View source: R/umx_build_high_level_models.R
umxFactorScores | R Documentation |
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.
mxFactorScores()
Other Reporting Functions:
umxAPA()
,
umxGetLatents()
,
umxGetManifests()
,
umxGetModel()
,
umxGetParameters()
,
umxParameters()
,
umx_aggregate()
,
umx_time()
,
umx
## 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)
m1 = umxEFA(mtcars, factors = 1)
x = umxFactorScores(m1, type = 'Regression', minManifests = 3)
x
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.