predictML.predkmeans: Prediction of Cluster Membership

In predkmeans: Covariate Adaptive Clustering

Description

Predicts cluster membership using either multinomial logistic regression or SVMs.

Usage

## S3 method for class 'predkmeans'
predictML(
  object = NULL,
  centers = object$centers,
  K = nrow(centers),
  R,
  Rstar,
  Xstar = NULL,
  tr.assign = object$cluster,
  muStart = "random",
  maxitMlogit = 500,
  verbose = 1,
  nMlogitStarts = 1,
  mlogit.control = list(suppressFittedWarning = TRUE),
  ...
)

## S3 method for class 'predkmeans'
predictSVM(
  object = NULL,
  centers = object$centers,
  R,
  Rstar,
  K = nrow(centers),
  Xstar = NULL,
  tr.assign = object$cluster,
  svm.control = list(gamma = c(1/(2:1), 2), cost = seq(20, 100, by = 20)),
  ...
)

## S3 method for class 'predkmeans'
predictMixExp(object, R, Rstar = NULL, ...)

Arguments

object	A predkmeans object, from which the cluster centers will be extracted.
centers	Matrix of cluster centers, assumed to be K-by-p
K	Number of clusters
R	matrix of covariates for observations to be predicted at.
Rstar	matrix of covariates at training locations
Xstar	matrix of observation at training locations. Either this or tr.assign is required.
tr.assign	vector of cluster assignments at training locations. By default, extracted from object.
muStart	starting value for cluster centers in mlogit optimization (IDEA: change to pull from predkmeans object?). If not provided, starting values are selected randomly.
maxitMlogit	Maximum number of iterations for mlogit in prediction
verbose	integer indicating amount of output to be displayed
nMlogitStarts	number of mlogit starts to use in estimation of parameters
mlogit.control	list of control parameters to be passes to mlogit
...	Unused additional arguments
svm.control	list of options for best.svm

Details

Function for predicting cluster membership in clusters identified by k-means or predictive k-means using multinomial logistic regression or support vector machines (SVMs). For multinomial logitic regression, parameter estimation is handled by mlogit. The SVMs are fit using best.svm from e1071 package.

Because this prediction includes return information about cluster assignment and prediction model parameters, this method is deliberately distinct from the generic predict functions.

The predictMixExp funciton provides predictions from the 'working' cluster assignments created as part of the mixture of experts algorithm from predkmeans.

Value

A list containing some or all of the following elements:

tr.assign	Cluster assignments at training locations
mlfit	A subset of the mlogit object returned by the function of that name
beta	Estimated model parameters
test.pred	Predicted cluster assignments at test locations

Author(s)

Joshua Keller

See Also

mlogit, predkmeans, predictionMetrics

Other methods for predkmeans objects: relevel.predkmeans()

Examples

n <- 200
r1 <- rnorm(n)
r2 <- rnorm(n)
u1 <- rbinom(n, size=1,prob=0)
cluster <- ifelse(r1<0, ifelse(u1, "A", "B"), ifelse(r2<0, "C", "D"))
mu1 <- c(A=2, B=2, C=-2, D=-2)
mu2 <- c(A=1, B=-1, C=-1, D=-1)
x1 <- rnorm(n, mu1[cluster], 4)
x2 <- rnorm(n, mu2[cluster], 4)
R <- cbind(1, r1, r2)
X <- cbind(x1, x2)
pkm <- predkmeans(X=cbind(x1, x2), R=R, K=4)
n_pred <- 50
Rnew <- cbind(1, r1=rnorm(n_pred), r2=rnorm(n_pred))
pkmPred <- predictML(pkm, R=Rnew, Rstar=R)
pkmPred$test.pred 

predkmeans documentation built on Jan. 11, 2020, 9:29 a.m.