Assign clusters to a new vector of categories

Description

Maps a vector of cluster numbers to another categorical vector, yielding a new vector of matching cluster numbers. Useful for distributing cluster numbers back out to the original observations in cases where the clustering was performed on a table of unique levels rather than directly on the observations (such as with greenclust).

Usage

assign.cluster(x, clusters, impute = FALSE)

Arguments

Details

Any categories in x that do not exist in names(clusters) are given a cluster of NA, or (if impute is TRUE) assigned the cluster number that is most-frequently used for the other existing categories, with ties going to the lowest cluster number. If there are no matching clusters for any of the categories in x, imputation will simply use the first cluster number in clusters.

If there are duplicate names in clusters, the first occurrence takes precedence.

Value

A factor vector of the same length as x, representing assigned cluster numbers.

See Also

greenclust, greencut, greenplot

Examples

# Cluster feed types based on number of "underweight" chicks
grc <- greenclust(table(chickwts$feed,
                        ifelse(chickwts$weight < 200, "Y", "N")))
# Assign clusters to each original observation
feed.clustered <- assign.cluster(chickwts$feed, greencut(grc))
table(chickwts$feed, feed.clustered)

Row Clustering Using Greenacre's Method

Description

Iteratively collapses the rows of a table (typically a contingency table) by selecting the pair of rows each time whose combination creates the smalled loss of chi-squared.

Usage

greenclust(x, correct = FALSE, verbose = FALSE)

Arguments

Value

An object of class greenclust which is compatible with most hclust object functions, such as plot() and rect.hclust(). The height vector represents the proportion of chi-squared, relative to the original table, seen at each clustering step. The greenclust object also includes a vector for the chi-squared test p-value at each step and a boolean vector indicating whether the step had a tie for "winner".

References

Greenacre, M.J. (1988) "Clustering the Rows and Columns of a Contingency Table," Journal of Classification 5, 39-51. doi:10.1007/BF01901670

See Also

greencut, greenplot, assign.cluster

Examples

# Combine Titanic passenger attributes into a single category
tab <- t(as.data.frame(apply(Titanic, 4:1, FUN=sum)))
# Remove rows with all zeros
tab <- tab[apply(tab, 1, sum) > 0, ]

# Perform clustering on contingency table
grc <- greenclust(tab)

# Plot r-squared and p-values for each potential cut point
greenplot(grc)

# Get clusters at suggested cut point
clusters <- greencut(grc)

# Plot dendrogram with clusters marked
plot(grc)
rect.hclust(grc, max(clusters))

Cut a Greenclust Tree into Optimal Groups

Description

Cuts a greenclust tree at an automatically-determined number of groups.

Usage

greencut(g, k = NULL, h = NULL)

Arguments

Details

The cut point is calculated by finding the number of groups/clusters that results in a collapsed contingency table with the most-significant (lowest p-value) chi-squared test. If there are ties, the smallest number of groups wins.

If a certain number of groups is required or a specific r-squared (1 - height) threshold is targeted, values for either k or h may be provided. (While the regular cutree function could also be used in this circumstance, it may still be useful to have the additional attributes that greencut() provides.)

As with cutree(), k overrides h if both are given.

Value

greencut returns a vector of group memberships, with the resulting r-squared value and p-value as object attributes, accessable via attr.

References

Greenacre, M.J. (1988) "Clustering the Rows and Columns of a Contingency Table," Journal of Classification 5, 39-51. doi:10.1007/BF01901670

See Also

greenclust, greenplot, assign.cluster

Examples

# Combine Titanic passenger attributes into a single category
# and create a contingency table for the non-zero levels
tab <- t(as.data.frame(apply(Titanic, 4:1, FUN=sum)))
tab <- tab[apply(tab, 1, sum) > 0, ]

grc <- greenclust(tab)
greencut(grc)

plot(grc)
rect.hclust(grc, max(greencut(grc)),
            border=unique(greencut(grc))+1)

Plot Statistics for a Greenclust Object

Description

Displays a connected scatterplot showing the r-squared values (x-axis) and p-values (y-axis) at each clustering step of a greenclust object. Points are labeled with their cutpoints, i.e., the number of groups/clusters found at each step. The point with the lowest p-value (typically the optimal cutpoint) is highlighted.

Usage

greenplot(
  g,
  type = "b",
  bg = "gray75",
  pch = 21,
  cex = 1,
  optim.col = "red",
  pos = 2,
  main = "P-Value vs. R-Squared for Num. Clusters",
  xlab = "r-squared",
  ylab = NULL,
  ...
)

Arguments

References

Greenacre, M.J. (1988) "Clustering the Rows and Columns of a Contingency Table," Journal of Classification 5, 39-51. doi:10.1007/BF01901670

See Also

greenclust, greencut, assign.cluster

Examples

# Combine Titanic passenger attributes into a single category
# and create a contingency table for the non-zero levels
tab <- t(as.data.frame(apply(Titanic, 4:1, FUN=sum)))
tab <- tab[apply(tab, 1, sum) > 0, ]

grc <- greenclust(tab)
greenplot(grc)


# Plot using custom graphical parameters
greenplot(grc, type="p", bg="lightblue", optim.col="darkorange",
          pos=3, bty="n", cex.main=2, col.main="blue")