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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_factor_analysisdm.wasp
Title produced by softwareFactor Analysis
Date of computationTue, 01 May 2012 07:23:35 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/May/01/t1335871494kgphozkkbcbnl14.htm/, Retrieved Sat, 04 May 2024 18:56:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=165445, Retrieved Sat, 04 May 2024 18:56:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact91

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Factor Analysis] [] [2012-05-01 11:23:35] [b3fea4edbb2b32c665e3884fb2b4b154] [Current]
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Dataset

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=165445&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=165445&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=165445&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Rotated Factor Loadings
VariablesFactor1Factor2
C20.5640.157
C40.7390.113
C60.7190.067
C80.7180.081
C100.5960.217
C120.6680.149
C140.5150.188
C160.4660.168
C180.3560.517
C200.2240.545
C220.1690.685
C240.2490.624
C260.580.368
C280.4520.351
C300.6090.267
C320.5380.36
C340.1830.711
C360.1490.746
C380.2090.703
C400.0810.629
C420.5120.4
C440.4360.386
C460.6080.205
C480.5070.262

\begin{tabular}{lllllllll}
\hline
Rotated Factor Loadings \tabularnewline
Variables & Factor1 & Factor2 \tabularnewline
C2 & 0.564 & 0.157 \tabularnewline
C4 & 0.739 & 0.113 \tabularnewline
C6 & 0.719 & 0.067 \tabularnewline
C8 & 0.718 & 0.081 \tabularnewline
C10 & 0.596 & 0.217 \tabularnewline
C12 & 0.668 & 0.149 \tabularnewline
C14 & 0.515 & 0.188 \tabularnewline
C16 & 0.466 & 0.168 \tabularnewline
C18 & 0.356 & 0.517 \tabularnewline
C20 & 0.224 & 0.545 \tabularnewline
C22 & 0.169 & 0.685 \tabularnewline
C24 & 0.249 & 0.624 \tabularnewline
C26 & 0.58 & 0.368 \tabularnewline
C28 & 0.452 & 0.351 \tabularnewline
C30 & 0.609 & 0.267 \tabularnewline
C32 & 0.538 & 0.36 \tabularnewline
C34 & 0.183 & 0.711 \tabularnewline
C36 & 0.149 & 0.746 \tabularnewline
C38 & 0.209 & 0.703 \tabularnewline
C40 & 0.081 & 0.629 \tabularnewline
C42 & 0.512 & 0.4 \tabularnewline
C44 & 0.436 & 0.386 \tabularnewline
C46 & 0.608 & 0.205 \tabularnewline
C48 & 0.507 & 0.262 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=165445&T=1

[TABLE]
[ROW][C]Rotated Factor Loadings[/C][/ROW]
[ROW][C]Variables[/C][C]Factor1[/C][C]Factor2[/C][/ROW]
[ROW][C]C2[/C][C]0.564[/C][C]0.157[/C][/ROW]
[ROW][C]C4[/C][C]0.739[/C][C]0.113[/C][/ROW]
[ROW][C]C6[/C][C]0.719[/C][C]0.067[/C][/ROW]
[ROW][C]C8[/C][C]0.718[/C][C]0.081[/C][/ROW]
[ROW][C]C10[/C][C]0.596[/C][C]0.217[/C][/ROW]
[ROW][C]C12[/C][C]0.668[/C][C]0.149[/C][/ROW]
[ROW][C]C14[/C][C]0.515[/C][C]0.188[/C][/ROW]
[ROW][C]C16[/C][C]0.466[/C][C]0.168[/C][/ROW]
[ROW][C]C18[/C][C]0.356[/C][C]0.517[/C][/ROW]
[ROW][C]C20[/C][C]0.224[/C][C]0.545[/C][/ROW]
[ROW][C]C22[/C][C]0.169[/C][C]0.685[/C][/ROW]
[ROW][C]C24[/C][C]0.249[/C][C]0.624[/C][/ROW]
[ROW][C]C26[/C][C]0.58[/C][C]0.368[/C][/ROW]
[ROW][C]C28[/C][C]0.452[/C][C]0.351[/C][/ROW]
[ROW][C]C30[/C][C]0.609[/C][C]0.267[/C][/ROW]
[ROW][C]C32[/C][C]0.538[/C][C]0.36[/C][/ROW]
[ROW][C]C34[/C][C]0.183[/C][C]0.711[/C][/ROW]
[ROW][C]C36[/C][C]0.149[/C][C]0.746[/C][/ROW]
[ROW][C]C38[/C][C]0.209[/C][C]0.703[/C][/ROW]
[ROW][C]C40[/C][C]0.081[/C][C]0.629[/C][/ROW]
[ROW][C]C42[/C][C]0.512[/C][C]0.4[/C][/ROW]
[ROW][C]C44[/C][C]0.436[/C][C]0.386[/C][/ROW]
[ROW][C]C46[/C][C]0.608[/C][C]0.205[/C][/ROW]
[ROW][C]C48[/C][C]0.507[/C][C]0.262[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=165445&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=165445&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Rotated Factor Loadings
VariablesFactor1Factor2
C20.5640.157
C40.7390.113
C60.7190.067
C80.7180.081
C100.5960.217
C120.6680.149
C140.5150.188
C160.4660.168
C180.3560.517
C200.2240.545
C220.1690.685
C240.2490.624
C260.580.368
C280.4520.351
C300.6090.267
C320.5380.36
C340.1830.711
C360.1490.746
C380.2090.703
C400.0810.629
C420.5120.4
C440.4360.386
C460.6080.205
C480.5070.262


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = all ; par3 = prep ; par4 = all ; par5 = COLLES preferred ;
Parameters (R input):
par1 = 2 ; par2 = all ; par3 = prep ; par4 = all ; par5 = COLLES preferred ;
R code (references can be found in the software module):
library(psych)
x <- as.data.frame(read.table(file='https://automated.biganalytics.eu/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$Year==as.numeric(par4),]
}
cAc <- with(x,cbind( A1, A2, A3, A4, A5, A6, A7, A8, A9,A10))
cAs <- with(x,cbind(A11,A12,A13,A14,A15,A16,A17,A18,A19,A20))
cA <- cbind(cAc,cAs)
cCa <- with(x,cbind(C1,C3,C5,C7, C9,C11,C13,C15,C17,C19,C21,C23,C25,C27,C29,C31,C33,C35,C37,C39,C41,C43,C45,C47))
cCp <- with(x,cbind(C2,C4,C6,C8,C10,C12,C14,C16,C18,C20,C22,C24,C26,C28,C30,C32,C34,C36,C38,C40,C42,C44,C46,C48))
cC <- cbind(cCa,cCp)
cU <- with(x,cbind(U1,U2,U3,U4,U5,U6,U7,U8,U9,U10,U11,U12,U13,U14,U15,U16,U17,U18,U19,U20,U21,U22,U23,U24,U25,U26,U27,U28,U29,U30,U31,U32,U33))
cE <- with(x,cbind(BC,NNZFG,MRT,AFL,LPM,LPC,W,WPA))
cX <- with(x,cbind(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14,X15,X16,X17,X18))
if (par5=='ATTLES connected') x <- cAc
if (par5=='ATTLES separate') x <- cAs
if (par5=='ATTLES all') x <- cA
if (par5=='COLLES actuals') x <- cCa
if (par5=='COLLES preferred') x <- cCp
if (par5=='COLLES all') x <- cC
if (par5=='CSUQ') x <- cU
if (par5=='Learning Activities') x <- cE
if (par5=='Exam Items') x <- cX
ncol <- length(x[1,])
for (jjj in 1:ncol) {
x <- x[!is.na(x[,jjj]),]
}
par1 <- as.numeric(par1)
nrows <- length(x[,1])
rownames(x) <- 1:nrows
y <- x
fit <- principal(y, nfactors=par1, rotate='varimax')
fit
fs <- factor.scores(y,fit)
fs
bitmap(file='test1.png')
fa.diagram(fit)
dev.off()
bitmap(file='test2.png')
plot(fs,pch=20)
text(fs,labels=rownames(y),pos=3)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Rotated Factor Loadings',par1+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variables',1,TRUE)
for (i in 1:par1) {
a<-table.element(a,paste('Factor',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (j in 1:length(fit$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='mytable.tab')