.mat: Predict method for Modern Analogue Technique models

Description

Predicted values based on a MAT model object.

Usage

## S3 method for class 'mat':
predict(object, newdata, k, weighted = FALSE,
        bootstrap = FALSE, n.boot = 1000,
        probs = c(0.01, 0.025, 0.05, 0.1), ...)

Arguments

object

an object of mat.

newdata

data frame; required only if predictions for some new data are required. Mst have the same number of columns, in same order, as x in mat. See example below or

k

number of analogues to use. If missing, k is chosen automatically as the k that achieves lowest RMSE.

weighted

logical; should the analysis use the weighted mean of environmental data of analogues as predicted values?

bootstrap

logical; should bootstrap derived estimates and sample specific errors be calculated-ignored if newdata is missing.

n.boot

numeric; the number of bootstrap samples to take.

probs

numeric; vector of probabilities with values in [0,1].

...

arguments passed to of from other methods.

Value

• A object of class predict.mat is returned if newdata is supplied, otherwise an object of fitted.mat is returned. If bootstrap = FALSE then not all components will be returned.
• observed
  vector of observed environmental values.
• apparent
  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.
• 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.} apparent{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.}

Details

This function returns one or more of three sets of results depending on the supplied arguments: [object Object],[object Object],[object Object] The latter is simply a wrapper for bootstrap(model, newdata, ...) - see bootstrap.

References

Birks, H.J.B., Line, J.M., Juggins, S., Stevenson, A.C. and ter Braak, C.J.F. (1990). Diatoms and pH reconstruction. Philosophical Transactions of the Royal Society of London; Series B, 327; 263--278.

See Also

mat, bootstrap

Examples

## continue the RLGH and SWAP example from ?join
example(join)

## fit the MAT model using the squared chord distance measure
swap.mat <- mat(swapdiat, swappH, method = "SQchord")

## returns only fitted values
predict(swap.mat)

## predict for RLGH data
predict(swap.mat, rlgh)

## again but with bootstrap estimates and sample specific errors
predict(swap.mat, rlgh, bootstrap = TRUE)