findintercorr {SimMultiCorrData}

Calculate Intermediate MVN Correlation for Ordinal, Continuous, Poisson, or Negative Binomial Variables: Correlation Method 1

Description

This function calculates a k x k intermediate matrix of correlations, where k = k_cat + k_cont + k_pois + k_nb, to be used in simulating variables with rcorrvar. The ordering of the variables must be ordinal, continuous, Poisson, and Negative Binomial (note that it is possible for k_cat, k_cont, k_pois, and/or k_nb to be 0). The function first checks that the target correlation matrix rho is positive-definite and the marginal distributions for the ordinal variables are cumulative probabilities with r - 1 values (for r categories). There is a warning given at the end of simulation if the calculated intermediate correlation matrix Sigma is not positive-definite. This function is called by the simulation function rcorrvar, and would only be used separately if the user wants to find the intermediate correlation matrix only. The simulation functions also return the intermediate correlation matrix.

Usage

findintercorr(n, k_cont = 0, k_cat = 0, k_pois = 0, k_nb = 0,
  method = c("Fleishman", "Polynomial"), constants, marginal = list(),
  support = list(), nrand = 100000, lam = NULL, size = NULL,
  prob = NULL, mu = NULL, rho = NULL, seed = 1234, epsilon = 0.001,
  maxit = 1000)

Arguments

n

the sample size (i.e. the length of each simulated variable)

k_cont

the number of continuous variables (default = 0)

k_cat

the number of ordinal (r >= 2 categories) variables (default = 0)

k_pois

the number of Poisson variables (default = 0)

k_nb

the number of Negative Binomial variables (default = 0)

method

the method used to generate the k_cont continuous variables. "Fleishman" uses a third-order polynomial transformation and "Polynomial" uses Headrick's fifth-order transformation.

constants

a matrix with k_cont rows, each a vector of constants c0, c1, c2, c3 (if method = "Fleishman") or c0, c1, c2, c3, c4, c5 (if method = "Polynomial") like that returned by find_constants

marginal

a list of length equal to k_cat; the i-th element is a vector of the cumulative probabilities defining the marginal distribution of the i-th variable; if the variable can take r values, the vector will contain r - 1 probabilities (the r-th is assumed to be 1; default = list())

support

a list of length equal to k_cat; the i-th element is a vector of containing the r ordered support values; if not provided (i.e. support = list()), the default is for the i-th element to be the vector 1, ..., r

nrand

the number of random numbers to generate in calculating the bound (default = 10000)

lam

a vector of lambda (> 0) constants for the Poisson variables (see Poisson)

size

a vector of size parameters for the Negative Binomial variables (see NegBinomial)

prob

a vector of success probability parameters

mu

a vector of mean parameters (*Note: either prob or mu should be supplied for all Negative Binomial variables, not a mixture; default = NULL)

rho

the target correlation matrix (must be ordered ordinal, continuous, Poisson, Negative Binomial; default = NULL)

seed

the seed value for random number generation (default = 1234)

epsilon

the maximum acceptable error between the final and target correlation matrices (default = 0.001) in the calculation of ordinal intermediate correlations with ordnorm

maxit

the maximum number of iterations to use (default = 1000) in the calculation of ordinal intermediate correlations with ordnorm

Value

the intermediate MVN correlation matrix

Overview of Correlation Method 1

The intermediate correlations used in correlation method 1 are more simulation based than those in correlation method 2, which means that accuracy increases with sample size and the number of repetitions. In addition, specifying the seed allows for reproducibility. In addition, method 1 differs from method 2 in the following ways:

1) The intermediate correlation for count variables is based on the method of Yahav & Shmueli (2012, doi: 10.1002/asmb.901), which uses a simulation based, logarithmic transformation of the target correlation. This method becomes less accurate as the variable mean gets closer to zero.

2) The ordinal - count variable correlations are based on an extension of the method of Amatya & Demirtas (2015, doi: 10.1080/00949655.2014.953534), in which the correlation correction factor is the product of the upper Frechet-Hoeffding bound on the correlation between the count variable and the normal variable used to generate it and a simulated upper bound on the correlation between an ordinal variable and the normal variable used to generate it (see Demirtas & Hedeker, 2011, doi: 10.1198/tast.2011.10090).

3) The continuous - count variable correlations are based on an extension of the methods of Amatya & Demirtas (2015) and Demirtas et al. (2012, doi: 10.1002/sim.5362), in which the correlation correction factor is the product of the upper Frechet-Hoeffding bound on the correlation between the count variable and the normal variable used to generate it and the power method correlation between the continuous variable and the normal variable used to generate it (see Headrick & Kowalchuk, 2007, doi: 10.1080/10629360600605065). The intermediate correlations are the ratio of the target correlations to the correction factor.

The processes used to find the intermediate correlations for each variable type are described below. Please see the corresponding function help page for more information:

Ordinal Variables

Correlations are computed pairwise. If both variables are binary, the method of Demirtas et al. (2012, doi: 10.1002/sim.5362) is used to find the tetrachoric correlation (code adapted from Tetra.Corr.BB). This method is based on Emrich and Piedmonte's (1991, doi: 10.1080/00031305.1991.10475828) work, in which the joint binary distribution is determined from the third and higher moments of a multivariate normal distribution: Let Y_{1} and Y_{2} be binary variables with E[Y_{1}] = Pr(Y_{1} = 1) = p_{1}, E[Y_{2}] = Pr(Y_{2} = 1) = p_{2}, and correlation \rho_{y1y2}. Let \Phi[x_{1}, x_{2}, \rho_{x1x2}] be the standard bivariate normal cumulative distribution function, given by:

\Phi[x_{1}, x_{2}, \rho_{x1x2}] = \int_{-\infty}^{x_{1}} \int_{-\infty}^{x_{2}} f(z_{1}, z_{2}, \rho_{x1x2}) dz_{1} dz_{2}

where

f(z_{1}, z_{2}, \rho_{x1x2}) = [2\pi\sqrt{1 - \rho_{x1x2}^2}]^{-1} * exp[-0.5(z_{1}^2 - 2\rho_{x1x2}z_{1}z_{2} + z_{2}^2)/(1 - \rho_{x1x2}^2)]

Then solving the equation

\Phi[z(p_{1}), z(p_{2}), \rho_{x1x2}] = \rho_{y1y2}\sqrt{p_{1}(1 - p_{1})p_{2}(1 - p_{2})} + p_{1}p_{2}

for \rho_{x1x2} gives the intermediate correlation of the standard normal variables needed to generate binary variables with correlation \rho_{y1y2}. Here z(p) indicates the pth quantile of the standard normal distribution.

Otherwise, ordnorm is called for each pair. If the resulting intermediate matrix is not positive-definite, there is a warning given because it may not be possible to find a MVN correlation matrix that will produce the desired marginal distributions after discretization. Any problems with positive-definiteness are corrected later.

Continuous Variables

Correlations are computed pairwise. findintercorr_cont is called for each pair.

Poisson Variables

findintercorr_pois is called to calculate the intermediate MVN correlation for all Poisson variables.

Negative Binomial Variables

findintercorr_nb is called to calculate the intermediate MVN correlation for all Negative Binomial variables.

Continuous - Ordinal Pairs

findintercorr_cont_cat is called to calculate the intermediate MVN correlation for all Continuous and Ordinal combinations.

Ordinal - Poisson Pairs

findintercorr_cat_pois is called to calculate the intermediate MVN correlation for all Ordinal and Poisson combinations.

Ordinal - Negative Binomial Pairs

findintercorr_cat_nb is called to calculate the intermediate MVN correlation for all Ordinal and Negative Binomial combinations.

Continuous - Poisson Pairs

findintercorr_cont_pois is called to calculate the intermediate MVN correlation for all Continuous and Poisson combinations.

Continuous - Negative Binomial Pairs

findintercorr_cont_nb is called to calculate the intermediate MVN correlation for all Continuous and Negative Binomial combinations.

Poisson - Negative Binomial Pairs

findintercorr_pois_nb is called to calculate the intermediate MVN correlation for all Poisson and Negative Binomial combinations.

References

Please see rcorrvar for additional references.

Emrich LJ & Piedmonte MR (1991). A Method for Generating High-Dimensional Multivariate Binary Variables. The American Statistician, 45(4): 302-4. doi: 10.1080/00031305.1991.10475828.

Inan G & Demirtas H (2016). BinNonNor: Data Generation with Binary and Continuous Non-Normal Components. R package version 1.3. https://CRAN.R-project.org/package=BinNonNor

Vale CD & Maurelli VA (1983). Simulating Multivariate Nonnormal Distributions. Psychometrika, 48, 465-471. doi: 10.1007/BF02293687.

See Also

find_constants, rcorrvar

Examples

## Not run: 

# Binary, Ordinal, Continuous, Poisson, and Negative Binomial Variables

options(scipen = 999)
seed <- 1234
n <- 10000

# Continuous Distributions: Normal, t (df = 10), Chisq (df = 4),
# Beta (a = 4, b = 2), Gamma (a = 4, b = 4)
Dist <- c("Gaussian", "t", "Chisq", "Beta", "Gamma")

# calculate standardized cumulants
# those for the normal and t distributions are rounded to ensure the
# correct values (i.e. skew = 0)

M1 <- round(calc_theory(Dist = "Gaussian", params = c(0, 1)), 8)
M2 <- round(calc_theory(Dist = "t", params = 10), 8)
M3 <- calc_theory(Dist = "Chisq", params = 4)
M4 <- calc_theory(Dist = "Beta", params = c(4, 2))
M5 <- calc_theory(Dist = "Gamma", params = c(4, 4))
M <- cbind(M1, M2, M3, M4, M5)
M <- round(M[-c(1:2),], digits = 6)
colnames(M) <- Dist
rownames(M) <- c("skew", "skurtosis", "fifth", "sixth")
means <- rep(0, length(Dist))
vars <- rep(1, length(Dist))

# calculate constants
con <- matrix(1, nrow = ncol(M), ncol = 6)
for (i in 1:ncol(M)) {
 con[i, ] <- find_constants(method = "Polynomial", skews = M[1, i],
                            skurts = M[2, i], fifths = M[3, i],
                            sixths = M[4, i])
}

# Binary and Ordinal Distributions
marginal <- list(0.3, 0.4, c(0.1, 0.5), c(0.3, 0.6, 0.9),
                 c(0.2, 0.4, 0.7, 0.8))
support <- list()

# Poisson Distributions
lam <- c(1, 5, 10)

# Negative Binomial Distributions
size <- c(3, 6)
prob <- c(0.2, 0.8)

ncat <- length(marginal)
ncont <- ncol(M)
npois <- length(lam)
nnb <- length(size)

# Create correlation matrix from a uniform distribution (-0.8, 0.8)
set.seed(seed)
Rey <- diag(1, nrow = (ncat + ncont + npois + nnb))
for (i in 1:nrow(Rey)) {
  for (j in 1:ncol(Rey)) {
    if (i > j) Rey[i, j] <- runif(1, -0.8, 0.8)
    Rey[j, i] <- Rey[i, j]
  }
}

# Test for positive-definiteness
library(Matrix)
if(min(eigen(Rey, symmetric = TRUE)$values) < 0) {
  Rey <- as.matrix(nearPD(Rey, corr = T, keepDiag = T)$mat)
}

# Make sure Rey is within upper and lower correlation limits
valid <- valid_corr(k_cat = ncat, k_cont = ncont, k_pois = npois,
                    k_nb = nnb, method = "Polynomial", means = means,
                    vars = vars, skews = M[1, ], skurts = M[2, ],
                    fifths = M[3, ], sixths = M[4, ], marginal = marginal,
                    lam = lam, size = size, prob = prob, rho = Rey,
                    seed = seed)

# Find intermediate correlation
Sigma1 <- findintercorr(n = n, k_cont = ncont, k_cat = ncat, k_pois = npois,
                        k_nb = nnb, method = "Polynomial", constants = con,
                        marginal = marginal, lam = lam, size = size,
                        prob = prob, rho = Rey, seed = seed)
Sigma1


## End(Not run)