Return factor scores from a model as an easily consumable dataframe.
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.
umxFactorScores(
model,
type = c("ML", "WeightedML", "Regression"),
minManifests = NA,
return = c("Scores", "StandardErrors")
)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. |
dataframe of scores.
Other Reporting Functions:
umxAPA(),
umxGetParameters(),
umxParameters(),
umx_aggregate(),
umx_time(),
umx
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 ## End(Not run)
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