Mixed-Mode Analysis

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.

Note

Experimental.

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 .

Author

N. Frerebeau

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)

# }