RCLSMIX-methods {rebmix} | R Documentation |
Returns as default the RCLSMIX algorithm output for mixtures of conditionally independent normal, lognormal, Weibull, gamma, binomial, Poisson, Dirac or von Mises component densities. If model
equals "RCLSMVNORM"
output for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices is returned.
## S4 method for signature 'RCLSMIX' RCLSMIX(model = "RCLSMIX", x = list(), Dataset = data.frame(), Zt = factor(), ...) ## ... and for other signatures ## S4 method for signature 'RCLSMIX' summary(object, ...) ## ... and for other signatures
model |
see Methods section below. |
x |
a list of objects of class |
Dataset |
a data frame containing test dataset Y_{\mathrm{test}} of length n_{\mathrm{test}}. For the test dataset the corresponding class membership \bm{Ω}_{g} is not known.
The default value is |
Zt |
a factor of true class membership \bm{Ω}_{g} for the test dataset. The default value is |
object |
see Methods section below. |
... |
currently not used; additional arguments affecting the summary produced. |
Returns an object of class RCLSMIX
or RCLSMVNORM
.
signature(model = "RCLSMIX")
a character giving the default class name "RCLSMIX"
for mixtures of conditionally independent normal, lognormal, Weibull, gamma, binomial, Poisson, Dirac or von Mises component densities.
signature(model = "RCLSMVNORM")
a character giving the class name "RCLSMVNORM"
for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices.
signature(object = "RCLSMIX")
an object of class RCLSMIX
.
signature(object = "RCLSMVNORM")
an object of class RCLSMVNORM
.
Marko Nagode
R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. John Wiley & Sons, New York, 1973.
## Not run: devAskNewPage(ask = TRUE) data("adult") # Find complete cases. adult <- adult[complete.cases(adult),] # Replace levels with numbers. adult <- as.data.frame(data.matrix(adult)) # Find numbers of levels. cmax <- unlist(lapply(apply(adult[, c(-1, -16)], 2, unique), length)) cmax # Split adult dataset into train and test subsets for two Incomes # and remove Type and Income columns. Adult <- split(p = list(type = 1, train = 2, test = 1), Dataset = adult, class = 16) # Estimate number of components, component weights and component parameters # for the set of chunks 1:14. adultest <- list() for (i in 1:14) { adultest[[i]] <- REBMIX(Dataset = a.train(chunk(Adult, i)), Preprocessing = "histogram", cmax = min(120, cmax[i]), Criterion = "BIC", pdf = "Dirac", K = 1) } # Class membership prediction based upon the best first search algorithm. adultcla <- BFSMIX(x = adultest, Dataset = a.test(Adult), Zt = a.Zt(Adult)) adultcla summary(adultcla) # Plot selected chunks. plot(adultcla, nrow = 5, ncol = 2) ## End(Not run)