rebmix (version 2.8.0)

pfmix: Predictive Distribution Function Calculation

Description

pfmix returns (invisibly) the vector containing predictive distribution functions $F(\bm{x} | c, \bm{w}, \bm{\Theta})$. Vector $\bm{x}$ ia a subvector of $\bm{y} = (y_{1}, \ldots, y_{d})^{\top}$.

Usage

pfmix(x = NULL, w = NULL, Theta = NULL, lower.tail = TRUE, log.p = FALSE, ...)

Arguments

x

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

w

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

Theta

a list containing $c$ parametric family types pdfi. One of "normal", "lognormal", "Weibull", "gamma", "binomial", "Poisson" or "Dirac". Component para

lower.tail

logical. If TRUE (default), probabilities are $P[X \leq x]$, otherwise, $P[X > x]$.

log.p

logical. if TRUE, probabilities $p$ are given as $\log(p)$.

...

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. M. Nagode. Finite mixture modeling via REBMIX. Journal of Algorithms and Optimization, 3(2):14-28, 2015. http://www.academicpub.org/jao/paperInfo.aspx?PaperID=16625.

Examples

# Generate simulated dataset.

Theta <- list(pdf1 = c("normal", "Weibull"),
  theta1.1 = c(10, 3),
  theta2.1 = c(3, 2),
  pdf2 = c("normal", "Weibull"),
  theta1.2 = c(20, 2),
  theta2.2 = c(2, 10))

simulated <- RNGMIX(Dataset.name = "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",
  cmax = 4,
  Criterion = "BIC",
  pdf = c("normal", "Weibull"),
  K = 8)

# Predictive distribution calculation.

i <- 1

x <- simulatedest@Dataset[[1]][, i]

F <- pfmix(x = x,
  w = simulatedest@w[[1]],
  Theta = lapply(simulatedest@Theta[[1]], "[", i))
  
plot(x, F)

i <- 2

x <- simulatedest@Dataset[[1]][, i]

F <- pfmix(x = x,
  w = simulatedest@w[[1]],
  Theta = lapply(simulatedest@Theta[[1]], "[", i))
  
plot(x, F)