search for: rotmat

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...if(ncol(x) < 2) return(x) dn <- dimnames(x) xx <- varimax(x) x <- xx$loadings Q <- x * abs(x)^(m-1) U <- lm.fit(x, Q)$coefficients d <- diag(solve(t(U) %*% U)) U <- U %*% diag(sqrt(d)) dimnames(U) <- NULL z <- x %*% U U <- xx$rotmat %*% U dimnames(z) <- dn class(z) <- "loadings" list(loadings = z, rotmat = U, crap = x, coeff = Q) } And the line I'm having trouble with is: U <- lm.fit(x, Q)$coefficients I'm trying to rewrite this routine in Java and got stuck on the lm.fit part. Thanks...

...ese starting value(s)") load <- fit$loadings if(rotation != "none") { rot <- do.call(rotation, c(list(load), cn$rotate)) # the following lines modified by J. Fox, 26 June 2005 if (is.list(rot)){ load <- rot$loadings fit$rotmat <- rot$rotmat } else load <- rot # end modifications J. Fox, 26 June 2005 } fit$loadings <- sortLoadings(load) class(fit$loadings) <- "loadings" fit$na.action <- na.act # not used currently if(have.x && scores != "non...

...0.35573, 0.85995, 0.07887, 0.79376, 0.22499, 0.26307, 0.85087, 0.29449, 0.72966, 0.06064, 0.68135), c(2,6))) Clearly, the varimax function of R confirms this because varimax(x)$rotmat [,1] [,2] [1,] 1.000000e+00 2.021128e-07 [2,] -2.021128e-07 1.000000e+00 yields the identity matrix. SAS will return the following matrix after a promax rotation (for which m=3 by default): 0.75760 0.18383 0.91956 -0.14061 0.80720 0.03711 0.05546...

...t(load), cn$rotate)) # the following lines modified by J. Fox, 26 June 2005 if (is.list(rot)){ load <- rot$loadings #the following lines changed by Ulrich Keller, 9 Sept 2008 fit$rotmat <- if(inherits(rot, "GPArotation")) { t(solve(rot$Th)) } else { rot$rotmat } #end changes Ulrich Keller, 9 Sept 2008 } else load <- rot...

...s # the prerotation: res <- factanal(X, factors=2, rotation="none") vm <- varimax(loadings(res)) pm <- promax(loadings(vm), m=3) # m=3 is default in SAS L <- loadings(pm) # Calculate the "normalized oblique transformation matrix" (label from # SAS output) A <- vm$rotmat %*% pm$rotmat # Calculate the "inter-factor correlation matrix" (label from SAS output Phi <- solve(t(A) %*% A) # Calculating regression factor scores according to the second formula # as t(Phi %*% t(L) %*% solve(R) %*% t(X)) # yields scores that are pretty close, although not identi...

...DS140_07 0.273 DS140_08 0.392 DS140_09 0.340 DS140_10 0.413 DS140_11 0.347 DS140_12 0.388 DS140_13 0.389 DS140_14 0.355 Comp.1 Comp.2 SS loadings 1.000 1.000 Proportion Var 0.071 0.071 Cumulative Var 0.071 0.143 $rotmat [,1] [,2] [1,] 0.7585207 0.6516489 [2,] -0.6516489 0.7585207 This is the output generated by SPSS Call: FACTOR /VARIABLES ds140_01 ds140_02 ds140_03 ds140_04 ds140_05 ds140_06 ds140_07 ds140_08 ds140_09 ds140_10 ds140_11 ds140_12 ds140_13 ds140_14 /MISSING LISTWISE /ANAL...

...s: Comp.1 Comp.2 Comp.3 Murder Assault -0.998 UrbanPop -0.997 Rape 0.995 Comp.1 Comp.2 Comp.3 SS loadings 1.00 1.00 1.00 Proportion Var 0.25 0.25 0.25 Cumulative Var 0.25 0.50 0.75 $rotmat [,1] [,2] [,3] [1,] 0.99211386 0.03604908 -0.1200439 [2,] -0.05442524 0.98664663 -0.1535132 [3,] 0.11290692 0.15883603 0.9808278 > R.Version() $platform [1] "i386-pc-linux-gnu" $arch [1] "i386" $os [1] "linux-gnu" $system [1] "i38...

R-1.7.0 built on NetBSD 1.6, but the validation test suite failed: Machinetype: Intel Pentium III (600 MHz); NetBSD 1.6 (GENERIC) Remote gcc version: gcc (GCC) 3.2.2 Remote g++ version: g++ (GCC) 3.2.2 Configure environment: CC=gcc CXX=g++ LDFLAGS=-Wl,-rpath,/usr/local/lib make[5]: Entering directory `/local/build/R-1.7.0/src/library' >>> Building/Updating