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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 computationThu, 24 May 2012 17:46:13 -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/24/t1337895994q5l2njurgpr9iab.htm/, Retrieved Mon, 06 May 2024 00:41:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=167417, Retrieved Mon, 06 May 2024 00:41:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact94

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Factor Analysis] [female,bachelor] [2012-05-24 21:46:13] [5a1a483e8dfc82db7780f9c55bb04c44] [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=167417&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=167417&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=167417&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
C20.5990.255
C40.8310.065
C60.8510.114
C80.8070.067
C100.4550.49
C120.4430.459
C140.3330.496
C160.4370.453
C180.2030.453
C200.2620.464
C220.2770.561
C240.440.548
C260.5740.383
C280.5330.28
C300.5590.298
C320.540.237
C340.3790.571
C360.0560.727
C380.0150.755
C400.0320.539
C420.4130.536
C440.4230.507
C460.4880.444
C480.3620.395

\begin{tabular}{lllllllll}
\hline
Rotated Factor Loadings \tabularnewline
Variables & Factor1 & Factor2 \tabularnewline
C2 & 0.599 & 0.255 \tabularnewline
C4 & 0.831 & 0.065 \tabularnewline
C6 & 0.851 & 0.114 \tabularnewline
C8 & 0.807 & 0.067 \tabularnewline
C10 & 0.455 & 0.49 \tabularnewline
C12 & 0.443 & 0.459 \tabularnewline
C14 & 0.333 & 0.496 \tabularnewline
C16 & 0.437 & 0.453 \tabularnewline
C18 & 0.203 & 0.453 \tabularnewline
C20 & 0.262 & 0.464 \tabularnewline
C22 & 0.277 & 0.561 \tabularnewline
C24 & 0.44 & 0.548 \tabularnewline
C26 & 0.574 & 0.383 \tabularnewline
C28 & 0.533 & 0.28 \tabularnewline
C30 & 0.559 & 0.298 \tabularnewline
C32 & 0.54 & 0.237 \tabularnewline
C34 & 0.379 & 0.571 \tabularnewline
C36 & 0.056 & 0.727 \tabularnewline
C38 & 0.015 & 0.755 \tabularnewline
C40 & 0.032 & 0.539 \tabularnewline
C42 & 0.413 & 0.536 \tabularnewline
C44 & 0.423 & 0.507 \tabularnewline
C46 & 0.488 & 0.444 \tabularnewline
C48 & 0.362 & 0.395 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=167417&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]0.599[/C][C]0.255[/C][/ROW]
[ROW][C]C4[/C][C]0.831[/C][C]0.065[/C][/ROW]
[ROW][C]C6[/C][C]0.851[/C][C]0.114[/C][/ROW]
[ROW][C]C8[/C][C]0.807[/C][C]0.067[/C][/ROW]
[ROW][C]C10[/C][C]0.455[/C][C]0.49[/C][/ROW]
[ROW][C]C12[/C][C]0.443[/C][C]0.459[/C][/ROW]
[ROW][C]C14[/C][C]0.333[/C][C]0.496[/C][/ROW]
[ROW][C]C16[/C][C]0.437[/C][C]0.453[/C][/ROW]
[ROW][C]C18[/C][C]0.203[/C][C]0.453[/C][/ROW]
[ROW][C]C20[/C][C]0.262[/C][C]0.464[/C][/ROW]
[ROW][C]C22[/C][C]0.277[/C][C]0.561[/C][/ROW]
[ROW][C]C24[/C][C]0.44[/C][C]0.548[/C][/ROW]
[ROW][C]C26[/C][C]0.574[/C][C]0.383[/C][/ROW]
[ROW][C]C28[/C][C]0.533[/C][C]0.28[/C][/ROW]
[ROW][C]C30[/C][C]0.559[/C][C]0.298[/C][/ROW]
[ROW][C]C32[/C][C]0.54[/C][C]0.237[/C][/ROW]
[ROW][C]C34[/C][C]0.379[/C][C]0.571[/C][/ROW]
[ROW][C]C36[/C][C]0.056[/C][C]0.727[/C][/ROW]
[ROW][C]C38[/C][C]0.015[/C][C]0.755[/C][/ROW]
[ROW][C]C40[/C][C]0.032[/C][C]0.539[/C][/ROW]
[ROW][C]C42[/C][C]0.413[/C][C]0.536[/C][/ROW]
[ROW][C]C44[/C][C]0.423[/C][C]0.507[/C][/ROW]
[ROW][C]C46[/C][C]0.488[/C][C]0.444[/C][/ROW]
[ROW][C]C48[/C][C]0.362[/C][C]0.395[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=167417&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=167417&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
C20.5990.255
C40.8310.065
C60.8510.114
C80.8070.067
C100.4550.49
C120.4430.459
C140.3330.496
C160.4370.453
C180.2030.453
C200.2620.464
C220.2770.561
C240.440.548
C260.5740.383
C280.5330.28
C300.5590.298
C320.540.237
C340.3790.571
C360.0560.727
C380.0150.755
C400.0320.539
C420.4130.536
C440.4230.507
C460.4880.444
C480.3620.395


Figures (Output of Computation)

Input Parameters & R Code

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