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Type 'q()' to quit R. > par5 = 'ATTLES connected' > par4 = 'all' > par3 = 'prep' > par2 = 'all' > par1 = '2' > par5 <- 'ATTLES connected' > par4 <- 'all' > par3 <- 'prep' > par2 <- 'all' > par1 <- '2' > #'GNU S' R Code compiled by R2WASP v. 1.2.291 () > #Author: root > #To cite this work: Wessa P., 2012, Factor Analysis for Information Management 2 (v1.0.7) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_factor_analysis.wasp/ > #Source of accompanying publication: > # > library(psych) > x <- as.data.frame(read.table(file='http://www.wessa.net/download/utaut.csv',sep=',',header=T)) > x$U25 <- 6-x$U25 > if(par2 == 'female') x <- x[x$Gender==0,] > if(par2 == 'male') x <- x[x$Gender==1,] > if(par3 == 'prep') x <- x[x$Pop==1,] > if(par3 == 'bachelor') x <- x[x$Pop==0,] > if(par4 != 'all') { + x <- x[x$Year==as.numeric(par4),] + } > cAc <- with(x,cbind( A1, A2, A3, A4, A5, A6, A7, A8, A9,A10)) > cAs <- with(x,cbind(A11,A12,A13,A14,A15,A16,A17,A18,A19,A20)) > cA <- cbind(cAc,cAs) > cCa <- with(x,cbind(C1,C3,C5,C7, C9,C11,C13,C15,C17,C19,C21,C23,C25,C27,C29,C31,C33,C35,C37,C39,C41,C43,C45,C47)) > cCp <- with(x,cbind(C2,C4,C6,C8,C10,C12,C14,C16,C18,C20,C22,C24,C26,C28,C30,C32,C34,C36,C38,C40,C42,C44,C46,C48)) > cC <- cbind(cCa,cCp) > cU <- with(x,cbind(U1,U2,U3,U4,U5,U6,U7,U8,U9,U10,U11,U12,U13,U14,U15,U16,U17,U18,U19,U20,U21,U22,U23,U24,U25,U26,U27,U28,U29,U30,U31,U32,U33)) > cE <- with(x,cbind(BC,NNZFG,MRT,AFL,LPM,LPC,W,WPA)) > cX <- with(x,cbind(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14,X15,X16,X17,X18)) > if (par5=='ATTLES connected') x <- cAc > if (par5=='ATTLES separate') x <- cAs > if (par5=='ATTLES all') x <- cA > if (par5=='COLLES actuals') x <- cCa > if (par5=='COLLES preferred') x <- cCp > if (par5=='COLLES all') x <- cC > if (par5=='CSUQ') x <- cU > if (par5=='Learning Activities') x <- cE > if (par5=='Exam Items') x <- cX > ncol <- length(x[1,]) > for (jjj in 1:ncol) { + x <- x[!is.na(x[,jjj]),] + } > par1 <- as.numeric(par1) > nrows <- length(x[,1]) > rownames(x) <- 1:nrows > y <- x > fit <- principal(y, nfactors=par1, rotate='varimax') > fit Principal Components Analysis Call: principal(r = y, nfactors = par1, rotate = "varimax") Standardized loadings based upon correlation matrix RC1 RC2 h2 u2 A1 0.56 -0.10 0.33 0.67 A2 -0.20 0.70 0.52 0.48 A3 0.63 0.07 0.40 0.60 A4 0.12 0.54 0.31 0.69 A5 0.39 0.24 0.21 0.79 A6 0.64 0.04 0.41 0.59 A7 0.25 0.50 0.32 0.68 A8 0.53 0.38 0.42 0.58 A9 0.09 0.68 0.47 0.53 A10 0.56 0.11 0.32 0.68 RC1 RC2 SS loadings 2.0 1.72 Proportion Var 0.2 0.17 Cumulative Var 0.2 0.37 Test of the hypothesis that 2 factors are sufficient. The degrees of freedom for the null model are 45 and the objective function was 0.98 The degrees of freedom for the model are 26 and the objective function was 0.5 The number of observations was 553 with Chi Square = 274.84 with prob < 2.2e-43 Fit based upon off diagonal values = 0.55> fs <- factor.scores(y,fit) > fs RC1 RC2 1 0.3697331298 -0.8806398618 2 1.9561907521 -1.3351029657 3 -0.2059482651 1.0042168444 4 -0.8571157638 -1.0188958978 5 -1.4440500722 -1.3231089343 6 -0.2603351778 0.4721170598 7 -0.8299444838 -0.4988380024 8 -0.1274417831 -0.6275433083 9 1.0720391880 -0.5176207596 10 -0.6954503525 -0.1049450567 11 -2.6642431431 0.1703668248 12 1.0659474163 -0.3777080957 13 -0.2636348042 0.0738583616 14 0.1168255767 -0.0316949112 15 1.1013435672 0.2622028606 16 0.2311721582 -0.9647566954 17 0.5396936295 0.0641446415 18 0.1980292962 -0.1296221666 19 -0.4816697831 -0.8502895950 20 0.9152250800 -1.0839787658 21 -0.3463139518 0.6170141489 22 0.0559694945 -1.8571136272 23 0.2160655257 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0.5209038444 -0.4906555299 509 0.2135454277 -0.0375598977 510 -2.1271813323 0.4558176727 511 1.8485597609 1.8263888455 512 0.0246206559 0.8802100478 513 -2.9695293298 -0.8312182109 514 0.4881525136 1.3043084419 515 -0.0565903052 0.2673142688 516 -0.1653629702 0.9318519354 517 0.9357831522 1.2743601474 518 -0.6498620252 -0.0459688878 519 -0.3251197586 0.5113134751 520 0.5917919158 1.1601722244 521 0.6904214180 2.5531210777 522 0.2420888419 0.3212957942 523 0.1956954503 -0.2047461407 524 -1.7682478792 -0.1656723946 525 -0.9208449004 -0.6180205688 526 0.6426967726 1.5426972513 527 -0.1767038487 0.9694893115 528 1.8652806276 1.5072032334 529 -0.6113501656 -0.1196685435 530 -0.6716640087 0.7515282650 531 -0.1863721002 -0.1023686071 532 -0.4938897842 0.8755550813 533 -0.3849414092 0.4259120153 534 -0.8217082622 1.0875707250 535 -0.3073471022 0.8980468422 536 0.2654574919 0.8100409315 537 -0.2487255777 0.4731178344 538 -0.5241852269 -0.1857418594 539 -0.6502234123 0.4047048807 540 -0.5106106509 1.1947406934 541 0.4422202229 -0.8373546423 542 0.7763886410 0.4671986178 543 0.3893968902 1.3188347477 544 0.5591251025 0.8551060031 545 0.2975568341 0.8852119169 546 0.7108170923 -0.1680474342 547 -1.2927370634 0.8688573239 548 -0.5657661014 -1.2232272663 549 -0.2427716002 -0.2058512606 550 -0.5134518098 0.2563603230 551 0.8088764437 2.0028382155 552 -0.5354221121 0.6853275679 553 1.3443880977 2.4428099732 > postscript(file="/var/wessaorg/rcomp/tmp/1cxxu1337202122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > fa.diagram(fit) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/28ltk1337202122.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(fs,pch=20) > text(fs,labels=rownames(y),pos=3) > dev.off() null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Rotated Factor Loadings',par1+1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Variables',1,TRUE) > for (i in 1:par1) { + a<-table.element(a,paste('Factor',i,sep=''),1,TRUE) + } > a<-table.row.end(a) > for (j in 1:length(fit$loadings[,1])) { + a<-table.row.start(a) + a<-table.element(a,rownames(fit$loadings)[j],header=TRUE) + for (i in 1:par1) { + a<-table.element(a,round(fit$loadings[j,i],3)) + } + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/3ffo21337202122.tab") > > try(system("convert tmp/1cxxu1337202122.ps tmp/1cxxu1337202122.png",intern=TRUE)) character(0) > try(system("convert tmp/28ltk1337202122.ps tmp/28ltk1337202122.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 1.361 0.270 1.623