Partitioning Around Medoids Dialog Function

DESCRIPTION:

This function is called by the Partitioning Around Medoids dialog.

USAGE:

menuPam(data, variables="<ALL>", subset=NULL, na.rm=T, diss=F, 
        obj.diss, metric="euclidean", stand=F, k, large.data=F, samples=5, 
        sampsize, save.x=T, save.diss=T, print.type="Short", save.name=NULL, 
        save.cluster.p=F, clusplot.p=T, silplot.p=T, x) 

REQUIRED ARGUMENTS:

data
data.frame or data.sheet containing the data to cluster. Each row corresponds to an observation, and each column corresponds to a variable. All variables must be numeric. Missing values (NAs) are allowed.
k
integer, the number of clusters.

OPTIONAL ARGUMENTS:

variables
variables to cluster. All variables must be numeric.
subset
subsetting expression for rows. AG na.rm logical value indicating whether to omit rows with missing values.
diss
logical flag: if TRUE, then a data set must be specified as data. Otherwise a dissimilarity object must be specified as obj.diss.
obj.diss
a dissimilarity matrix, typically the output of daisy or dist. Also a vector with length n*(n-1)/2 is allowed (where n is the number of objects), and will be interpreted in the same way as the output of the above-mentioned functions. Missing values (NAs) are not allowed.
metric
character string specifying the metric to be used for calculating dissimilarities between objects. The currently available options are "euclidean" and "manhattan". Euclidean distances are root sum-of-squares of differences, and manhattan distances are the sum of absolute differences. If x is already a dissimilarity matrix, 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 variables mean absolute deviation. If x is already a dissimilarity matrix, then this argument will be ignored.
large.data
logical flag: if TRUE, the "Clustering Large Applications" algorithm clara is used rather than pam.
samples
integer, number of samples to be drawn from the dataset if clara is used.
sampsize
integer, number of observations in each sample if clara is used.
save.x
logical flag: if TRUE, the standardized data is stored in the model object.
save.diss
logical flag: if TRUE, the dissimilarities are stored in the model object.
print.type
character string "None", "Short", or "Long" indicating whether to display no printed output, the print method, or the summary method for the model.
save.name
character string giving name of data frame in which to save cluster membership indices if save.cluster.p=T.
save.cluster.p
logical flag indicating whether to save cluster membership indices.
clusplot.p
logical flag indicating whether to plot the clusplot for the model.
silplot.p
logical flag indicating whether to plot the silhouette plot for the model.
x
deprecated argument.

VALUE:

object of class pam or clara. See pam.object or clara.object for details.

SEE ALSO:

, , , .

EXAMPLES:

menuPam(state.x77,4)