29     sortLoadings <- function(Lambda)  argument
31         cn <- colnames(Lambda)
32         Phi <- attr(Lambda, "covariance")
33         ssq <- apply(Lambda, 2L, function(x) -sum(x^2))
34         Lambda <- Lambda[, order(ssq), drop = FALSE]
35         colnames(Lambda) <- cn
36         neg <- colSums(Lambda) < 0
37         Lambda[, neg] <- -Lambda[, neg]
40             attr(Lambda, "covariance") <-
43         Lambda
154         Lambda <- fit$loadings  functionVar
158                    sc <- zz %*% solve(cv, Lambda)
159                    if(!is.null(Phi <- attr(Lambda, "covariance")))
164                    tmp <- t(Lambda * d)
165                    sc <- t(solve(tmp %*% Lambda, tmp %*% t(zz)))
168         colnames(sc) <- colnames(Lambda)
223     Lambda <- FAout(res$par, cmat, factors)  functionVar
224     dimnames(Lambda) <- list(dimnames(cmat)[[1L]],
229     class(Lambda) <- "loadings"
231                 loadings = Lambda, uniquenesses = un,
241     Lambda <- unclass(x)  functionVar
242     p <- nrow(Lambda)
243     factors <- ncol(Lambda)
245         mx <- max.col(abs(Lambda))
247         mx[abs(Lambda[ind]) < 0.5] <- factors + 1
248         Lambda <- Lambda[order(mx, 1L:p),]
251     fx <- setNames(format(round(Lambda, digits)), NULL)
253     fx[abs(Lambda) < cutoff] <- strrep(" ", nc)