Cor2DataFrame: Convert correlation or covariance matrices into a dataframe of correlations or covariances with their sampling covariance matrices

Description

It converts the correlation or covariance matrices into a dataframe of correlations or covariances with their asymptotic sampling covariance matrices. It uses the asyCov at the backend.

Usage

Cor2DataFrame(x, n, v.na.replace = TRUE, row.names.unique = FALSE,
              cor.analysis = TRUE, acov="weighted", append.vars=TRUE, ...)

Value

A list of components: (1) a data frame of correlations or covariances with their sampling covariance matrices; (2) a vector of sample sizes; (3) labels of the correlations; and (3) labels of their sampling covariance matrices.

Arguments

x: A list of data with correlation/covariance matrix in x$data and sample sizes x$n. Additional variables in x can be attached.
n: If x is a list of correlation matrices without x$data and x$n, a vector of sample sizes n must be provided.
v.na.replace: Logical. Missing value is not allowed in definition variables. If it is TRUE (the default), missing value is replaced by a large value (1e10). These values are not used in the analysis.
row.names.unique: Logical, If it is FALSE (the default), unique row names are not created.
cor.analysis: Logical. The output is either a correlation or covariance matrix.
acov: If it is weighted, the average correlation/covariance matrix is calculated based on the weighted mean with the sample sizes. The average correlation/covariance matrix is used to calculate the sampling variance-covariance matrices.
append.vars: Whether to append the additional variables to the output dataframe.
...: Further arguments to be passed to asyCov.

Author

Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>

Examples

Run this code

if (FALSE) {
## Provide a list of correlation matrices and a vector of sample sizes as the inputs
my.df1 <- Cor2DataFrame(Nohe15A1$data, Nohe15A1$n)

## Add Lag time as a variable
my.df1$data <- data.frame(my.df1$data, Lag=Nohe15A1$Lag, check.names=FALSE)

## Data
my.df1$data

## Sample sizes
my.df1$n

## ylabels
my.df1$ylabels

## vlabels
my.df1$vlabels

#### Simplified version to do it
my.df2 <- Cor2DataFrame(Nohe15A1)
}