cor_test {BFpack}

Bayesian correlation analysis

Description

Estimate the unconstrained posterior for the correlations using a joint uniform prior.

Usage

cor_test(..., formula = NULL, iter = 5000, burnin = 3000, nugget.scale = 0.995)

Arguments

...

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 formula. This allows for including control variables in the model (e.g., ~ education).

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).

Value

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

Examples

# 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)