In fchamroukhi/MEteorits: Mixture-of-Experts Modeling for Complex Non-Normal Distributions

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MEteorits: Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributions

MEteorits is an open source toolbox (available in R and Matlab) containing several original and flexible mixtures-of-experts models to model, cluster and classify heteregenous data in many complex situations where the data are distributed according to non-normal and possibly skewed distributions, and when they might be corrupted by atypical observations. The toolbox also contains sparse mixture-of-experts models for high-dimensional data.

Our (dis-)covered meteorits are for instance the following ones:

• NMoE (Normal Mixtures-of-Experts);
• tMoE (t Mixtures-of-Experts);
• SNMoE (Skew-Normal Mixtures-of-Experts);
• StMoE (Skew t Mixtures-of-Experts).

The models and algorithms are developped and written in Matlab by Faicel Chamroukhi, and translated and designed into R packages by Florian Lecocq, Marius Bartcus and Faicel Chamroukhi.

Installation

You can install the development version of MEteorits from GitHub with:

# install.packages("devtools")
devtools::install_github("fchamroukhi/MEteorits")

To build vignettes for examples of usage, type the command below instead:

# install.packages("devtools")
devtools::install_github("fchamroukhi/MEteorits", 
                         build_opts = c("--no-resave-data", "--no-manual"), 
                         build_vignettes = TRUE)

Use the following command to display vignettes:

browseVignettes("meteorits")

Usage

library(meteorits)

NMoE

# Application to a simulated data set

n <- 500 # Size of the sample
alphak <- matrix(c(0, 8), ncol = 1) # Parameters of the gating network
betak <- matrix(c(0, -2.5, 0, 2.5), ncol = 2) # Regression coefficients of the experts
sigmak <- c(1, 1) # Standard deviations of the experts
x <- seq.int(from = -1, to = 1, length.out = n) # Inputs (predictors)

# Generate sample of size n
sample <- sampleUnivNMoE(alphak = alphak, betak = betak, 
                         sigmak = sigmak, x = x)
y <- sample$y

K <- 2 # Number of regressors/experts
p <- 1 # Order of the polynomial regression (regressors/experts)
q <- 1 # Order of the logistic regression (gating network)

nmoe <- emNMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE)

nmoe$summary()

nmoe$plot()

# Application to a real data set

data("tempanomalies")
x <- tempanomalies$Year
y <- tempanomalies$AnnualAnomaly

K <- 2 # Number of regressors/experts
p <- 1 # Order of the polynomial regression (regressors/experts)
q <- 1 # Order of the logistic regression (gating network)

nmoe <- emNMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE)

nmoe$summary()

nmoe$plot()

TMoE

# Application to a simulated data set

n <- 500 # Size of the sample
alphak <- matrix(c(0, 8), ncol = 1) # Parameters of the gating network
betak <- matrix(c(0, -2.5, 0, 2.5), ncol = 2) # Regression coefficients of the experts
sigmak <- c(0.5, 0.5) # Standard deviations of the experts
nuk <- c(5, 7) # Degrees of freedom of the experts network t densities
x <- seq.int(from = -1, to = 1, length.out = n) # Inputs (predictors)

# Generate sample of size n
sample <- sampleUnivTMoE(alphak = alphak, betak = betak, sigmak = sigmak, 
                         nuk = nuk, x = x)
y <- sample$y

K <- 2 # Number of regressors/experts
p <- 1 # Order of the polynomial regression (regressors/experts)
q <- 1 # Order of the logistic regression (gating network)

tmoe <- emTMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE)

tmoe$summary()

tmoe$plot()

# Application to a real data set

library(MASS)
data("mcycle")
x <- mcycle$times
y <- mcycle$accel

K <- 4 # Number of regressors/experts
p <- 2 # Order of the polynomial regression (regressors/experts)
q <- 1 # Order of the logistic regression (gating network)

tmoe <- emTMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE)

tmoe$summary()

tmoe$plot()

SNMoE

# Application to a simulated data set

n <- 500 # Size of the sample
alphak <- matrix(c(0, 8), ncol = 1) # Parameters of the gating network
betak <- matrix(c(0, -2.5, 0, 2.5), ncol = 2) # Regression coefficients of the experts
lambdak <- c(3, 5) # Skewness parameters of the experts
sigmak <- c(1, 1) # Standard deviations of the experts
x <- seq.int(from = -1, to = 1, length.out = n) # Inputs (predictors)

# Generate sample of size n
sample <- sampleUnivSNMoE(alphak = alphak, betak = betak, 
                          sigmak = sigmak, lambdak = lambdak, 
                          x = x)
y <- sample$y

K <- 2 # Number of regressors/experts
p <- 1 # Order of the polynomial regression (regressors/experts)
q <- 1 # Order of the logistic regression (gating network)

snmoe <- emSNMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE)

snmoe$summary()

snmoe$plot()

# Application to a real data set

data("tempanomalies")
x <- tempanomalies$Year
y <- tempanomalies$AnnualAnomaly

K <- 2 # Number of regressors/experts
p <- 1 # Order of the polynomial regression (regressors/experts)
q <- 1 # Order of the logistic regression (gating network)

snmoe <- emSNMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE)

snmoe$summary()

snmoe$plot()

StMoE

# Applicartion to a simulated data set

n <- 500 # Size of the sample
alphak <- matrix(c(0, 8), ncol = 1) # Parameters of the gating network
betak <- matrix(c(0, -2.5, 0, 2.5), ncol = 2) # Regression coefficients of the experts
sigmak <- c(0.5, 0.5) # Standard deviations of the experts
lambdak <- c(3, 5) # Skewness parameters of the experts
nuk <- c(5, 7) # Degrees of freedom of the experts network t densities
x <- seq.int(from = -1, to = 1, length.out = n) # Inputs (predictors)

# Generate sample of size n
sample <- sampleUnivStMoE(alphak = alphak, betak = betak, 
                          sigmak = sigmak, lambdak = lambdak, 
                          nuk = nuk, x = x)
y <- sample$y

K <- 2 # Number of regressors/experts
p <- 1 # Order of the polynomial regression (regressors/experts)
q <- 1 # Order of the logistic regression (gating network)

stmoe <- emStMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE)

stmoe$summary()

stmoe$plot()

# Applicartion to a real data set

library(MASS)
data("mcycle")
x <- mcycle$times
y <- mcycle$accel

K <- 4 # Number of regressors/experts
p <- 2 # Order of the polynomial regression (regressors/experts)
q <- 1 # Order of the logistic regression (gating network)

stmoe <- emStMoE(X = x, Y = y, K = K, p = p, q = q, verbose = TRUE)

stmoe$summary()

stmoe$plot()

References

fchamroukhi/MEteorits documentation built on Feb. 26, 2020, 4:57 p.m.