| mclustDAtrain {mclust} | R Documentation |
Training phase for MclustDA discriminant analysis.
mclustDAtrain(data, labels, G, emModelNames, eps, tol, itmax,
equalPro, warnSingular, verbose)
data |
A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
labels |
A numeric or character vector assigning a class label to each observation. |
G |
An integer vector specifying the numbers of Gaussian mixture components (clusters) for which the BIC is to be calculated (the same specification is used for all classes). Default: 1:9. |
emModelNames |
A vector of character strings indicating the models to be fitted
in the EM phase of clustering. Possible models: "E" for spherical, equal variance (one-dimensional) "V" for spherical, variable variance (one-dimensional) "EII": spherical, equal volume "VII": spherical, unequal volume "EEI": diagonal, equal volume, equal shape "VEI": diagonal, varying volume, equal shape "EVI": diagonal, equal volume, varying shape "VVI": diagonal, varying volume, varying shape "EEE": ellipsoidal, equal volume, shape, and orientation "EEV": ellipsoidal, equal volume and equal shape "VEV": ellipsoidal, equal shape "VVV": ellipsoidal, varying volume, shape, and orientation The default is .Mclust\$emModelNames.
|
eps |
A scalar tolerance for deciding when to terminate computations due
to computational singularity in covariances. Smaller values of
eps allow computations to proceed nearer to singularity. The
default is .Mclust\$eps.
|
tol |
A scalar tolerance for relative convergence of the loglikelihood.
The default is .Mclust\$tol.
|
itmax |
An integer limit on the number of EM iterations.
The default is .Mclust\$itmax.
|
equalPro |
Logical variable indicating whether or not the mixing proportions are
equal in the model. The default is .Mclust\$equalPro.
|
warnSingular |
A logical value indicating whether or not a warning should be issued
whenever a singularity is encountered.
The default is warnSingular=FALSE.
|
verbose |
A logical value indicating whether or not to print the models and
numbers of components for each class.
Default:verbose=TRUE.
|
A list in which each element gives the optimal parameters for the model best fitting each class according to BIC.
C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust.
C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.
summary.mclustDAtrain,
mclustDAtest,
EMclust,
hc,
mclustOptions
n <- 250 ## create artificial data
set.seed(0)
par(pty = "s")
x <- rbind(matrix(rnorm(n*2), n, 2) %*% diag(c(1,9)),
matrix(rnorm(n*2), n, 2) %*% diag(c(1,9))[,2:1])
xclass <- c(rep(1,n),rep(2,n))
## Not run:
mclust2Dplot(x, classification = xclass, type="classification", ask=FALSE)
## End(Not run)
odd <- seq(1, 2*n, 2)
train <- mclustDAtrain(x[odd, ], labels = xclass[odd]) ## training step
summary(train)
even <- odd + 1
test <- mclustDAtest(x[even, ], train) ## compute model densities
clEven <- summary(test)$class ## classify training set
compareClass(clEven,xclass[even])