imbc: A function that combines model-based clustering as well as...

In lsheremet/imbc:

Description

A function that combines model-based clustering as well as your input to cluster your data

Usage

imbc(data, n = 1, G = NULL, query = "minimax",
  distanceMethod = "euclidean", iterationMax = 500)

Arguments

data	the data you wish to use (must be continuous)
n	the number of points you wish to be queried on at once
G	number of clusters. The default allows Mclust to identify the number of clusters
query	how you wish to be queried. This must be one of "minimax", "maxWithinClust" or "minBetweenClust", with a default of "minimax".
distanceMethod	a method used to find distances between points. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski", with a default of "euclidean."
iterationMax	the maximum number of iterations you wish to see when converging mstep and estep

Value

An object providing the optimal (according to BIC) mixture model estimation.

The details of the output components are as follows:

call	The matched call
data	The input data matrix
modelName	A character string denoting the model at which the optimal BIC occurs
n	The number of observations in the data
d	The dimension of the data
G	The optimal number of mixture components
BIC	All BIC values
bic	Optimal BIC value
loglik	The log-likelihood corresponding to the optimal BIC
df	The number of estimated parameters
hypvol	The hypervolume parameter for the noise component if required, otherwise set to NULL (see hypvol).
parameters	A list with the following components:
pro	A vector whose kth component is the mixing proportion for the kth component of the mixture model. If missing, equal proportions are assumed
mean	The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the kth component of the mixture model.
variance	A list of variance parameters for the model. The components of this list depend on the model specification. See the help file for mclustVariance for details.
z	A matrix whose [i,k]th entry is the probability that observation i in the test data belongs to the kth class
classification	The classification corresponding to z, i.e. map(z).
uncertainty	The uncertainty associated with the classification.

Examples

#Load data
library(mclust)
data(banknote)

#Create new dataset with only continuous variables
bankdata <- banknote[,2:7]

#Run imbc while querying user on onyl 1 data point at a time
#Use default querying algorithm (minimax)
output <- imbc(bankdata)

#query two points at once and using minimum between cluster distance as query method, and specifying 2 clusters
output2 <- imbc(bankdata, n = 2, G = 2, query = "minBetweenClust")

#gives vector of classification of each row
output2$classification

#classification probability matrix
#output2$z

lsheremet/imbc documentation built on May 20, 2019, 7:01 p.m.