# bins: Binning of Data In rebmix: Finite Mixture Modeling, Clustering & Classification

## Description

Returns the list of data frames containing bin means \bar{\bm{y}}_{1}, …, \bar{\bm{y}}_{v} and frequencies k_{1}, …, k_{v} for the histogram preprocessing.

## Usage

 1 2 3 4 ## S4 method for signature 'list' bins(Dataset = list(), K = matrix(), y0 = numeric(), ymin = numeric(), ymax = numeric(), ...) ## ... and for other signatures 

## Arguments

 Dataset a list of length n_{\mathrm{D}} of data frames of size n \times d containing d-dimensional datasets. Each of the d columns represents one random variable. Numbers of observations n equal the number of rows in the datasets. K a matrix of size n_{\mathrm{D}} \times d containing numbers of bins v_{1}, …, v_{d} for the histogram. If, e.g., K = matrix(c(10, 15, 18, 5, 7, 9), byrow = TRUE, ncol = 3) than d = 3 and the list Dataset contains n_{\mathrm{D}} = 2 data frames. Hence, different numbers of bins can be assigned to y_{1}, …, y_{d}. The default value is matrix(). y0 a vector of length d containing origins. The default value is numeric(). ymin a vector of length d containing minimum observations. The default value is numeric(). ymax a vector of length d containing maximum observations. The default value is numeric(). ... currently not used.

## Methods

signature(x = "list")

a list of data frames.

## Author(s)

Branislav Panic, Marko Nagode

## References

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 # Generate multivariate normal datasets. n <- c(7, 10) Theta <- new("RNGMVNORM.Theta", c = 2, d = 2) a.theta1(Theta, 1) <- c(8, 6) a.theta1(Theta, 2) <- c(6, 8) a.theta2(Theta, 1) <- c(8, 2, 2, 4) a.theta2(Theta, 2) <- c(2, 1, 1, 4) sim2d <- RNGMIX(model = "RNGMVNORM", Dataset.name = paste("sim2d_", 1:2, sep = ""), rseed = -1, n = n, Theta = a.Theta(Theta)) # Calculate optimal numbers of bins. opt.k <- optbins(Dataset = sim2d@Dataset, Rule = "Knuth equal", kmin = 1, kmax = 20) opt.k Y <- bins(Dataset = sim2d@Dataset, K = opt.k) Y opt.k <- optbins(Dataset = sim2d@Dataset, Rule = "Knuth unequal", kmin = 1, kmax = 20) opt.k Y <- bins(Dataset = sim2d@Dataset, K = opt.k) Y 

rebmix documentation built on July 28, 2021, 5:08 p.m.