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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 computationFri, 18 May 2012 13:22:48 -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/18/t1337361788oamybhfq1kd41u0.htm/, Retrieved Fri, 03 May 2024 20:05:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=166691, Retrieved Fri, 03 May 2024 20:05:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Factor Analysis] [Factor Analysis] [2012-05-18 17:22:48] [63b8a9573feb7e5c5af439dd6e45c15a] [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'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166691&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166691&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166691&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'Gwilym Jenkins' @ jenkins.wessa.net






Rotated Factor Loadings
VariablesFactor1Factor2
C10.5340.121
C30.6920.039
C50.6930.109
C70.7110.043
C90.5190.281
C110.4990.276
C130.4580.258
C150.4580.3
C170.2380.53
C190.0890.595
C210.1190.677
C230.280.558
C250.5920.308
C270.4290.304
C290.6260.096
C310.4660.264
C330.2770.683
C350.2450.705
C370.2540.656
C390.020.61
C410.4660.308
C430.4160.434
C450.5640.171
C470.5370.216

\begin{tabular}{lllllllll}
\hline
Rotated Factor Loadings \tabularnewline
Variables & Factor1 & Factor2 \tabularnewline
C1 & 0.534 & 0.121 \tabularnewline
C3 & 0.692 & 0.039 \tabularnewline
C5 & 0.693 & 0.109 \tabularnewline
C7 & 0.711 & 0.043 \tabularnewline
C9 & 0.519 & 0.281 \tabularnewline
C11 & 0.499 & 0.276 \tabularnewline
C13 & 0.458 & 0.258 \tabularnewline
C15 & 0.458 & 0.3 \tabularnewline
C17 & 0.238 & 0.53 \tabularnewline
C19 & 0.089 & 0.595 \tabularnewline
C21 & 0.119 & 0.677 \tabularnewline
C23 & 0.28 & 0.558 \tabularnewline
C25 & 0.592 & 0.308 \tabularnewline
C27 & 0.429 & 0.304 \tabularnewline
C29 & 0.626 & 0.096 \tabularnewline
C31 & 0.466 & 0.264 \tabularnewline
C33 & 0.277 & 0.683 \tabularnewline
C35 & 0.245 & 0.705 \tabularnewline
C37 & 0.254 & 0.656 \tabularnewline
C39 & 0.02 & 0.61 \tabularnewline
C41 & 0.466 & 0.308 \tabularnewline
C43 & 0.416 & 0.434 \tabularnewline
C45 & 0.564 & 0.171 \tabularnewline
C47 & 0.537 & 0.216 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=166691&T=1

[TABLE]
[ROW][C]Rotated Factor Loadings[/C][/ROW]
[ROW][C]Variables[/C][C]Factor1[/C][C]Factor2[/C][/ROW]
[ROW][C]C1[/C][C]0.534[/C][C]0.121[/C][/ROW]
[ROW][C]C3[/C][C]0.692[/C][C]0.039[/C][/ROW]
[ROW][C]C5[/C][C]0.693[/C][C]0.109[/C][/ROW]
[ROW][C]C7[/C][C]0.711[/C][C]0.043[/C][/ROW]
[ROW][C]C9[/C][C]0.519[/C][C]0.281[/C][/ROW]
[ROW][C]C11[/C][C]0.499[/C][C]0.276[/C][/ROW]
[ROW][C]C13[/C][C]0.458[/C][C]0.258[/C][/ROW]
[ROW][C]C15[/C][C]0.458[/C][C]0.3[/C][/ROW]
[ROW][C]C17[/C][C]0.238[/C][C]0.53[/C][/ROW]
[ROW][C]C19[/C][C]0.089[/C][C]0.595[/C][/ROW]
[ROW][C]C21[/C][C]0.119[/C][C]0.677[/C][/ROW]
[ROW][C]C23[/C][C]0.28[/C][C]0.558[/C][/ROW]
[ROW][C]C25[/C][C]0.592[/C][C]0.308[/C][/ROW]
[ROW][C]C27[/C][C]0.429[/C][C]0.304[/C][/ROW]
[ROW][C]C29[/C][C]0.626[/C][C]0.096[/C][/ROW]
[ROW][C]C31[/C][C]0.466[/C][C]0.264[/C][/ROW]
[ROW][C]C33[/C][C]0.277[/C][C]0.683[/C][/ROW]
[ROW][C]C35[/C][C]0.245[/C][C]0.705[/C][/ROW]
[ROW][C]C37[/C][C]0.254[/C][C]0.656[/C][/ROW]
[ROW][C]C39[/C][C]0.02[/C][C]0.61[/C][/ROW]
[ROW][C]C41[/C][C]0.466[/C][C]0.308[/C][/ROW]
[ROW][C]C43[/C][C]0.416[/C][C]0.434[/C][/ROW]
[ROW][C]C45[/C][C]0.564[/C][C]0.171[/C][/ROW]
[ROW][C]C47[/C][C]0.537[/C][C]0.216[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=166691&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=166691&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
C10.5340.121
C30.6920.039
C50.6930.109
C70.7110.043
C90.5190.281
C110.4990.276
C130.4580.258
C150.4580.3
C170.2380.53
C190.0890.595
C210.1190.677
C230.280.558
C250.5920.308
C270.4290.304
C290.6260.096
C310.4660.264
C330.2770.683
C350.2450.705
C370.2540.656
C390.020.61
C410.4660.308
C430.4160.434
C450.5640.171
C470.5370.216


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = all ; par3 = bachelor ; par4 = all ; par5 = COLLES actuals ;
Parameters (R input):
par1 = 2 ; par2 = all ; par3 = bachelor ; par4 = all ; par5 = COLLES actuals ;
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')