| /dports/math/R-cran-psych/psych/R/ |
| H A D | factor.pa.R | 15 model <- loadings %*% t(loadings)
95 model <- loadings %*% t(loadings)
118 loadings <- uls$loadings
132 loadings <- loadings %*% diag(sign.max)
138 loadings <- as.matrix(loadings)
147 …} else { if (sum(loadings) <0) {loadings <- -as.matrix(loadings)} else {loadings <- as.matrix(load…
156 model <- loadings %*% t(loadings)
165 loadings <- rotated$loadings
178 loadings <- ob$loadings
188 loadings <- loadings[,ev.order]}
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| H A D | factor.minres.R | 13 model <- loadings %*% t(loadings)
24 model <- loadings %*% t(loadings)
104 model <- loadings %*% t(loadings)
126 loadings <- uls$loadings
146 loadings <- as.matrix(loadings)
155 …} else { if (sum(loadings) <0) {loadings <- -as.matrix(loadings)} else {loadings <- as.matrix(load…
164 model <- loadings %*% t(loadings)
172 loadings <- rotated$loadings
185 loadings <- ob$loadings
197 loadings <- loadings[,ev.order]}
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| H A D | factor.wls.R | 14 model <- loadings %*% t(loadings)
97 model <- loadings %*% t(loadings)
119 loadings <- uls$loadings
133 loadings <- loadings %*% diag(sign.max)
139 loadings <- as.matrix(loadings)
148 …} else { if (sum(loadings) <0) {loadings <- -as.matrix(loadings)} else {loadings <- as.matrix(load…
157 model <- loadings %*% t(loadings)
165 loadings <- rotated$loadings
178 loadings <- ob$loadings
190 loadings <- loadings[,ev.order]}
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| H A D | principal.R | 30 if(nfactors >0) {loadings <- loadings[,1:nfactors]} else {nfactors <- n}
41 loadings <- loadings %*% diag(sign.tot)
42 …} else { if (sum(loadings) <0) {loadings <- -as.matrix(loadings)} else {loadings <- as.matrix(load…
56 loadings <- rotated$loadings
60 loadings <- pro$loadings
62 if (rotate == "cluster") {loadings <- varimax(loadings)$loadings
64 loadings <- pro$loadings
73 loadings <- ob$loadings
81 ev.rotated <- diag(t(loadings) %*% loadings)
83 loadings <- loadings[,ev.order]}
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| H A D | fa.R | 103 model <- loadings %*% t(loadings)
239 model <- loadings %*% t(loadings)
262 loadings <- uls$loadings
263 model <- loadings %*% t(loadings)
282 …} else { if (sum(loadings) <0) {loadings <- -as.matrix(loadings)} else {loadings <- as.matrix(load…
291 model <- loadings %*% t(loadings)
301 loadings <- rotated$loadings
309 loadings <- pro$loadings
318 loadings <- ob$loadings
335 loadings <- loadings[,ev.order]}
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| H A D | ICLUST.sort.R | 2 if(is.matrix(ic.load)) {loadings <- ic.load} else { loadings <- ic.load$loadings} functionVar
4 nclust <- dim(loadings)[2]
5 nitems <- dim(loadings)[1]
6 loadings <- as.matrix(loadings) #just in case there is just one cluster
7 loadings <- unclass(loadings) #to get around the problem of a real loading matrix
8 …if(nclust > 1) {eigenvalue <- diag(t(ic.load$pattern)%*% loadings) #put the clusters into descend…
10 …if(clustsort) loadings <- loadings[,evorder] #added the clustsort option 2011.12.22 until now had…
13 var.labels <- rownames(loadings)} else {var.labels=labels}
17 loads <- data.frame(item=seq(1:nitems),content=var.labels,cluster=rep(0,nitems),loadings)
21 loads$cluster <- apply(abs(loadings),1,which.max)
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| H A D | omega.bifactor.R | 16 key <- sign(f$loadings[,1])
20 f$loadings <- diag(key) %*% f$loadings
25 rownames(f$loadings) <- r.names
32 gload <- f$loadings[,1]
36 uniq <- nvar - tr(f$loadings %*% t(f$loadings))
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| H A D | factor.congruence.R | 10 if (!is.matrix(xi)) {if(!is.null(xi$loadings)) {xi <- xi$loadings} else {xi <- as.matrix(xi)}}
21 if (!is.matrix(x)) {if(!is.null(x$loadings)) {x <- x$loadings} else {x <- as.matrix(x)} }
22 if (!is.matrix(y)) {if(!is.null(y$loadings)) { y <- y$loadings } else {y <- as.matrix(y)}}
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| H A D | eigen.loadings.R | 8 if(!is.null(x$loadings)) {
9 ans <- x$loadings %*% diag(x$sdev)
10 rownames(ans) <- rownames(x$loadings)
11 colnames(ans) <- colnames(x$loadings)
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| H A D | schmid.R | 28 orth.load <- loadings(fact)
39 obminfact <-list(loadings= orth.load) nameattr
51 loadings <- obminfact$loadings functionVar
60 if(nfactors > 1) rownames(obminfact$loadings) <- attr(model,"dimnames")[[1]]
64 fload <- obminfact$loadings
74 gload <- loadings(gfactor) } else {gload<- c(NA,NA) #consider the case of two factors
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| /dports/math/R/R-4.1.2/src/library/stats/man/ |
| H A D | loadings.Rd | 1 % File src/library/stats/man/loadings.Rd
6 \name{loadings}
7 \alias{loadings}
8 \alias{print.loadings}
12 Extract or print loadings in factor analysis (or principal
16 loadings(x, ...)
27 and loadings.}
35 ignored for \code{loadings}.}
45 draw the eye to the pattern of the larger loadings.
48 \code{"loadings"} method to print the loadings, and so passes down
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| H A D | summary.princomp.Rd | 11 \method{summary}{princomp}(object, loadings = FALSE, cutoff = 0.1, \dots)
13 \method{print}{summary.princomp}(x, digits = 3, loadings = x$print.loadings,
19 \item{loadings}{logical. Should loadings be included?}
24 loadings.}
32 \code{print.loadings}.
41 loadings = TRUE, cutoff = 0.2), digits = 2)
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| /dports/math/libRmath/R-4.1.1/src/library/stats/man/ |
| H A D | loadings.Rd | 1 % File src/library/stats/man/loadings.Rd
6 \name{loadings}
7 \alias{loadings}
8 \alias{print.loadings}
12 Extract or print loadings in factor analysis (or principal
16 loadings(x, ...)
27 and loadings.}
35 ignored for \code{loadings}.}
45 draw the eye to the pattern of the larger loadings.
48 \code{"loadings"} method to print the loadings, and so passes down
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| H A D | summary.princomp.Rd | 11 \method{summary}{princomp}(object, loadings = FALSE, cutoff = 0.1, \dots)
13 \method{print}{summary.princomp}(x, digits = 3, loadings = x$print.loadings,
19 \item{loadings}{logical. Should loadings be included?}
24 loadings.}
32 \code{print.loadings}.
41 loadings = TRUE, cutoff = 0.2), digits = 2)
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| /dports/math/R/R-4.1.2/src/library/stats/R/ |
| H A D | princomp-add.R | 24 p <- NCOL(object$loadings)
25 nm <- rownames(object$loadings)
35 scale(newdata, object$center, object$scale) %*% object$loadings
38 summary.princomp <- function(object, loadings = FALSE, cutoff = 0.1, ...) argument
41 object$print.loadings <- loadings
47 function(x, digits = 3L, loadings = x$print.loadings, cutoff = x$cutoff, argument
56 if(loadings) {
58 cx <- format(round(x$loadings, digits = digits))
59 cx[abs(x$loadings) < cutoff] <-
93 loadings <- function(x, ...) x$loadings function
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| /dports/math/libRmath/R-4.1.1/src/library/stats/R/ |
| H A D | princomp-add.R | 24 p <- NCOL(object$loadings)
25 nm <- rownames(object$loadings)
35 scale(newdata, object$center, object$scale) %*% object$loadings
38 summary.princomp <- function(object, loadings = FALSE, cutoff = 0.1, ...) argument
41 object$print.loadings <- loadings
47 function(x, digits = 3L, loadings = x$print.loadings, cutoff = x$cutoff, argument
56 if(loadings) {
58 cx <- format(round(x$loadings, digits = digits))
59 cx[abs(x$loadings) < cutoff] <-
93 loadings <- function(x, ...) x$loadings function
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| /dports/math/R-cran-pls/pls/man/ |
| H A D | scores.Rd | 5 \alias{loadings}
7 \alias{loadings.default}
13 loadings(object, ...)
15 \method{loadings}{default}(object, ...)
33 A matrix with scores or loadings.
43 The default \code{scores} and \code{loadings} methods also handle
44 \code{prcomp} objects (their scores and loadings components are called
50 There is a \code{loadings} function in package \pkg{stats}. It simply
51 returns any element named \code{"loadings"}. See
53 \code{stats::loadings(...)}.
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| /dports/devel/R-cran-broom/broom/R/ |
| H A D | stats-factanal-tidiers.R | 53 loadings <- stats::loadings(x) functionVar
54 class(loadings) <- "matrix"
57 variable = rownames(loadings),
59 data.frame(loadings)
159 loadings <- stats::loadings(x) functionVar
160 class(loadings) <- "matrix"
161 total.variance <- sum(apply(loadings, 2, function(i) sum(i^2) / length(i)))
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| /dports/math/R-cran-robustbase/robustbase/R/ |
| H A D | classPC.R | 15 .signflip <- function(loadings) { argument
16 apply(loadings, 2L,
39 loadings <- svd$v[,1:rank, drop=FALSE] functionVar
49 loadings <- crossprod(x, e$vectors[,ii]) * rep(1/sqrt(evs), each=p)
54 loadings <- .signflip(loadings)
56 list(rank=rank, eigenvalues=eigenvalues, loadings=loadings, nameattr
57 scores = if(scores) x %*% loadings,
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| /dports/math/R-cran-psych/psych/man/ |
| H A D | eigen.loadings.Rd | 1 \name{eigen.loadings}
2 \alias{eigen.loadings}
3 …e{Convert eigen vectors and eigen values to the more normal (for psychologists) component loadings}
4 …loadings from a factor analysis. eigen.loadings translates them into the more typical metric of e…
7 eigen.loadings(x)
15 …A matrix of Principal Component loadings more typical for what is expected in psychometrics. That…
26 y$loadings[1:8,1:4] #as they appear from princomp
27 eigen.loadings(x)[1:8,1:4] # rescaled by the eigen values
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| H A D | factor.congruence.Rd | 4 \description{Given two sets of factor loadings, report their degree of congruence (vector cosine).
11 \item{x}{ A matrix of factor loadings or a list of matrices of factor loadings}
12 \item{y}{ A second matrix of factor loadings (if x is a list, then y may be empty)}
16 \details{Find the coefficient of factor congruence between two sets of factor loadings.
18 …he cosines of pairs of vectors defined by the loadings matrix and based at the origin. Thus, for …
20 …loadings. Factor congruences are based upon the raw cross products, while correlations are based …
22 …actor analysis or principal components analyis output (which includes a loadings object), or a mix…
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| H A D | polar.Rd | 3 \title{Convert Cartesian factor loadings into polar coordinates }
4 …loadings). Tables of factor loadings are frequently sorted by the size of loadings. This style o…
11 \item{f}{A matrix of loadings or the output from a factor or cluster analysis program}
14 …s have high loadings on two factors. (These items are said to have complexity 2, see \code{\link{…
16 For each pair of factors, item loadings are converted to an angle with the first factor, and a vect…
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| H A D | fa.sort.Rd | 5 \title{Sort factor analysis or principal components analysis loadings}
7 …loadings, sometimes it is useful to do this outside of the print function. fa.sort takes the outpu…
18 The fa.results$loadings are replaced with sorted loadings.
20 \value{ A sorted factor analysis, principal components analysis, or omega loadings matrix.
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| /dports/math/R-cran-recipes/recipes/tests/testthat/ |
| H A D | test_ica.R | 97 loadings <- dimRed::getRotationMatrix(ica_extract_trained$steps[[1]]$res) globalVar
98 comps <- ncol(loadings)
99 loadings <- as.data.frame(loadings) globalVar
100 rownames(loadings) <- vars
101 colnames(loadings) <- paste0("IC", 1:comps)
102 loadings <- utils::stack(loadings) globalVar
106 component = as.character(loadings$ind),
107 value = loadings$values,
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| /dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/multivariate/ |
| H A D | factor.py | 129 self.loadings = None
277 self.loadings = A
431 self.loadings = load
561 self.loadings = factor.loadings
659 L = self.loadings
771 loadings = pd.DataFrame(
772 self.loadings,
778 summ.add_df(loadings)
841 self.loadings,
955 loadings = self.loadings_no_rot if plot_prerotated else self.loadings
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