CARP
returns a fast approximation to the Convex Clustering
solution path along with visualizations such as dendrograms and
cluster paths. CARP
solves the Convex Clustering problem via an efficient
Algorithmic Regularization scheme.
CARP( X, ..., weights = sparse_rbf_kernel_weights(k = "auto", phi = "auto", dist.method = "euclidean", p = 2), labels = rownames(X), X.center = TRUE, X.scale = FALSE, back_track = FALSE, exact = FALSE, norm = 2, t = 1.05, npcs = min(4L, NCOL(X), NROW(X)), dendrogram.scale = NULL, impute_func = function(X) { if (anyNA(X)) missForest(X)$ximp else X }, status = (interactive() && (clustRviz_logger_level() %in% c("MESSAGE", "WARNING", "ERROR"))) )
X  The data matrix (\(X \in R^{n \times p}\)): rows correspond to
the observations (to be clustered) and columns to the variables (which
will not be clustered). If 

...  Unused arguements. An error will be thrown if any unrecognized
arguments as given. All arguments other than 
weights  One of the following:

labels  A character vector of length \(n\): observations (row) labels 
X.center  A logical: Should 
X.scale  A logical: Should 
back_track  A logical: Should backtracking be used to exactly identify fusions? By default, backtracking is not used. 
exact  A logical: Should the exact solution be computed using an iterative algorithm?
By default, algorithmic regularization is applied and the exact solution
is not computed. Setting 
norm  Which norm to use in the fusion penalty? Currently only 
t  A number greater than 1: the size of the multiplicative update to
the cluster fusion regularization parameter (not used by
backtracking variants). Typically on the scale of 
npcs  An integer >= 2. The number of principal components to compute for path visualization. 
dendrogram.scale  A character string denoting how the scale of dendrogram
regularization proportions should be visualized.
Choices are 
impute_func  A function used to impute missing data in 
status  Should a status message be printed to the console? 
An object of class CARP
containing the following elements (among others):
X
: the original data matrix
n
: the number of observations (rows of X
)
p
: the number of variables (columns of X
)
alg.type
: the CARP
variant used
X.center
: a logical indicating whether X
was centered
columnwise before clustering
X.scale
: a logical indicating whether X
was scaled
columnwise before centering
weight_type
: a record of the scheme used to create
fusion weights
carp_fit < CARP(presidential_speech[1:10,1:4])#>#>#>print(carp_fit)#> CARP Fit Summary #> ==================== #> #> Algorithm: CARP (t = 1.05) #> Fit Time: 0.008 secs #> Total Time: 1.025 secs #> #> Number of Observations: 10 #> Number of Variables: 4 #> #> Preprocessing options: #>  Columnwise centering: TRUE #>  Columnwise scaling: FALSE #> #> Weights: #>  Source: Radial Basis Function Kernel Weights #>  Distance Metric: Euclidean #>  Scale parameter (phi): 0.1 [DataDriven] #>  Sparsified: 2 Nearest Neighbors [DataDriven] #>plot(carp_fit)