pemix-methods {rebmix} | R Documentation |
Empirical Distribution Function Calculation
Description
Returns the data frame containing observations \bm{x}_{1}, \ldots, \bm{x}_{n}
and empirical
distribution functions F_{1}, \ldots, F_{n}
. Vectors \bm{x}
are subvectors of
\bm{y} = (y_{1}, \ldots, y_{d})^{\top}
.
Usage
## S4 method for signature 'REBMIX'
pemix(x = NULL, pos = 1, variables = expression(1:d),
lower.tail = TRUE, log.p = FALSE, ...)
## ... and for other signatures
Arguments
x |
see Methods section below. |
pos |
a desired row number in |
variables |
a vector containing indices of variables in subvectors |
lower.tail |
logical. If |
log.p |
logical. if |
... |
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. 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. 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))
# Create object of class EM.Control.
EM <- new("EM.Control", strategy = "exhaustive", variant = "ECM",
acceleration = "fixed", acceleration.multiplier = 1.0, tolerance = 1.0E-4,
maximum.iterations = 1000)
# Estimate number of components, component weights and component parameters.
simulatedest <- REBMIX(Dataset = a.Dataset(simulated),
Preprocessing = "kernel density estimation",
cmax = 4,
pdf = c("n", "n", "n"),
EMcontrol = EM)
# Preprocess simulated dataset.
f <- pemix(simulatedest, pos = 3, variables = c(1))
f