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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_factor_analysis.wasp
Title produced by softwareFactor Analysis
Date of computationThu, 22 Feb 2018 13:40:58 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2018/Feb/22/t1519303321fam6h25i6bz2uha.htm/, Retrieved Sun, 05 May 2024 22:14:19 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sun, 05 May 2024 22:14:19 +0200
QR Codes:

Original text written by user:
IsPrivate?This computation is private
User-defined keywords
Estimated Impact0

Tree of Dependent Computations

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
'Economic well being'	0.0. 0.234543935587 0.2	0.819543935587	0.3	0.30102999566398	0.65321251377534	1.6232492903979	3	1	3
'Human capital'	3.40602894496362	3.66304097489397	2.1	0.25527250510331	1.83884909073726	2.79518458968242	3	5	4
'Income to needs ratio'	1.02325245963371	2.25406445291434	9.1	-0.15490195998574	1.43136376415899	2.25527250510331	4	4	4
'Community poverty'	-1.69897000433602	-0.52287874528034	15.8	0.5910646070265	1.27875360095283	1.54406804435028	1	1	1
'Age'	2.20411998265592	2.22788670461367	5.2	0	1.48287358360875	2.59328606702046	4	5	4
'Gender'	0.51851393987789	1.40823996531185	10.9	0.55630250076729	1.44715803134222	1.79934054945358	1	2	1
'Ethnicity'	1.71733758272386	2.64345267648619	8.3	0.14612803567824	1.69897000433602	2.36172783601759	1	1	1
'Single-parent status'	-0.36653154442041	0.80617997398389	11	0.17609125905568	0.84509804001426	2.04921802267018	5	4	4
'Confounding conditions'	2.66745295288995	2.62634036737504	3.2	-0.15490195998574	1.47712125471966	2.44870631990508	5	5	5
'General health status'	-1.09691001300806	0.079181246047625	6.3	0.32221929473392	0.54406804435028	1.6232492903979	1	1	1
'Specific health problems'	-0.10237290870956	0.54406804435028	6.6	0.61278385671974	0.77815125038364	1.6232492903979	2	2	2

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2 ;
Parameters (R input):
par1 = 2 ;
R code (references can be found in the software module):
par1 <- '2'
library(psych)
par1 <- as.numeric(par1)
x <- t(x)
nrows <- length(x[,1])
ncols <- length(x[1,])
y <- array(as.double(x[1:nrows,2:ncols]),dim=c(nrows,ncols-1))
colnames(y) <- colnames(x)[2:ncols]
rownames(y) <- x[,1]
print(y)
fit <- principal(y, nfactors=par1, rotate='varimax')
fit
fs <- factor.scores(y,fit)
fs
bitmap(file='test2.png')
plot(fs$scores,pch=20)
text(fs$scores,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')