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, cor.analysis=TRUE,
                acov=c("weighted", "individual", "unweighted"),
                Means, row.names.unique=FALSE, append.vars=TRUE,
                asyCovOld=FALSE, ...)

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.
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.
Means: An optional matrix of means. The number of rows must be the same as the length of n. The sampling covariance matrices of the means are calculated by the covariance matrices divided by the sample sizes. Therefore, it is important to make sure that covariance matrices (not correlation matrices) are used in x when Means are included; otherwise, the calculated sampling covariance matrices of the means are incorrect.
row.names.unique: Logical, If it is FALSE (the default), unique row names are not created.
append.vars: Whether to append the additional variables to the output dataframe.
asyCovOld: Whether to use the old version of asyCov. See asyCov.
...: Further arguments to be passed to asyCov.

Author

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

Examples

Run this code

# \donttest{
## 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)
# }