rebmix (version 2.6.1)

dfmix: Predictive Density Calculation

Description

dfmix returns (invisibly) the vector containing predictive densities $f(\bm{x} | c, \bm{w}, \bm{\Theta})$, where $\bm{x}$ stands for a subvector of $\bm{y} = (y_{1}, \ldots, y_{d})^{\top}$.

Usage

dfmix(x = NULL, w = NULL, Theta = NULL, ...)

Arguments

x

a vector, a matrix or a data frame containing continuous or discrete vector observations $\bm{x}$.

w

a vector or a data frame containing $c$ component weights $w_{l}$ summing to 1.

Theta

a matrix or a data frame containing $c$ parametric family types pdfi. One of "normal", "lognormal", "Weibull", "gamma", "binomial", "Poisson" or "Dirac"

...

currently not used.

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. http://dx.doi.org/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. http://dx.doi.org/10.1080/03610921003725788.

Examples

## Generate simulated dataset.

Theta <- rbind(pdf1 = rep("normal", 2),
  theta1.1 = c(10, 20),
  theta2.1 = c(3.0, 2.0),
  pdf1 = rep("Weibull", 2),
  theta1.1 = c(3, 2),
  theta2.1 = c(20, 10))

simulated <- RNGMIX(Dataset = "simulated",
  rseed = -1,
  n = c(15, 25),
  Theta = Theta)

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

simulatedest <- REBMIX(Dataset = simulated$Dataset,
  Preprocessing = "Parzen window",
  D = 0.025,
  cmax = 4,
  Criterion = "BIC",
  Variables = c("continuous", "continuous"),
  pdf = c("normal", "Weibull"),
  K = 8)

## Preprocess and plot finite mixtures.

opar <- plot(simulatedest)

par(opar)

y1f <- demix(x = simulatedest$Dataset[[1]][, 1, drop = FALSE],
  Preprocessing = "Parzen window",
  Variables = "continuous",
  k = 8)

y1 <- seq(from = min(y1f[, 1]), to = max(y1f[, 1]), length.out = 200)

f1 <- dfmix(x = cbind(y1), w = simulatedest$w[[1]], simulatedest$Theta[[1]][1:3,])

y2f <- demix(x = simulatedest$Dataset[[1]][, 2, drop = FALSE],
  Preprocessing = "Parzen window",
  Variables = "continuous",
  k = 8)

y2 <- seq(from = min(y2f[, 1]), to = max(y2f[, 1]), length.out = 200)

f2 <- dfmix(x = cbind(y2), w = simulatedest$w[[1]], simulatedest$Theta[[1]][4:6,])

opar <- par(mfrow = c(1, 2))

plot(y1, f1, xlab = bquote(y[1]), ylab = bquote(f(y[1])), type = "l", col = "blue")

points(y1f, pch = 3, col = "red")

plot(y2, f2, xlab = bquote(y[2]), ylab = bquote(f(y[2])), type = "l", col = "blue")

points(y2f, pch = 3, col = "red")

par(opar)