daisy {cluster} | R Documentation |
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())
x |
data matrix or dataframe. Dissimilarities will be computed
between the rows of x . Columns of mode numeric will
be recognized as interval scaled variables, columns of class
factor will be recognized as nominal variables, and columns
of class ordered will be recognized as ordinal variables.
Other variable types should be specified with the type
argument. Missing values (NAs) are allowed.
|
metric |
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 the sum of absolute differences. If not all columns of x are numeric, then this argument
will be ignored.
|
stand |
logical flag: if TRUE, then the measurements in x
are standardized before calculating the
dissimilarities. Measurements are standardized for each variable
(column), by subtracting the variable's mean value and dividing by
the variable's mean absolute deviation.
If not all columns of x are numeric, then this argument will be ignored.
|
type |
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), "logratio" (ratio scaled variables that
must be logarithmically transformed), "asymm" (asymmetric
binary variables). Each component's value is a vector, containing
the names or the numbers of the corresponding columns of x .
Variables not mentioned in the type list are interpreted as
usual (see argument x ).
|
daisy
is fully described in chapter 1 of Kaufman and Rousseeuw (1990).
Compared to 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.
In the daisy
algorithm, missing values in a row of x are not
included in the dissimilarities involving that row. There are two
main cases,
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.
If nok
is the number of nonzero weights, the dissimilarity is
multiplied by the factor 1/nok
and thus ranges between 0 and 1.
If nok = 0
, the dissimilarity is set to NA
.
an object of class "dissimilarity"
containing the dissimilarities among
the rows of x. This is typically the input for the functions pam
,
fanny
, agnes
or diana
. See
dissimilarity.object
for details.
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.
dissimilarity.object
, dist
,
pam
, fanny
, clara
,
agnes
, diana
.
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)))