pfmix: Predictive Marginal Distribution Function Calculation In rebmix: Finite Mixture Modeling, Clustering & Classification

Description

Returns the data frame containing observations \bm{x}_{1}, …, \bm{x}_{n} and predictive marginal distribution functions F(\bm{x} | c, \bm{w}, \bm{Θ}). Vectors \bm{x} are subvectors of \bm{y} = (y_{1}, …, y_{d})^{\top}. If \bm{x} = \bm{y} the method returns the data frame containing observations \bm{y}_{1}, …, \bm{y}_{n} and the corresponding predictive mixture distribution function F(\bm{y} | c, \bm{w}, \bm{Θ}).

Usage

 1 2 3 4 ## S4 method for signature 'REBMIX' pfmix(x = NULL, Dataset = data.frame(), pos = 1, variables = expression(1:d), lower.tail = TRUE, log.p = FALSE, ...) ## ... and for other signatures

Arguments

 x see Methods section below. Dataset a data frame containing observations \bm{y} = (y_{1}, …, y_{d})^{\top} for which the predictive marginal distribution functions are calculated. The default value is data.frame(). pos a desired row number in x@summary for which the predictive marginal distribution functions are calculated. The default value is 1. variables a vector containing indices of variables in subvectors \bm{x}. The default value is 1:d. lower.tail logical. If TRUE, probabilities are P[X ≤q x], otherwise, P[X > x]. The default value is TRUE. log.p logical. if TRUE, probabilities p are given as \log(p). The default value is FALSE. ... currently not used.

Methods

signature(x = "REBMIX")

an object of class REBMIX.

signature(x = "REBMVNORM")

an object of class REBMVNORM.

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. 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. doi: 10.1080/03610921003725788.

M. Nagode. Finite mixture modeling via REBMIX. Journal of Algorithms and Optimization, 3(2):14-28, 2015. doi: 10.5963/JAO0302001.

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 # 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(Dataset = a.Dataset(simulated), Preprocessing = "h", cmax = 4, Criterion = "BIC", pdf = c("n", "n", "n")) # Preprocess simulated dataset. Dataset <- data.frame(c(25, 5, -20), NA, c(31, 20, 20)) f <- pfmix(simulatedest, Dataset = Dataset, pos = 3, variables = c(1, 3)) f # Plot finite mixture. opar <- plot(simulatedest, pos = 3, nrow = 3, ncol = 1, what = "pdf", contour.drawlabels = TRUE, contour.labcex = 0.6) par(usr = opar[]\$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 28, 2021, 5:08 p.m.