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

 bins-methods R Documentation

## Binning of Data

### 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

## S4 method for signature 'list'
bins(Dataset = list(), K = matrix(),
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(). 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. https://repozitorij.uni-lj.si/Dokument.php?id=127674&lang=eng.

### Examples

# 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 Aug. 18, 2022, 1:06 a.m.