analog {analogue} | R Documentation |

Analogue matching is a more general implementation of the modern analogue methodology than MAT, where we are only interested in identifying sufficiently similar samples from a modern training as being suitable modern analogues for one or more fossil samples.

```
analog(x, ...)
## Default S3 method:
analog(x, y,
method = c("euclidean", "SQeuclidean", "chord", "SQchord",
"bray", "chi.square", "SQchi.square",
"information", "chi.distance", "manhattan",
"kendall", "gower", "alt.gower", "mixed"),
keep.train = TRUE, ...)
## S3 method for class 'distance'
analog(x, train = NULL, keep.train = TRUE, ...)
```

`x` , `y` |
data frames with same columns. |

`method` |
character string naming the dissimilarity methods to be used. See Details below. |

`keep.train` |
logical; should the dissimilarity matrix for the training set be stored? |

`train` |
a pre-computed dissimilarity matrix for the training set
samples. Objects of classes |

`...` |
arguments passed to or from other methods. |

`analog`

implements analogue matching *sensu* Flower et
al (1997) and Simpson et al (2005), where the aim is to identify
suitable close analogues of fossil samples from a modern training
set. These results are generally used within ecological restoration,
but the identification of close modern analogues for fossil samples is
also used as a technique for assessing transfer function
reconstructions.

`analog`

is a simple and very general function that generates a
pairwise dissimilarity matrix for the modern training set, and a second
matrix containing the pairwise dissimilarities between each fossil
sample and each sample in the training set. These results can then be
assessed using other functions and to extract the close modern
analogues using function `cma`

. See the See Also section
below.

Analysis of the pairwise dissimilarity matrix for the modern training
set can be used to decide on a suitable dissimilarity threshold
for defining close modern analogues. By default this matrix is
returned as part of the output from the `analog`

function.

A list of class `"analog"`

with the following components:

`analogs` |
matrix of pairwise dissimilarities between each fossil
sample ( |

`train` |
if argument |

`call` |
the matched function call. |

`method` |
character; the dissimilarity coefficient used. |

Gavin L. Simpson

Flower, R.J., Juggins, S. and Battarbee, R.W. (1997) Matching diatom
assemblages in lake sediment cores and modern surface sediment
samples: the implications for lake conservation and restoration with
special reference to acidified systems. *Hydrobiologia*
**344**; 27–40.

Simpson, G.L., Shilland, E.M., Winterbottom, J. M. and Keay, J. (2005)
Defining reference conditions for acidified waters using a modern
analogue approach. *Environmental Pollution* **137**;
119–133.

`distance`

for the function that calculates the
dissimilarity matrices.
`cma`

for extraction of close modern analogues.
`dissimilarities`

and `plot.dissimilarities`

for analysis of distribution of pairwise dissimilarity matrix for
modern training set.

```
## 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
## Imbrie and Kipp foraminfera sea-surface temperature
## analog matching between SWAP and RLGH core
ik.analog <- analog(ImbrieKipp, V12.122, method = "chord")
ik.analog
summary(ik.analog)
## Can take pre-computed dissimilarity objects
d1 <- distance(ImbrieKipp, V12.122)
d2 <- distance(ImbrieKipp)
ik <- analog(d1, d2, keep.train = TRUE)
ik
```

[Package *analogue* version 0.17-6 Index]