View source: R/Cor2DataFrame.R
Cor2DataFrame | R Documentation |
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.
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, ...)
x |
A list of data with correlation/covariance matrix in |
n |
If |
v.na.replace |
Logical. Missing value is not allowed in definition
variables. If it is |
cor.analysis |
Logical. The output is either a correlation or covariance matrix. |
acov |
If it is |
Means |
An optional matrix of means. The number of rows must be the same as the length of |
row.names.unique |
Logical, If it is |
append.vars |
Whether to append the additional variables to the output dataframe. |
asyCovOld |
Whether to use the old version of |
... |
Further arguments to be passed to |
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.
Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>
asyCov
, osmasem
, create.vechsR
,
create.Tau2
, create.V
## 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)
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