Mixes chemical and petrographic matrices.

## Usage

```
mix(x, y, ...)
# S4 method for class 'matrix,matrix'
mix(x, y, lambda = 1, ...)
# S4 method for class 'dist,dist'
mix(x, y, mu = 0.5)
```

## Arguments

- x
A

`matrix`

of chemical compositional data or a dissimilarity matrix for these chemical compositional data.- y
A

`matrix`

of coded mineralogical binary data or a dissimilarity matrix for these mineralogical data.- ...
Extra parameters to be passed to

`cluster::daisy()`

.- lambda
A length-one

`numeric`

vector giving a weighting factor.- mu
A length-one

`numeric`

vector that lies between 0 and 1 giving the mixing parameter.

## Value

A stats::dist object.

## Methods (by class)

`mix(x = matrix, y = matrix)`

: First approach of mixed-mode analysis.`mix(x = dist, y = dist)`

: Second approach of mixed-mode analysis.

## References

Baxter, M. J., Beardah, C. C., Papageorgiou, I., Cau, M. A., Day, P. M. &
Kilikoglou, V. (2008). On Statistical Approaches to the Study of Ceramic
Artefacts Using Geochemical and Petrographic Data. *Archaeometry*, 50(1):
142-157. doi:10.1111/j.1475-4754.2007.00359.x
.

Beardah, C. C., Baxter, M. J., Papageorgiou, I. & Cau, M. A. (2003).
"Mixed-Mode" Approaches to the Grouping of Ceramic Artefacts Using S-Plus.
In M. Doerr and A. Sarris, *The Digital Heritage of Archaeology*, p. 261-266.
Athens: Archive of Monuments and Publications, Hellenic Ministry of Culture.

Gower, J. C. (1971). A general coefficient of similarity and some of its
properties. *Biometrics*, 27(4):857-874. doi:10.2307/2528823
.

## Examples

```
# \donttest{
## Can Sora datasets
## Data from Cau (1999) and Cau et al. (2007)
path_chem <- system.file("extdata", "cansora_chemistry.csv", package = "nexus")
chemistry <- read.csv(path_chem, header = TRUE, row.names = 1)
path_petro <- system.file("extdata", "cansora_petrography.csv", package = "nexus")
petrography <- read.csv(path_petro, header = TRUE, row.names = 1)
## Prepare chemical data
major <- c("Fe2O3", "Al2O3", "MnO", "P2O5", "TiO2",
"MgO", "CaO", "Na2O", "K2O", "SiO2")
chem <- chemistry[-1, major]
## Prepare petrographic data
petro <- petrography[-c(7, 8), -1]
petro <- cdt(petro) # Get the complete disjunctive table
## First approach
mix1 <- mix(as.matrix(chem), as.matrix(petro), lambda = 2)
#> Warning: binary variable(s) 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59 treated as interval scaled
mds1 <- stats::cmdscale(mix1) # Multi-Dimensional Scaling
plot(mds1)
# }
```