cor_test | R Documentation |
Estimate the unconstrained posterior for the correlations using a joint uniform prior (Mulder and Gelissen, 2023) or a marginally uniform prior (Barnard et al., 2000, Mulder, 2016). Correlation matrices are sampled from the posterior using the MCMC algorithm of Talhouk et al. (2012).
cor_test(..., formula = NULL, iter = 5000, burnin = 3000, nugget.scale = 0.999)
... |
matrices (or data frames) of dimensions n (observations) by p (variables) for different groups (in case of multiple matrices or data frames). |
formula |
an object of class |
iter |
number of iterations from posterior (default is 5000). |
burnin |
number of iterations for burnin (default is 3000). |
nugget.scale |
a scalar to avoid computational issues due to posterior draws for the corralations
too close to 1 in absolute value. Posterior draws for the correlations are multiplied with this nugget.scale.
So |
list of class cor_test
:
meanF
posterior means of Fisher transform correlations
covmF
posterior covariance matrix of Fisher transformed correlations
correstimates
posterior estimates of correlation coefficients
corrdraws
list of posterior draws of correlation matrices per group
corrnames
names of all correlations
Barnard, J., McCulloch, R., & Meng, X. L. (2000). Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage. Statistica Sinica, 1281-1311. <https://www.jstor.org/stable/24306780>
Joe, H. (2006). Generating random correlation matrices based on partial correlations. Journal of Multivariate Analysis, 97(10), 2177-2189. <https://doi.org/10.1016/j.jmva.2005.05.010>
Mulder, J., & Gelissen, J. P. (2023). Bayes factor testing of equality and order constraints on measures of association in social research. Journal of Applied Statistics, 50(2), 315-351. <https://doi.org/10.1080/02664763.2021.1992360>
Mulder, J. (2016). Bayes factors for testing order-constrained hypotheses on correlations. Journal of Mathematical Psychology, 72, 104-115. <https://doi.org/10.1016/j.jmp.2014.09.004>
Talhouk, A., Doucet, A., & Murphy, K. (2012). Efficient Bayesian inference for multivariate probit models with sparse inverse correlation matrices. Journal of Computational and Graphical Statistics, 21(3), 739-757. <https://doi.org/10.1080/10618600.2012.679239>
# Bayesian correlation analysis of the 6 variables in 'memory' object
# we consider a correlation analysis of the first three variable of the memory data.
fit <- cor_test(BFpack::memory[,1:3])
# Bayesian correlation of variables in memory object in BFpack while controlling
# for the Cat variable
fit <- cor_test(BFpack::memory[,c(1:4)],formula = ~ Cat)
# Example of Bayesian estimation of polyserial correlations
memory_example <- memory[,c("Im","Rat")]
memory_example$Rat <- as.ordered(memory_example$Rat)
fit <- cor_test(memory_example)
# Bayesian correlation analysis of first three variables in memory data
# for two different groups
HC <- subset(BFpack::memory[,c(1:3,7)], Group == "HC")[,-4]
SZ <- subset(BFpack::memory[,c(1:3,7)], Group == "SZ")[,-4]
fit <- cor_test(HC,SZ)
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