RMSEP {analogue} | R Documentation |

Calculates or extracts the RMSEP from transfer function models.

```
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"),
...)
```

`object` |
An R object. |

`k` |
numeric; the number of analogues to use in calculating the
RMSEP. May be missing. If missing, |

`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. |

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"
```

.

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

.

Gavin L. Simpson

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`

.

```
## Imbrie and Kipp example
## load the example data
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]