umx (version 4.20.0)

umxFactorScores: Return factor scores from a model as an easily consumable dataframe.

Description

umxFactorScores takes a model, and computes factors scores using the selected method (one of 'ML', 'WeightedML', or 'Regression') It is a simple wrapper around mxFactorScores. For missing data, you must specify the least number of variables allowed for a score (subjects with fewer than minManifests will return a score of NA.

Usage

umxFactorScores(
  model,
  type = c("ML", "WeightedML", "Regression"),
  minManifests = NA,
  return = c("Scores", "StandardErrors")
)

Value

• dataframe of scores.

Arguments

model

The model from which to generate scores.

type

Method of computing the score ('ML', 'WeightedML', or 'Regression').

minManifests

The minimum number of variables not NA to return a score for a participant (Default = ask).

return

What to return (defaults to "Scores", which is what most users want, but can return "StandardErrors" on each score.

References

• https://github.com/tbates/umx, https://tbates.github.io

See Also

• mxFactorScores()

Other Reporting Functions: umxAPA(), umxGetLatents(), umxGetManifests(), umxGetModel(), umxGetParameters(), umxParameters(), umx_aggregate(), umx_time(), umx

Examples

if (FALSE) {
m1 = umxEFA(mtcars, factors = 2)
x = umxFactorScores(m1, type = 'Regression', minManifests = 3)

# =========================================================================
# = histogram of F1 and plot of F1 against F2 showing they are orthogonal =
# =========================================================================
hist(x$F1)
plot(F1 ~ F2, data = x)

m1 = umxEFA(mtcars, factors = 1)
x = umxFactorScores(m1, type = 'Regression', minManifests = 3)
x
}