metaSEM: Cor2DataFrame – R documentation

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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, ...)

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`.

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.

Author(s)

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

Examples

## Not run: 
## 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)

## End(Not run)

metaSEM

Meta-Analysis using Structural Equation Modeling

v1.2.5

GPL (>= 2)

Authors

Mike Cheung [aut, cre] (<https://orcid.org/0000-0003-0113-0758>)

Initial release

2020-11-29