umx (version 1.2.5)

umxEFA: umxEFA

Description

Perform full-information maximum-likelihood factor analysis on a data matrix. You need only offer up manifest data, specify the number of factors.

Usage

umxEFA(x = NULL, factors = NULL, data = NULL, covmat = NULL,
  n.obs = NULL, rotation = c("varimax", "promax", "none"), name = "efa",
  digits = 2, report = c("1", "table", "html"))

Arguments

x

Either 1: data, 2: A formula (not implemented yet), 3: A collection of variable names, or 4: A name for the model.

factors

Either number of factors to request or a vector of factor names.

data

A dataframe of manifest columns you are modeling

covmat

Covariance matrix of data you are modeling (not implemented)

n.obs

Number of observations in covmat (if provided, default = NA)

rotation

A rotation to perform on the loadings (default = "varimax")

name

A name for your model.

digits

rounding (default = 2)

report

What to report

Value

• - EFA mxModel

Details

You can create an EFA either by specifying some factor names:

umxEFA(factors = c("g", "v"), data = mtcars)

or

umxEFA(factors = 2, data = mtcars)

umxEFA.png

notes: In an EFA, all items may load on all factors. For identification we need m^2 degrees of freedom. We get m * (m+1)/2 from fixing factor variances to 1 and covariances to 0. We get another m(m-1)/2 degrees of freemdom by fixing the upper-right hand corner of the factor loadings component of the A matrix. The manifest variances are also lbounded at 0.

EFA reports standardized loadings: to do this, we scale the data.

Bear in mind that factor scores are indeterminate and can be rotated.

This is very much early days. I will add "scores" if there is demand. Currently, you can get scores from with mxFactorScores.

todo: detect ordinal items and switch to UWLS

References

- http://github.com/tbates/umx

See Also

- factanal

Other Super-easy helpers: umxJiggle, umx

Examples

myVars <- c("mpg", "disp", "hp", "wt", "qsec")
m1 = umxEFA(mtcars[, myVars], factors =   2, rotation = "promax")
loadings(m1)
m2 = factanal(~ mpg + disp + hp + wt + qsec, factors = 2, rotation = "promax", data = mtcars)
loadings(m2)
plot(m2)
m3 = umxEFA(myVars, factors = 2, data = mtcars, rotation = "promax")
m4 = umxEFA(name = "named", factors = "g", data = mtcars[, myVars])
m5 = umxEFA(name = "by_number", factors = 2, rotation = "promax", data = mtcars[, myVars])