ordsample

0th

Percentile

Drawing a sample of discrete data

The function draws a sample from a multivariate discrete variable with correlation matrix Sigma and prescribed marginal distributions marginal

Keywords
multivariate, models, distribution, htest, datagen
Usage
ordsample(n, marginal, Sigma, support = list(), Spearman = FALSE, cormat = "discrete")
Arguments
n
the sample size
marginal
a list of $k$ elements, where $k$ is the number of variables. The $i$-th element of marginal is the vector of the cumulative probabilities defining the marginal distribution of the $i$-th component of the multivariate variable. If the $i$-th component can take $k_i$ values, the $i$-th element of marginal will contain $k_i-1$ probabilities (the $k_i$-th is obviously 1 and shall not be included).
Sigma
the target correlation matrix of the multivariate discrete variable
support
a list of $k$ elements, where $k$ is the number of variables. The $i$-th element of support is the vector containing the ordered values of the support of the $i$-th variable. By default, the support of the $i$-th variable is $1,2,...,k_i$
Spearman
if TRUE, the function finds Spearman's correlations (and it is not necessary to provide support), if FALSE (default) Pearson's correlations
cormat
"discrete" if the Sigma in input is the target correlation matrix of the multivariate discrete variable; "continuous" if the Sigma in input is the intermediate correlation matrix of the multivariate standard normal
Value

a $n\times k$ matrix of values drawn from the $k$-variate discrete r.v. with the desired marginal distributions and correlation matrix

See Also

contord, ordcont, corrcheck

Aliases
  • ordsample
Examples
# Example 1

# draw a sample from a bivariate ordinal variable
# with 4 of categories and asymmetrical marginal distributions
# and correlation coefficient 0.6 (to be checked)
k <- 2
marginal <- list(c(0.1,0.3,0.6), c(0.4,0.7,0.9))
corrcheck(marginal) # check ok
Sigma <- matrix(c(1,0.6,0.6,1),2,2)
# sample size 1000
n <- 1000
# generate a sample of size n
m <- ordsample(n, marginal, Sigma)
head(m)
# sample correlation matrix
cor(m) # compare it with Sigma
# empirical marginal distributions
cumsum(table(m[,1]))/n
cumsum(table(m[,2]))/n # compare them with the two marginal distributions

# Example 1bis

# draw a sample from a bivariate ordinal variable
# with 4 of categories and asymmetrical marginal distributions
# and Spearman correlation coefficient 0.6 (to be checked)
k <- 2
marginal <- list(c(0.1,0.3,0.6), c(0.4,0.7,0.9))
corrcheck(marginal, Spearman=TRUE) # check ok
Sigma <- matrix(c(1,0.6,0.6,1),2,2)
# sample size 1000
n <- 1000
# generate a sample of size n
m <- ordsample(n, marginal, Sigma, Spearman=TRUE)
head(m)
# sample correlation matrix
cor(rank(m[,1]),rank(m[,2])) # compare it with Sigma
# empirical marginal distributions
cumsum(table(m[,1]))/n
cumsum(table(m[,2]))/n # compare them with the two marginal distributions

# Example 1ter

# draw a sample from a bivariate random variable
# with binomial marginal distributions (n=3, p=1/3 and n=4, p=2/3)
# and Pearson correlation coefficient 0.6 (to be checked)
k <- 2
marginal <- list(pbinom(0:2, 3, 1/3),pbinom(0:3, 4, 2/3))
marginal
corrcheck(marginal, support=list(0:3, 0:4)) # check ok
Sigma <- matrix(c(1,0.6,0.6,1),2,2)
# sample size 1000
n <- 1000
# generate a sample of size n
m <- ordsample(n, marginal, Sigma, support=list(0:3,0:4))
head(m)
# sample correlation matrix
cor(m) # compare it with Sigma
# empirical marginal distributions
cumsum(table(m[,1]))/n
cumsum(table(m[,2]))/n # compare them with the two marginal distributions

# Example 2

# draw a sample from a 4-dimensional ordinal variable
# with different number of categories and uniform marginal distributions
# and different correlation coefficients
k <- 4
marginal <- list(0.5, c(1/3,2/3), c(1/4,2/4,3/4), c(1/5,2/5,3/5,4/5))
corrcheck(marginal)
# select a feasible correlation matrix
Sigma <- matrix(c(1,0.5,0.4,0.3,0.5,1,0.5,0.4,0.4,0.5,1,0.5,0.3,0.4,0.5,1),
4, 4, byrow=TRUE)
Sigma
# sample size 100
n <- 100
# generate a sample of size n
set.seed(1)
m <- ordsample(n, marginal, Sigma)
# sample correlation matrix
cor(m) # compare it with Sigma
# empirical marginal distribution
cumsum(table(m[,4]))/n # compare it with the fourth marginal
head(m)
# or equivalently...
set.seed(1)
res <- ordcont(marginal, Sigma)
res[[1]] # the intermediate correlation matrix of the multivariate normal
m <- ordsample(n, marginal, res[[1]], cormat="continuous")
head(m)

# Example 3
# simulation of two correlated Poisson r.v.
# modification to GenOrd sampling function for Poisson distribution
ordsamplep<-function (n, lambda, Sigma) 
{
   k <- length(lambda)
   valori <- mvrnorm(n, rep(0, k), Sigma)
   for (i in 1:k)
   {
   valori[, i] <- qpois(pnorm(valori[,i]), lambda[i])
   }
   return(valori)
}
# number of variables
k <- 2
# Poisson parameters
lambda <- c(2, 5)
# correlation matrix
Sigma <- matrix(0.25, 2, 2)
diag(Sigma) <- 1
# sample size
n <- 10000
# preliminar stage: support TRUNCATION
# required for recovering the correlation matrix
# of the standard bivariate normal
# truncation error
epsilon <- 0.0001
# corresponding maximum value
kmax <- qpois(1-epsilon, lambda)
# truncated marginals
l <- list()
for(i in 1:k)
{
l[[i]] <- 0:kmax[i]
}
marg <- list()
for(i in 1:k)
{
marg[[i]] <- dpois(0:kmax[i],lambda[i])
marg[[i]][kmax[i]+1] <- 1-sum(marg[[i]][1:(kmax[i])])
}
cm <- list()
for(i in 1:k)
{
cm[[i]] <- cumsum(marg[[i]])
cm[[i]] <- cm[[i]][-(kmax[i]+1)]
}
# check feasibility of correlation matrix
RB <- corrcheck(cm, support=l)
RL <- RB[[1]]
RU <- RB[[2]]
Sigma <= RU & Sigma >= RL # OK
res <- ordcont(cm, Sigma, support=l)
res[[1]]
Sigma <- res[[1]]
# draw the sample
m <- ordsamplep(n, lambda, Sigma)
# sample correlation matrix
cor(m)
head(m)

# Example 4
# simulation of 4 correlated binary and Poisson r.v.'s (2+2)
# modification to GenOrd sampling function
ordsamplep <- function (n, marginal, lambda, Sigma) 
{
k <- length(lambda)
valori <- mvrnorm(n, rep(0, k), Sigma)
for(i in 1:k)
{
if(lambda[i]==0)
{
	valori[, i] <- as.integer(cut(valori[, i], breaks = c(min(valori[,i]) - 1,
               qnorm(marginal[[i]]), max(valori[, i]) + 1)))
valori[, i] <- support[[i]][valori[, i]]
}
else
{
	valori[, i] <- qpois(pnorm(valori[,i]), lambda[i])
}
	}
return(valori)
}
# number of variables
k <- 4
# Poisson parameters (only 3rd and 4th are Poisson)
lambda <- c(0, 0, 2, 5)
# 1st and 2nd are Bernoulli with p=0.5
marginal <- list()
marginal[[1]] <- .5
marginal[[2]] <- .5
marginal[[3]] <- 0
marginal[[4]] <- 0
# support
support <- list()
support[[1]] <- 0:1
support[[2]] <- 0:1
# correlation matrix
Sigma <- matrix(0.25, k, k)
diag(Sigma) <- 1
# sample size
n <- 10000
# preliminar stage: support TRUNCATION
# required for recovering the correlation matrix
# of the standard bivariate normal
# truncation error
epsilon <- 0.0001
# corresponding maximum value
kmax <- qpois(1-epsilon, lambda)
# truncated marginals
for(i in 3:4)
{
support[[i]] <- 0:kmax[i]
}
marg <- list()
for(i in 3:4)
{
marg[[i]] <- dpois(0:kmax[i],lambda[i])
marg[[i]][kmax[i]+1] <- 1-sum(marg[[i]][1:(kmax[i])])
}
for(i in 3:4)
{
marginal[[i]] <- cumsum(marg[[i]])
marginal[[i]] <- marginal[[i]][-(kmax[i]+1)]
}
# check feasibility of correlation matrix
RB <- corrcheck(marginal, support=support)
RL <- RB[[1]]
RU <- RB[[2]]
Sigma <= RU & Sigma >= RL # OK
# compute correlation matrix of the 4-variate standard normal
res <- ordcont(marginal, Sigma, support=support)
res[[1]]
Sigma <- res[[1]]
# draw the sample
m <- ordsamplep(n, marginal, lambda, Sigma)
# sample correlation matrix
cor(m)
head(m)
Documentation reproduced from package GenOrd, version 1.4.0, License: GPL

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