Dissimilarity Matrix Calculation
Compute all the pairwise dissimilarities (distances) between observations in the dataset. The original variables may be of mixed types.
daisy(x, metric = "euclidean", stand = FALSE, type = list())
- numeric matrix or data frame. Dissimilarities will be computed
between the rows of
x. Columns of mode
numeric(i.e. all columns when
xis a matrix) will be recognized as interval scaled variables, colum
- character string specifying the metric to be used.
The currently available options are
"euclidean"(the default) and
"manhattan". Euclidean distances are root sum-of-squares of differences, and manhattan distances are
- logical flag: if TRUE, then the measurements in
xare standardized before calculating the dissimilarities. Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the
- list containing some (or all) of the types of the
variables (columns) in
x. The list may contain the following components:
"ordratio"(ratio scaled variables to be treated as ordinal variables),
daisy is fully described in chapter 1 of Kaufman and Rousseeuw (1990).
dist whose input must be numeric
variables, the main feature of
daisy is its ability to handle
other variable types as well (e.g. nominal, ordinal, asymmetric
binary) even when different types occur in the same dataset.
daisy algorithm, missing values in a row of x are not
included in the dissimilarities involving that row. There are two
- If all variables are interval scaled, the metric is "euclidean", and ng is the number of columns in which neither row i and j have NAs, then the dissimilarity d(i,j) returned is sqrt(ncol(x)/ng) times the Euclidean distance between the two vectors of length ng shortened to exclude NAs. The rule is similar for the "manhattan" metric, except that the coefficient is ncol(x)/ng. If ng is zero, the dissimilarity is NA.
- When some variables have a type other than interval scaled, the dissimilarity between two rows is the weighted sum of the contributions of each variable. The weight becomes zero when that variable is missing in either or both rows, or when the variable is asymmetric binary and both values are zero. In all other situations, the weight of the variable is 1.
The contribution of a nominal or binary variable to the total dissimilarity is 0 if both values are different, 1 otherwise. The contribution of other variables is the absolute difference of both values, divided by the total range of that variable. Ordinal variables are first converted to ranks.
nokis the number of nonzero weights, the dissimilarity is
multiplied by the factor
1/nokand thus ranges between 0 and 1.
nok = 0, the dissimilarity is set to
- an object of class
"dissimilarity"containing the dissimilarities among the rows of x. This is typically the input for the functions
Dissimilarities are used as inputs to cluster analysis and multidimensional scaling. The choice of metric may have a large impact.
Kaufman, L. and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.
Struyf, A., Hubert, M. and Rousseeuw, P.J. (1997). Integrating Robust Clustering Techniques in S-PLUS, Computational Statistics and Data Analysis, 26, 17-37.
data(agriculture) ## Example 1 in ref: ## Dissimilarities using Euclidean metric and without standardization d.agr <- daisy(agriculture, metric = "euclidean", stand = FALSE) d.agr as.matrix(d.agr)[,"DK"] # via as.matrix.dist(.) data(flower) ## Example 2 in ref summary(dfl1 <- daisy(flower, type = list(asymm = 3))) summary(dfl2 <- daisy(flower, type = list(asymm = c(1, 3), ordratio = 7)))
https://towardsdatascience.com/hierarchical-clustering-on-categorical-data-in-r-a27e578f2995 There's an articles, and author is using daisy.