umx (version 4.0.0)

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")
)

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.

Value

• dataframe of scores.

References

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

See Also

• mxFactorScores()

Other Reporting Functions: loadings.MxModel(), umxAPA(), umxGetParameters(), umxParameters(), umxReduce(), umx_aggregate(), umx_names(), umx_time(), umx

Examples

# NOT RUN {
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)

# }
# NOT RUN {
m1 = umxEFA(mtcars, factors = 1)
x = umxFactorScores(m1, type = 'Regression', minManifests = 3)
x
# }