factanal | R Documentation |
Perform maximum-likelihood factor analysis on a covariance matrix or data matrix.
factanal(x, factors, data = NULL, covmat = NULL, n.obs = NA, subset, na.action, start = NULL, scores = c("none", "regression", "Bartlett"), rotation = "varimax", control = NULL, ...)
x |
A formula or a numeric matrix or an object that can be coerced to a numeric matrix. |
factors |
The number of factors to be fitted. |
data |
An optional data frame (or similar: see
|
covmat |
A covariance matrix, or a covariance list as returned by
|
n.obs |
The number of observations, used if |
subset |
A specification of the cases to be used, if |
na.action |
The |
start |
|
scores |
Type of scores to produce, if any. The default is none,
|
rotation |
character. |
control |
A list of control values,
|
... |
Components of |
The factor analysis model is
x = Λ f + e
for a p–element vector x, a p x k matrix Λ of loadings, a k–element vector f of scores and a p–element vector e of errors. None of the components other than x is observed, but the major restriction is that the scores be uncorrelated and of unit variance, and that the errors be independent with variances Psi, the uniquenesses. It is also common to scale the observed variables to unit variance, and done in this function.
Thus factor analysis is in essence a model for the correlation matrix of x,
Σ = Λ Λ' + Ψ
There is still some indeterminacy in the model for it is unchanged if Λ is replaced by G Λ for any orthogonal matrix G. Such matrices G are known as rotations (although the term is applied also to non-orthogonal invertible matrices).
If 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
Jöreskog (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 = Λ' Σ^-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) Λ.
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.
An object of class "factanal"
with components
loadings |
A matrix of loadings, one column for each factor. The
factors are ordered in decreasing order of sums of squares of
loadings, and given the sign that will make the sum of the loadings
positive. This is of class |
uniquenesses |
The uniquenesses computed. |
correlation |
The correlation matrix used. |
criteria |
The results of the optimization: the value of the criterion (a linear function of the negative log-likelihood) and information on the iterations used. |
factors |
The argument |
dof |
The number of degrees of freedom of the factor analysis model. |
method |
The method: always |
rotmat |
The rotation matrix if relevant. |
scores |
If requested, a matrix of scores. |
n.obs |
The number of observations if available, or |
call |
The matched call. |
na.action |
If relevant. |
STATISTIC, PVAL |
The significance-test statistic and P value, if it can be computed. |
There are so many variations on factor analysis that it is hard to compare output from different programs. Further, the optimization in maximum likelihood factor analysis is hard, and many other examples we compared had less good fits than produced by this function. In particular, solutions which are ‘Heywood cases’ (with one or more uniquenesses essentially zero) are much more common than most texts and some other programs would lead one to believe.
Bartlett, M. S. (1937). The statistical conception of mental factors. British Journal of Psychology, 28, 97–104. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.1111/j.2044-8295.1937.tb00863.x")}.
Bartlett, M. S. (1938). Methods of estimating mental factors. Nature, 141, 609–610. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.1038/141246a0")}.
Jöreskog, K. G. (1963). Statistical Estimation in Factor Analysis. Almqvist and Wicksell.
Lawley, D. N. and Maxwell, A. E. (1971). Factor Analysis as a Statistical Method. Second edition. Butterworths.
Thomson, G. H. (1951). The Factorial Analysis of Human Ability. London University Press.
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 GPArotation and 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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