R: Automatically compute an apppropriate correlation matrix

cor_auto {qgraph} | R Documentation

## Automatically compute an apppropriate correlation matrix

### Description

This is mainly a wrapper around Lavaan function `lavCor` (Rosseel, 2012) to compute a correlation matrix based on pychoric, polyserial and/or Pearson correlations. The wrapper removes all factors and searches for possible ordinal variabes. A variable is classified as ordinal if it is either `ordered` or if it consist of at most 7 unique integer values. After computing the correlations an additional check will be performed to see if the correlation matrix is positive definite.

### Usage

    cor_auto(data, select, detectOrdinal = TRUE, ordinalLevelMax = 7, npn.SKEPTIC = FALSE,
              forcePD = FALSE, missing = "pairwise", verbose = TRUE)

### Arguments

`data` | A data frame
`select` | Variables to select from the data frame (as used in `subset`)
`detectOrdinal` | Logical, should ordinal variables be detected? If `FALSE` only variables that are `ordered` are treated as ordinal variables
`ordinalLevelMax` | Integer specyfying the amount of unique integer values a variable should have to be classified as ordinal
`npn.SKEPTIC` | Logical, should the Nonparanormal SKEPTIC from the huge package be applied if the data is continuous? See `huge.npn` (Zhao, Liu, Roeder, Lafferty and Wasserman, 2014)
`forcePD` | If `TRUE` the function checks if the correlation matrix is positive definite. If the matrix is not positive definite `nearPD` from the Matrix package will be used (Bates and Maechler, 2014).
`missing` | Corresponds to the `missing` argument in `lavCor`
`verbose` | Logical, should information be printed to the console?

### Value

A correlation matrix

### Author(s)

Sacha Epskamp <mail@sachaepskamp.com>

### References

Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. URL http://www.jstatsoft.org/v48/i02/.

Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty and Larry Wasserman (2014). huge: High-dimensional Undirected Graph Estimation. R package version 1.2.6. http://CRAN.R-project.org/package=huge

Douglas Bates and Martin Maechler (2014). Matrix: Sparse and Dense Matrix Classes and Methods. R package version 1.1-3. http://CRAN.R-project.org/package=Matrix

### Examples

    ## Not run:
    ### Examples from lavCor (lavaan): ###

    library("lavaan")

    # Holzinger and Swineford (1939) example
    HS9 <- HolzingerSwineford1939[,c("x1","x2","x3","x4","x5",
                                     "x6","x7","x8","x9")]

    # Pearson correlations
    cor_auto(HS9)

    # ordinal version, with three categories
    HS9ord <- as.data.frame( lapply(HS9, cut, 3, labels=FALSE) )

    # polychoric correlations, two-stage estimation
    cor_auto(HS9ord)

    ## End(Not run)

[Package qgraph version 1.9 Index]