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Type 'q()' to quit R. > par5 = 'ATTLES connected' > par4 = 'all' > par3 = 'all' > par2 = 'female' > par1 = '2' > par5 <- 'ATTLES connected' > par4 <- 'all' > par3 <- 'all' > par2 <- 'female' > 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 -0.03 0.31 0.69 A2 -0.11 0.69 0.49 0.51 A3 0.64 -0.03 0.41 0.59 A4 0.25 0.48 0.29 0.71 A5 0.54 0.08 0.30 0.70 A6 0.64 -0.04 0.41 0.59 A7 0.21 0.53 0.33 0.67 A8 0.61 0.36 0.50 0.50 A9 -0.14 0.73 0.55 0.45 A10 0.45 0.29 0.29 0.71 RC1 RC2 SS loadings 2.14 1.74 Proportion Var 0.21 0.17 Cumulative Var 0.21 0.39 Test of the hypothesis that 2 factors are sufficient. The degrees of freedom for the null model are 45 and the objective function was 1.12 The degrees of freedom for the model are 26 and the objective function was 0.53 The number of observations was 455 with Chi Square = 235.98 with prob < 9.7e-36 Fit based upon off diagonal values = 0.6> fs <- factor.scores(y,fit) > fs RC1 RC2 1 0.1738985166 -0.5983699286 2 1.6549387567 -0.9506001925 3 -0.9831257357 -1.0008810834 4 -1.5827149861 -1.2589885769 5 -0.8959573919 -0.4175203623 6 -0.3048934948 -0.3248947922 7 -2.6156425387 0.0619315955 8 1.4897463421 -0.0135186937 9 0.2464569620 -0.9177968524 10 0.7278595160 0.0351796292 11 -0.0622474007 -1.1908380487 12 0.8352168253 -0.9267026904 13 0.1149016891 -1.8544695283 14 -1.3951493119 0.3568160989 15 0.5175017979 0.1571372417 16 -0.4165984170 -1.0149317143 17 0.3540535053 -1.5844437121 18 -0.0008771382 -0.0538692831 19 0.1900913887 -1.7100056611 20 -0.2206355649 0.5981413295 21 0.7154769949 -0.0493516136 22 0.7216668326 -0.0337589823 23 -1.4340088668 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-0.1205871971 1.2605754409 445 0.4348439103 1.3880227680 446 -0.8367877433 0.4695216147 447 -0.8719068041 1.0476000197 448 -0.3233094178 1.2080704215 449 -0.0469728133 0.8187106484 450 -1.2041007928 0.5100085065 451 0.5223551953 1.4190905778 452 -1.9027950150 -0.2799761380 453 -4.4324311115 -0.4321017847 454 -0.5954969180 0.1159809724 455 -0.5247018012 0.2661236058 > postscript(file="/var/wessaorg/rcomp/tmp/1frqg1335799351.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/2vub61335799351.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/3dfzy1335799351.tab") > > try(system("convert tmp/1frqg1335799351.ps tmp/1frqg1335799351.png",intern=TRUE)) character(0) > try(system("convert tmp/2vub61335799351.ps tmp/2vub61335799351.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 0.930 0.183 1.108