# dfmix: Predictive Marginal Density Calculation In rebmix: Finite Mixture Modeling, Clustering & Classification

 dfmix-methods R Documentation

## Predictive Marginal Density Calculation

### Description

Returns the data frame containing observations \bm{x}_{1}, …, \bm{x}_{n} and predictive marginal densities 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 densities f(\bm{y} | c, \bm{w}, \bm{Θ}).

### Usage

```## S4 method for signature 'REBMIX'
dfmix(x = NULL, Dataset = data.frame(), pos = 1, variables = expression(1:d), ...)
## ... 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 densities are calculated. The default value is `data.frame()`. `pos` a desired row number in `x@summary` for which the predictive marginal densities are calculated. The default value is `1`. `variables` a vector containing indices of variables in subvectors \bm{x}. The default value is `1:d`. `...` 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. 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))

# 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(model = "REBMVNORM",
Dataset = a.Dataset(simulated),
Preprocessing = "h",
cmax = 4,
Criterion = "BIC")

# Preprocess simulated dataset.

Dataset <- data.frame(c(-7, 1), NA, c(3, 7))

f <- dfmix(simulatedest, Dataset = Dataset, pos = 3, variables = c(1, 3))

f

# Plot finite mixture.

opar <- plot(simulatedest, pos = 3, nrow = 3, ncol = 1,
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 Aug. 18, 2022, 1:06 a.m.