# The relliptical R package

The relliptical R package performs random numbers generation from a truncated multivariate elliptical distribution such as Normal, Pearson VII, Slash, Logistic, Kotz-type, and others by specifying the density generating function (DGF). It also computes first and second moments for some particular truncated multivariate elliptical distributions.

## Installation

You can install the released version of relliptical from CRAN with:

install.packages("relliptical")

Next, we will show the functions available in the package.

## Sampling random numbers

The function rtelliptical generates observations from a truncated multivariate elliptical distribution with location parameter equal to mean, scale matrix Sigma, lower and upper truncation points lower and upper via Slice Sampling algorithm (Neal 2003) with Gibbs sampler (Robert and Casella 2010) steps. Through argument dist, it is possible to get samples from the truncated Normal, Student-t, Pearson VII, Power Exponential, Contaminated Normal, and Slash distribution.

In the following example, we generate samples from the truncated bivariate Normal distribution.

library(relliptical)
library(ggplot2)
library(gridExtra)

# Sampling from the truncated Normal distribution
set.seed(1234)
mean  = c(0, 1)
Sigma = matrix(c(3,0.60,0.60,3), 2, 2)
lower = c(-3, -3)
upper = c(3, 3)
sample1 = rtelliptical(n=1e5, mean, Sigma, lower, upper, dist="Normal")
#>            [,1]       [,2]
#> [1,]  0.6643105  2.4005763
#> [2,] -1.3364441 -0.1756624
#> [3,] -0.1814043  1.7013605
#> [4,] -0.6841829  2.4750461
#> [5,]  2.0984490  0.1375868
#> [6,] -1.8796633 -1.2629126

# Histogram and density for variable 1
f1 = ggplot(data.frame(sample1), aes(x=X1)) +
geom_histogram(aes(y=..density..), colour="black", fill="grey", bins=15) +
geom_density(color="red") + labs(x=bquote(X), y="Density")

# Histogram and density for variable 2
f2 = ggplot(data.frame(sample1), aes(x=X2)) +
geom_histogram(aes(y=..density..), colour="black", fill="grey", bins=15) +
geom_density(color="red") + labs(x=bquote(X), y="Density")
grid.arrange(f1, f2, nrow=1) This function also allows generating random numbers from other truncated elliptical distributions by specifying the density generating function (DGF) through arguments expr or gFun. The DGF must be non-negative and strictly decreasing on the interval (0, Inf). The DGF must be provided as a character to argument expr. The notation used in expr needs to be understood by package Ryacas0 and the environment of R. For example if the DGF is , then expr="exp(1)^(-t)". In this case, the algorithm tries to compute a closed expression for the inverse function of ; and, a warning message is returned when it is not possible. Additionally, the function in expr must be written as a function depending only on variable , and any additional parameter must be given as a fixed value.

The following example draws random points from the truncated bivariate Logistic distribution, whose DGF is , see (Fang 2018).

library(stats)

# Function for plotting the sample autocorrelation using ggplot2
acf.plot = function(samples){
p = ncol(samples); n = nrow(samples); q1 = qnorm(0.975)/sqrt(n); acf1 = list(p)
for (i in 1:p){
bacfdf = with(acf(samples[,i], plot=FALSE), data.frame(lag, acf))
acf1[[i]] = ggplot(data=bacfdf, aes(x=lag,y=acf)) + geom_hline(aes(yintercept=0)) +
geom_segment(aes(xend=lag, yend=0)) + labs(x="Lag", y="ACF", subtitle=bquote(X[.(i)])) +
geom_hline(yintercept=c(q1,-q1), color="red", linetype="twodash")
}
return (acf1)
}

# Sampling from the Truncated Logistic distribution
mean  = c(0, 0)
Sigma = matrix(c(1,0.70,0.70,1), 2, 2)
lower = c(-2, -2)
upper = c(3, 2)
set.seed(5678)
# Sample autocorrelation with no thinning
sample2 = rtelliptical(n=1e4, mean, Sigma, lower, upper, dist=NULL, expr="exp(1)^(-t)/(1+exp(1)^(-t))^2")
grid.arrange(grobs=acf.plot(sample2), top="Sample ACF with no thinning", nrow=1) If the random observations are autocorrelated, it is recommended to use the argument thinning. The thinning factor reduces the autocorrelation of random points in Gibbs sampling. This value must be an integer greater than or equal to 1.

set.seed(8768)
# Sample autocorrelation with thinning = 3
sample3 = rtelliptical(n=1e4, mean, Sigma, lower, upper, dist=NULL, expr="exp(1)^(-t)/(1+exp(1)^(-t))^2",
thinning=3)
grid.arrange(grobs=acf.plot(sample3), top="Sample ACF with thinning = 3", nrow=1) If it was impossible to generate random samples from the argument expr, we could try through argument gFun by making dist = 'NULL' and expr = 'NULL'. This argument accepts the DGF as an R function. The inverse of the function can also be provided as an R function through ginvFun. If ginvFun = 'NULL', the inverse of gFun is approximated numerically.

The next example shows how to get samples from the Kotz-type distribution, whose DGF is given by

This function is strictly decreasing when , see (Fang 2018).

library(ggExtra)

# Sampling from the Truncated Kotz-type distribution
set.seed(9876)
mean  = c(0, 0)
Sigma = matrix(c(1,0.70,0.70,1), 2, 2)
lower = c(-2, -2)
upper = c(3, 2)
sample4 = rtelliptical(n=1e4, mean, Sigma, lower, upper, dist=NULL, expr=NULL,
gFun=function(t){ t^(-1/2)*exp(-2*t^(1/4)) })
f1 = ggplot(data.frame(sample4), aes(x=X1,y=X2)) + geom_point(size=0.50) +
labs(x=expression(X), y=expression(X), subtitle="Kotz(2,1/4,1/2)")
ggMarginal(f1, type="histogram", fill="grey") ## Mean and variance-covariance matrix

For this purpose, we call the function mvtelliptical(), which returns the mean vector and variance-covariance matrix for some specific truncated elliptical distributions. The argument dist sets the distribution to be used. The values are Normal, t, PE, PVII, Slash, and CN for the truncated Normal, Student-t, Power Exponential, Pearson VII, Slash, and Contaminated Normal distributions, respectively. The moments are computed through Monte Carlo integration for the truncated variables and using properties of the conditional expectation for the non-truncated variables.

The following examples compute the moments for a random variable , which follows truncated multivariate Student-t distribution with . In the first example, we have one doubly truncated variable; and in the second case, two doubly truncated variables.

# Truncated Student-t distribution
set.seed(5678)
mean = c(0.1, 0.2, 0.3)
Sigma = matrix(data = c(1,0.2,0.3,0.2,1,0.4,0.3,0.4,1), nrow=length(mean), ncol=length(mean), byrow=TRUE)

# Example 1: considering nu = 0.80 and one doubly truncated variable
a = c(-0.8, -Inf, -Inf)
b = c(0.5, 0.6, Inf)
mvtelliptical(mean, Sigma, a, b, "t", 0.80)
#> $EY #> [,1] #> [1,] -0.11001805 #> [2,] -0.54278399 #> [3,] -0.01119847 #> #>$EYY
#>            [,1]       [,2]       [,3]
#> [1,] 0.13761136 0.09694152 0.04317817
#> [2,] 0.09694152        NaN        NaN
#> [3,] 0.04317817        NaN        NaN
#>
#> $VarY #> [,1] [,2] [,3] #> [1,] 0.12550739 0.03722548 0.04194614 #> [2,] 0.03722548 NaN NaN #> [3,] 0.04194614 NaN NaN # Example 2: considering nu = 0.80 and two doubly truncated variables a = c(-0.8, -0.70, -Inf) b = c(0.5, 0.6, Inf) mvtelliptical(mean, Sigma, a, b, "t", 0.80) #>$EY
#>             [,1]
#> [1,] -0.08566441
#> [2,]  0.01563586
#> [3,]  0.19215627
#>
#> $EYY #> [,1] [,2] [,3] #> [1,] 0.126040187 0.005937196 0.01331868 #> [2,] 0.005937196 0.119761635 0.04700108 #> [3,] 0.013318682 0.047001083 1.14714388 #> #>$VarY
#>             [,1]        [,2]       [,3]
#> [1,] 0.118701796 0.007276632 0.02977964
#> [2,] 0.007276632 0.119517155 0.04399655
#> [3,] 0.029779636 0.043996554 1.11021985

It is worth mention that the Student-t distribution with degrees of freedom is a particular case of the Pearson VII distribution with parameters and when and . In the following example, we compute the moments for the truncated Pearson VII distribution with parameters and , which has the same distribution as the last example.

# Truncated Pearson VII distribution
set.seed(9876)
a = c(-0.8, -0.70, -Inf)
b = c(0.5, 0.6, Inf)
mean = c(0.1, 0.2, 0.3)
Sigma = matrix(data = c(1,0.2,0.3,0.2,1,0.4,0.3,0.4,1), nrow=length(mean), ncol=length(mean), byrow=TRUE)
mvtelliptical(mean, Sigma, a, b, "PVII", c(1.90,0.80), n=1e6) # More precision
#> $EY #> [,1] #> [1,] -0.08558130 #> [2,] 0.01420611 #> [3,] 0.19166895 #> #>$EYY
#>             [,1]        [,2]       [,3]
#> [1,] 0.128348258 0.006903655 0.01420704
#> [2,] 0.006903655 0.121364742 0.04749544
#> [3,] 0.014207043 0.047495444 1.15156461
#>
#> \$VarY
#>             [,1]        [,2]       [,3]
#> [1,] 0.121024099 0.008119433 0.03061032
#> [2,] 0.008119433 0.121162929 0.04477257
#> [3,] 0.030610322 0.044772574 1.11482763

Fang, Kai Wang. 2018. Symmetric Multivariate and Related Distributions. CRC Press.

Neal, Radford M. 2003. “Slice Sampling.” Annals of Statistics, 705–41.

Robert, Christian P, and George Casella. 2010. Introducing Monte Carlo Methods with r. Vol. 18. Springer.