dfmix: Predictive Marginal Density Calculation

In rebmix: Finite Mixture Modeling, Clustering & Classification

Predictive Marginal Density Calculation

Description

Returns the data frame containing observations \bm{x}_{1}, \ldots, \bm{x}_{n} and predictive marginal densities f(\bm{x} | c, \bm{w}, \bm{\Theta}). Vectors \bm{x} are subvectors of \bm{y} = (y_{1}, \ldots, y_{d})^{\top}. If \bm{x} = \bm{y} the method returns the data frame containing observations \bm{y}_{1}, \ldots, \bm{y}_{n} and the corresponding predictive mixture densities f(\bm{y} | c, \bm{w}, \bm{\Theta}).

Usage

## S4 method for signature 'REBMIX'
dfmix(x = NULL, Dataset = data.frame(), pos = 1, variables = expression(1:d), ...)
## ... and for other signatures

Arguments

x	see Methods section below.
Dataset	a data frame containing observations \bm{y} = (y_{1}, \ldots, y_{d})^{\top} for which the predictive marginal densities are calculated. The default value is data.frame().
pos	a desired row number in x@summary for which the predictive marginal densities are calculated. The default value is 1.
variables	a vector containing indices of variables in subvectors \bm{x}. The default value is 1:d.
...	currently not used.

Methods

signature(x = "REBMIX")

an object of class REBMIX.

signature(x = "REBMVNORM")

an object of class REBMVNORM.

Author(s)

Marko Nagode

References

M. Nagode and M. Fajdiga. The rebmix algorithm for the univariate finite mixture estimation. Communications in Statistics - Theory and Methods, 40(5):876-892, 2011a. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/03610920903480890")}.

M. Nagode and M. Fajdiga. The rebmix algorithm for the multivariate finite mixture estimation. Communications in Statistics - Theory and Methods, 40(11):2022-2034, 2011b. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/03610921003725788")}.

M. Nagode. Finite mixture modeling via REBMIX. Journal of Algorithms and Optimization, 3(2):14-28, 2015. https://repozitorij.uni-lj.si/Dokument.php?id=127674&lang=eng.

Examples

# Generate simulated dataset.

n <- c(15, 15)

Theta <- new("RNGMIX.Theta", c = 2, pdf = rep("normal", 3))

a.theta1(Theta, 1) <- c(10, 20, 30)
a.theta1(Theta, 2) <- c(3, 4, 5)
a.theta2(Theta, 1) <- c(3, 2, 1)
a.theta2(Theta, 2) <- c(15, 10, 5)

simulated <- RNGMIX(Dataset.name = paste("simulated_", 1:4, sep = ""),
  rseed = -1,
  n = n,
  Theta = a.Theta(Theta))

# Number of classes or nearest neighbours to be processed.

K <- c(as.integer(1 + log2(sum(n))), # Minimum v follows Sturges rule.
  as.integer(10 * log10(sum(n)))) # Maximum v follows log10 rule.

# Estimate number of components, component weights and component parameters.

simulatedest <- REBMIX(model = "REBMVNORM",
  Dataset = a.Dataset(simulated),
  Preprocessing = "h",
  cmax = 4,
  Criterion = "BIC")

# Preprocess simulated dataset.

Dataset <- data.frame(c(-7, 1), NA, c(3, 7))

f <- dfmix(simulatedest, Dataset = Dataset, pos = 3, variables = c(1, 3))

f

# Plot finite mixture.

opar <- plot(simulatedest, pos = 3, nrow = 3, ncol = 1,
  contour.drawlabels = TRUE, contour.labcex = 0.6)

par(usr = opar[[2]]$usr, mfg = c(2, 1))

points(x = f[, 1], y = f[, 2])

text(x = f[, 1], y = f[, 2], labels = format(f[, 3], digits = 3), cex = 0.8, pos = 4)

rebmix documentation built on July 26, 2023, 5:32 p.m.