R in a Nutshell, 2nd Edition

Factor Analysis

In most data analysis problems, there are some quantities that we can observe and some that we cannot. The classic examples come from the social sciences. Suppose that you wanted to measure intelligence. It’s not possible to directly measure an abstract concept like intelligence, but it is possible to measure performance on different tests. You could use factor analysis to analyze a set of test scores (the observed values) to try to determine intelligence (the hidden value).

Factor analysis is available in R through the function factanal in the stats package:

factanal(x, factors, data = NULL, covmat = NULL, n.obs = NA,
         subset, na.action, start = NULL,
         scores = c("none", "regression", "Bartlett"),
         rotation = "varimax", control = NULL, ...)

Here is a description of the arguments to factanal.

Argument	Description	Default
x	A formula or a numeric matrix to be used for analysis.
factors	A numeric value indicating the number of factors to be fitted.
data	A data frame in which to evaluate x (if x is a formula).	NULL
covmat	A covariance matrix (or a list returned by cov.wt).	NULL
n.obs	The number of observations (if covmat is specified).	NA
subset	Specifies which observations to include in the analysis.
na.action	A function that specifies how to handle missing observations (if x is a formula).
start	A matrix of starting values for the algorithm.	NULL
scores	A character value specifying the type of scores to produce. Use scores="none" for no scores, scores="regression" for Thompson’s scores, or ...