Description Usage Arguments Value References See Also Examples
This function approximates the expected correlation between two continuous mixture variables M1 and M2 based on their mixing proportions, component means, component standard deviations, and correlations between components across variables. The equations can be found in the Expected Cumulants and Correlations for Continuous Mixture Variables vignette. This function can be used to see what combination of component correlations gives a desired correlation between M1 and M2.
1 2 
mix_pis 
a list of length 2 with 1st component a vector of mixing probabilities that sum to 1 for component distributions of M1 and likewise for 2nd component and M2 
mix_mus 
a list of length 2 with 1st component a vector of means for component distributions of M1 and likewise for 2nd component and M2 
mix_sigmas 
a list of length 2 with 1st component a vector of standard deviations for component distributions of M1 and likewise for 2nd component and M2 
p_M1M2 
a matrix of correlations with rows corresponding to M1 and columns corresponding to M2; i.e.,

the expected correlation between M1 and M2
Davenport JW, Bezder JC, & Hathaway RJ (1988). Parameter Estimation for Finite Mixture Distributions. Computers & Mathematics with Applications, 15(10):81928.
Pearson RK (2011). Exploring Data in Engineering, the Sciences, and Medicine. In. New York: Oxford University Press.
1 2 3 4 5 6 7 8 9 10  # M1 is mixture of N(2, 1) and N(2, 1);
# M2 is mixture of Logistic(0, 1), Chisq(4), and Beta(4, 1.5)
# pairwise correlation between components across M1 and M2 set to 0.35
L < calc_theory("Logistic", c(0, 1))
C < calc_theory("Chisq", 4)
B < calc_theory("Beta", c(4, 1.5))
rho_M1M2(mix_pis = list(c(0.4, 0.6), c(0.3, 0.2, 0.5)),
mix_mus = list(c(2, 2), c(L[1], C[1], B[1])),
mix_sigmas = list(c(1, 1), c(L[2], C[2], B[2])),
p_M1M2 = matrix(0.35, 2, 3))

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.