RMSEP {analogue} R Documentation

## Root mean square error of prediction

### Description

Calculates or extracts the RMSEP from transfer function models.

### Usage

RMSEP(object, ...)

## S3 method for class 'mat'
RMSEP(object, k, weighted = FALSE,
...)

## S3 method for class 'bootstrap.mat'
RMSEP(object, type = c("birks1990", "standard"),
...)

## S3 method for class 'bootstrap.wa'
RMSEP(object, type = c("birks1990", "standard"),
...)


### Arguments

 object An R object. k numeric; the number of analogues to use in calculating the RMSEP. May be missing. If missing, k is extracted from the model using getK. weighted logical; Return the RMSEP for the weighted or unweighted model? The default is for an unweighted model. type The type of RMSEP to return/calculate. See Details, below. ... Arguments passed to other methods.

### Details

There are two forms of RMSEP in common usage. Within palaeoecology, the RMSEP of Birks et al. (1990) is most familiar:

\mathrm{RMSEP} = \sqrt{s_1^2 + s_2^2}

where where s_1 is the standard deviation of the out-of-bag (OOB) residuals and s_2 is the mean bias or the mean of the OOB residuals.

In the wider statistical literature, the following form of RMSEP is more commonly used:

\mathrm{RMSEP} = \sqrt{\frac{\sum_{i=1}^n (y_i - \hat{y}_i)^2}{n}}

where y_i are the observed values and \hat{y}_i the transfer function predictions/fitted values.

The first form of RMSEP is returned by default or if type = "birks1990" is supplied. The latter form is returned if type = "standard" is supplied.

The RMSEP for objects of class "mat" is a leave-one-out cross-validated RMSEP, and is calculated as for type = "standard".

### Value

A numeric vector of length 1 that is the RMSEP of object.

Gavin L. Simpson

### 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.

mat, bootstrap, wa, bootstrap.wa.

### Examples


## Imbrie and Kipp example
data(ImbrieKipp)
data(SumSST)
data(V12.122)

## merge training and test set on columns
dat <- join(ImbrieKipp, V12.122, verbose = TRUE)

## extract the merged data sets and convert to proportions
ImbrieKipp <- dat[[1]] / 100
V12.122 <- dat[[2]] / 100

## fit the MAT model using the squared chord distance measure
(ik.mat <- mat(ImbrieKipp, SumSST, method = "chord"))

## Leave-one-out RMSEP for the MAT model
RMSEP(ik.mat)

## bootstrap training set
(ik.boot <- bootstrap(ik.mat, n.boot = 100))

## extract the Birks et al (1990) RMSEP
RMSEP(ik.boot)

## Calculate the alternative formulation
RMSEP(ik.boot, type = "standard")



[Package analogue version 0.17-6 Index]