OpenMx: mxDescribeDataWLS – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

mxDescribeDataWLS

Determine whether a dataset will have weights and summary statistics for the means if used with mxFitFunctionWLS

Description

Given either a data.frame or an mxData of type raw, this function determines whether mxFitFunctionWLS will generate expectations for means.

Usage

mxDescribeDataWLS(
  data,
  allContinuousMethod = c("cumulants", "marginals"),
  verbose = FALSE
)

Arguments

`data`	the (currently raw) data being used in a `mxFitFunctionWLS` model.
`allContinuousMethod`	the method used to process data when all columns are continuous.
`verbose`	logical. Whether to report diagnostics.

Details

All-continuous data processed using the "cumulants" method lack means, while all continuous data processed with allContinuousMethod = "marginals" will have means.

When data are not all continuous, allContinuousMethod is ignored, and means are modelled.

Value

- list describing the data.

Examples

# ====================================
# = All continuous, data.frame input =
# ====================================

tmp = mxDescribeDataWLS(mtcars, allContinuousMethod= "cumulants", verbose = TRUE)
tmp$hasMeans # FALSE - no means with cumulants
tmp = mxDescribeDataWLS(mtcars, allContinuousMethod= "marginals") 
tmp$hasMeans # TRUE we get means with marginals

# ==========================
# = mxData object as input =
# ==========================
tmp = mxData(mtcars, type="raw")
mxDescribeDataWLS(tmp, allContinuousMethod= "cumulants", verbose = TRUE)$hasMeans # FALSE
mxDescribeDataWLS(tmp, allContinuousMethod= "marginals")$hasMeans  # TRUE

# =======================================
# = One var is a factor: Means modelled =
# =======================================
tmp = mtcars
tmp$cyl = factor(tmp$cyl)
mxDescribeDataWLS(tmp, allContinuousMethod= "cumulants")$hasMeans # TRUE - always has means
mxDescribeDataWLS(tmp, allContinuousMethod= "marginals")$hasMeans # TRUE

OpenMx

Extended Structural Equation Modelling

v2.19.5

Apache License (== 2.0)

Authors

Steven M. Boker [aut], Michael C. Neale [aut], Hermine H. Maes [aut], Michael J. Wilde [ctb], Michael Spiegel [aut], Timothy R. Brick [aut], Ryne Estabrook [aut], Timothy C. Bates [aut], Paras Mehta [ctb], Timo von Oertzen [ctb], Ross J. Gore [aut], Michael D. Hunter [aut], Daniel C. Hackett [ctb], Julian Karch [ctb], Andreas M. Brandmaier [ctb], Joshua N. Pritikin [aut, cre], Mahsa Zahery [aut], Robert M. Kirkpatrick [aut], Yang Wang [ctb], Ben Goodrich [ctb], Charles Driver [ctb], Massachusetts Institute of Technology [cph], S. G. Johnson [cph], Association for Computing Machinery [cph], Dieter Kraft [cph], Stefan Wilhelm [cph], Sarah Medland [cph], Carl F. Falk [cph], Matt Keller [cph], Manjunath B G [cph], The Regents of the University of California [cph], Lester Ingber [cph], Wong Shao Voon [cph], Juan Palacios [cph], Jiang Yang [cph], Gael Guennebaud [cph], Jitse Niesen [cph]

Initial release

2021-03-26