factanal(x, factors, data = NULL, covmat = NULL, n.obs = NA,
subset, na.action, start = NULL,
scores = c("none", "regression", "Bartlett"),
rotation = "varimax", control = NULL, …)
model.frame
), used only if x
is a formula. By
default the variables are taken from environment(formula)
.cov.wt
. Of course, correlation matrices are covariance
matrices.covmat
is a
covariance matrix.x
is
used as a matrix or formula.na.action
to be used if x
is
used as a formula.NULL
or a matrix of starting values, each column
giving an initial set of uniquenesses."regression"
gives Thompson's scores, "Bartlett"
given
Bartlett's weighted least-squares scores. Partial matching allows
these names to be abbreviated."none"
or the name of a function
to be used to rotate the factors: it will be called with first
argument the loadings matrix, and should return a list with component
loadings
giving the rotated loadings, or just the rotated loadings.start = NULL
. Default 1.FALSE
.optim
's control
argument.
control
can also be supplied as
named arguments to factanal
."factanal"
with components
"loadings"
: see
loadings
for its print
method.factors
."mle"
.napredict
is
applied to handle the treatment of values omitted by the na.action
.NA
.covmat
is supplied it is used. Otherwise x
is used
if it is a matrix, or a formula x
is used with data
to
construct a model matrix, and that is used to construct a covariance
matrix. (It makes no sense for the formula to have a response, and
all the variables must be numeric.) Once a covariance matrix is found
or calculated from x
, it is converted to a correlation matrix
for analysis. The correlation matrix is returned as component
correlation
of the result. The fit is done by optimizing the log likelihood assuming multivariate
normality over the uniquenesses. (The maximizing loadings for given
uniquenesses can be found analytically: Lawley & Maxwell (1971,
p. 27).) All the starting values supplied in start
are tried
in turn and the best fit obtained is used. If start = NULL
then the first fit is started at the value suggested by
J<U+00F6>reskog (1963) and given by Lawley & Maxwell
(1971, p. 31), and then control$nstart - 1
other values are
tried, randomly selected as equal values of the uniquenesses. The uniquenesses are technically constrained to lie in \([0, 1]\),
but near-zero values are problematical, and the optimization is
done with a lower bound of control$lower
, default 0.005
(Lawley & Maxwell, 1971, p. 32). Scores can only be produced if a data matrix is supplied and used.
The first method is the regression method of Thomson (1951), the
second the weighted least squares method of Bartlett (1937, 8).
Both are estimates of the unobserved scores \(f\). Thomson's method
regresses (in the population) the unknown \(f\) on \(x\) to yield
$$\hat f = \Lambda^\prime \Sigma^{-1} x$$
and then substitutes the sample estimates of the quantities on the
right-hand side. Bartlett's method minimizes the sum of squares of
standardized errors over the choice of \(f\), given (the fitted)
\(\Lambda\). If x
is a formula then the standard NA
-handling is
applied to the scores (if requested): see napredict
. The print
method (documented under loadings
)
follows the factor analysis convention of drawing attention to the
patterns of the results, so the default precision is three decimal
places, and small loadings are suppressed.loadings
(which explains some details of the
print
method), varimax
, princomp
,
ability.cov
, Harman23.cor
,
Harman74.cor
. Other rotation methods are available in various contributed packages,
including https://CRAN.R-project.org/package=GPArotation and https://CRAN.R-project.org/package=psych.# A little demonstration, v2 is just v1 with noise,
# and same for v4 vs. v3 and v6 vs. v5
# Last four cases are there to add noise
# and introduce a positive manifold (g factor)
v1 <- c(1,1,1,1,1,1,1,1,1,1,3,3,3,3,3,4,5,6)
v2 <- c(1,2,1,1,1,1,2,1,2,1,3,4,3,3,3,4,6,5)
v3 <- c(3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,5,4,6)
v4 <- c(3,3,4,3,3,1,1,2,1,1,1,1,2,1,1,5,6,4)
v5 <- c(1,1,1,1,1,3,3,3,3,3,1,1,1,1,1,6,4,5)
v6 <- c(1,1,1,2,1,3,3,3,4,3,1,1,1,2,1,6,5,4)
m1 <- cbind(v1,v2,v3,v4,v5,v6)
cor(m1)
factanal(m1, factors = 3) # varimax is the default
factanal(m1, factors = 3, rotation = "promax")
# The following shows the g factor as PC1
prcomp(m1) # signs may depend on platform
## formula interface
factanal(~v1+v2+v3+v4+v5+v6, factors = 3,
scores = "Bartlett")$scores
## a realistic example from Bartholomew (1987, pp. 61-65)
utils::example(ability.cov)
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