robustfa (version 1.0-4)

computeScores: Compute Factor Scores

Description

Compute factor scores on the result of factor analysis method, the method is one of "mle", "pca", and "pfa".

Usage

computeScores(out, x = data, covmat = covmat, cor = cor, scoresMethod = scoresMethod)

Arguments

out

The result of factorScorePca(), factorScorePfa(), or factanal(). It is a list.

x

A numeric matrix.

covmat

A list with components: cov, center, and n.obs.

cor

A logical value indicating whether the calculation should use the covariance matrix (cor = FALSE) or the correlation matrix (cor = TRUE).

scoresMethod

Type of scores to produce, if any. The default is "none", "regression" gives Thompson's scores, "Bartlett" gives Bartlett's weighted least-squares scores.

Value

• The output is a list. Except for the components of out, it also has components:
• scoringCoef
  The scoring coefficients.
• F
  The matrix of scores.
• meanF
  The sample mean of the scores.
• corF
  The sample correlation matrix of the scores.
• eigenvalues
  The eigenvalues of the running matrix.
• covariance
  The covariance matrix.
• usedMatrix
  The used matrix (running matrix) to compute scoringCoef etc..
• scoringCoef = F = meanF = corF = NULL if scoresMethod = "none".

References

Zhang, Y. Y. (2013), An Object Oriented Solution for Robust Factor Analysis.

Examples

data("stock611")
stock604 = stock611[-c(92,2,337,338,379,539,79), ]
data = as.matrix(stock604[, 3:12])

factors = 2
cor = TRUE
scoresMethod = "regression" 

covx = Cov(data)
covmat = list(cov = getCov(covx), center = getCenter(covx), n.obs = [e-mail])

out = factanal(factors = factors, covmat = covmat)

out = computeScores(out, x = data, covmat = covmat, cor = cor, scoresMethod = scoresMethod)
out