cor_test | R Documentation |
Estimate the unconstrained posterior for the correlations using a joint uniform prior.
cor_test(..., formula = NULL, iter = 5000, burnin = 3000, nugget.scale = 0.995)
... |
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 which serves to avoid violations of positive definite correlation matrices. It should be very close to 1 (the default is .995). |
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
# 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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