Usage
fa.multi(r, nfactors = 3, nfact2 = 1, n.obs = NA, n.iter = 1, rotate = "oblimin",
scores = "regression", residuals = FALSE, SMC = TRUE, covar = FALSE, missing =
FALSE,impute = "median", min.err = 0.001, max.iter = 50, symmetric = TRUE, warnings
=TRUE, fm = "minres", alpha = 0.1, p = 0.05, oblique.scores = FALSE, np.obs = NULL,
use ="pairwise", cor = "cor", ...)fa.multi.diagram(multi.results,sort=TRUE,labels=NULL,flabels=NULL,cut=.2,gcut=.2,
simple=TRUE,errors=FALSE,
digits=1,e.size=.1,rsize=.15,side=3,main=NULL,cex=NULL,color.lines=TRUE
,marg=c(.5,.5,1.5,.5),adj=2, ...)
Arguments
r
A correlation matrix or raw data matrix
nfactors
The desired number of factors for the lower level
nfact2
The desired number of factors for the higher level
n.obs
Number of observations used to find the correlation matrix if using a correlation matrix. Used for finding the goodness of fit statistics. Must be specified if using a correlaton matrix and finding confidence intervals.
np.obs
The pairwise number of observations. Used if using a correlation matrix and asking for a minchi solution.
rotate
"none", "varimax", "quartimax", "bentlerT", "equamax", "varimin", "geominT" and "bifactor" are orthogonal rotations. "promax", "oblimin", "simplimax", "bentlerQ, "geominQ" and "biquartimin" and "cluster" are possible oblique transformations of the solu
n.iter
Number of bootstrap interations to do in fa or fa.poly
residuals
Should the residual matrix be shown
scores
the default="regression" finds factor scores using regression. Alternatives for estimating factor scores include simple regression ("Thurstone"), correlaton preserving ("tenBerge") as well as "Anderson" and "Bartlett" using the appropriate algorithms (s
SMC
Use squared multiple correlations (SMC=TRUE) or use 1 as initial communality estimate. Try using 1 if imaginary eigen values are reported. If SMC is a vector of length the number of variables, then these values are used as starting values in the case of
covar
if covar is TRUE, factor the covariance matrix, otherwise factor the correlation matrix
missing
if scores are TRUE, and missing=TRUE, then impute missing values using either the median or the mean
impute
"median" or "mean" values are used to replace missing values
min.err
Iterate until the change in communalities is less than min.err
max.iter
Maximum number of iterations for convergence
symmetric
symmetric=TRUE forces symmetry by just looking at the lower off diagonal values
warnings
warnings=TRUE => warn if number of factors is too many
fm
factoring method fm="minres" will do a minimum residual (OLS), fm="wls" will do a weighted least squares (WLS) solution, fm="gls" does a generalized weighted least squares (GLS), fm="pa" will do the principal factor solution, fm="ml" will do a maximum li
alpha
alpha level for the confidence intervals for RMSEA
p
if doing iterations to find confidence intervals, what probability values should be found for the confidence intervals
oblique.scores
When factor scores are found, should they be based on the structure matrix (default) or the pattern matrix (oblique.scores=TRUE).
use
How to treat missing data, use="pairwise" is the default". See cor for other options.
cor
How to find the correlations: "cor" is Pearson", "cov" is covariance,
"tet" is tetrachoric, "poly" is polychoric, "mixed" uses mixed cor for a mixture of tetrachorics, polychorics, Pearsons, biserials, and polyserials, Yuleb is Yulebonett, Yuleq and Yu
multi.results
The results from fa.multi
flabels
Labels for the factors (not counting g)
size
size of graphics window
digits
Precision of labels
color.lines
Use black for positive, red for negative
marg
The margins for the figure are set to be wider than normal by default
adj
Adjust the location of the factor loadings to vary as factor mod 4 + 1
...
additional parameters, specifically, keys may be passed if using the target rotation, or delta if using geominQ, or whether to normalize if using Varimax. In addition, for fa.multi.diagram, other options to pass into the graphics packages
e.size
the size to draw the ellipses for the factors. This is scaled by the number of variables.
cut
Minimum path coefficient to draw
gcut
Minimum general factor path to draw
simple
draw just one path per item
sort
sort the solution before making the diagram
side
on which side should errors be drawn?
errors
show the error estimates
rsize
size of the rectangles