inorm_mix: Normal Mixture Estimation

In matheuscastro43/mixGM: Finite Mixture Models

Description

Estimates parameters of a univariate Normal mixture with k-means clustering and EM-algorithm.

Usage

inorm_mix(data, g, criteria = "dif.psi", plot.it = TRUE, empirical = FALSE,
          col.estimated = "orange", col.empirical = "navy", ...)

Arguments

data	vector containing the sample, or list obtained with rnorm_mix.
g	number of components in the mixture.
criteria	the stop criteria to be used, could be "dif.psi" to calculate differences on parameters matrix or "dif.lh" to calculate differences on Likelihood function.
plot.it	logical, TRUE to plot the histogram with estimated distribution curve.
empirical	logical, TRUE to add the empirical curve ("Kernel Density Estimation") in the plot.
col.estimated	a colour to be used in the curve of estimated density.
col.empirical	a colour to be used in the curve of empirical density.
...	further arguments and graphical parameters passed to hist.

Author(s)

CASTRO, M. O.; MONTALVO, G. S. A.

Examples

## Generate a sample.
data = rnorm_mix(n = 1000, pi = c(0.6, 0.4), mean = c(10, 18), sd = c(1, 2))

## And now, estimate the parameters, using the 'data' list.
inorm_mix(data, g = 2)

## Or using the sample vector.
inorm_mix(data$sample, g = 2)

## Using the diference in the log-likelihood as stop criteria.
inorm_mix(data, g = 2, criteria = "dif.lh")

## Not plotting the graphic.
inorm_mix(data, g = 2, plot.it = FALSE)

## Adding the empirical curve to the graphic.
inorm_mix(data, g = 2, empirical = TRUE)

## Changing the color of the curves.
inorm_mix(data, g = 2, empirical = TRUE, col.estimated = "pink", col.empirical = "red3")

## Using "..."
inorm_mix(data, g = 2, empirical = TRUE, col.estimated = "pink", col.empirical = "red3",
          breaks = 300)

matheuscastro43/mixGM documentation built on March 2, 2020, 12:19 a.m.