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Author

*Unverified author*

R Software Module

rwasp_factor_analysis.wasp

Title produced by software

Factor Analysis

Date of computation

Wed, 02 Apr 2014 12:26:01 -0400

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Apr/02/t1396456279uaakgec1m4os9qx.htm/, Retrieved Fri, 17 May 2024 04:59:06 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=234380, Retrieved Fri, 17 May 2024 04:59:06 +0000

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Original text written by user:

3 factores 74% Variancia total explicada

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

172

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Factor Analysis] [Analisis Factoria...] [2014-04-02 16:26:01] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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Boxplots

'GTM'	110.39	6.80	52.90	3.48	21.38	67.64	26.64	32.86	27.47	0.70	12.70	1.12	2.11	4.08	49.87	7.88	2.84	0.38	5.01	1.66	67.64	1.31	0.99	0.57	77.34
'PRO'	23.49	6.35	10.16	1.90	15.24	58.42	2.54	2.54	6.98	1.27	3.17	1.90	0.63	0.63	38.10	3.17	5.08	1.90	8.89	1.27	58.42	0.63	0.63	0.63	44.45
'SAC'	66.63	7.58	39.79	2.21	15.79	21.16	13.89	18.63	22.74	0.00	9.16	0.63	0.95	0.63	10.10	1.58	1.26	0.00	6.32	1.89	21.16	4.11	0.32	0.63	36.95
'CHM'	34.49	3.47	25.74	2.64	7.43	14.36	5.78	4.79	4.29	0.33	5.78	0.00	0.33	0.17	9.41	0.83	1.16	0.33	2.15	0.50	14.36	0.83	0.50	0.17	35.81
'ESC'	74.73	5.41	200.57	7.40	26.33	98.22	18.08	20.36	2.14	0.14	4.13	0.71	1.42	1.57	70.75	8.11	5.98	0.28	11.25	1.85	98.22	3.27	1.00	1.57	73.88
'SRO'	33.63	3.48	24.93	2.32	20.29	82.34	11.31	12.18	4.35	0.87	5.22	0.87	1.16	0.00	56.83	4.64	6.67	1.74	8.99	3.48	82.34	0.58	0.87	0.00	67.84
'SOL'	2.55	0.70	1.16	0.23	0.93	8.59	3.25	3.25	1.86	0.70	2.55	0.70	0.23	0.00	3.72	0.00	0.70	0.23	3.48	0.46	8.59	1.39	0.23	0.00	18.35
'TOT'	14.60	1.06	1.27	0.00	0.63	7.83	2.75	2.96	0.21	0.00	2.54	0.42	0.00	0.00	0.85	0.42	0.63	0.21	4.23	1.48	7.83	0.85	0.63	0.42	15.87
'QUT'	42.78	1.89	30.92	3.03	9.47	26.88	18.43	21.33	3.66	1.64	4.04	1.64	0.63	1.14	17.54	1.77	1.77	0.38	4.29	1.14	26.88	0.25	0.76	0.63	34.83
'SUC'	32.74	1.45	43.51	1.86	9.95	30.87	18.23	20.93	4.35	1.24	5.39	1.04	1.04	0.41	17.20	2.28	2.69	0.21	7.25	1.24	30.87	0.41	0.21	0.41	36.68
'RET'	15.84	1.32	52.14	1.98	5.94	35.97	12.87	16.50	1.32	1.65	2.64	1.98	1.32	0.66	23.10	1.32	1.65	0.33	8.25	1.32	35.97	1.32	0.66	0.66	37.62
'SMA'	8.02	0.78	7.14	0.78	4.70	15.56	4.21	4.11	1.66	0.49	1.66	0.10	0.10	0.00	10.67	0.29	1.37	0.10	2.74	0.39	15.56	1.27	0.10	0.10	12.72
'HUE'	8.69	0.70	31.64	1.30	4.35	6.43	4.78	4.87	1.65	0.52	1.13	0.09	0.17	0.00	3.82	0.26	0.70	0.09	1.48	0.09	6.43	1.83	0.26	0.70	13.12
'QUI'	4.50	0.84	12.66	0.94	2.09	6.38	2.93	3.14	0.73	0.10	2.41	0.42	0.21	0.00	2.30	0.31	0.73	0.21	2.41	0.42	6.38	0.52	0.42	0.21	14.44
'BVP'	6.33	1.86	13.40	2.23	6.33	18.62	12.29	14.89	2.61	0.37	4.84	2.61	0.00	0.00	7.45	1.49	0.74	0.37	7.82	0.74	18.62	0.37	1.12	0.74	23.09
'AVP'	8.48	0.27	14.56	0.63	6.97	13.04	8.13	9.73	1.43	0.63	1.07	0.27	0.18	0.00	7.41	0.45	1.61	0.18	2.86	0.54	13.04	0.71	0.18	0.27	14.47
'PET'	3.82	1.11	38.03	1.75	11.30	59.36	5.89	3.66	1.27	0.48	1.91	0.00	0.48	0.00	41.54	3.66	7.48	0.80	4.93	0.95	59.36	2.23	0.64	0.16	25.30
'IZA'	12.66	1.95	80.82	3.65	20.69	79.85	5.60	5.84	2.43	0.73	2.19	0.49	0.00	0.00	59.16	6.82	5.60	1.46	5.84	0.97	79.85	0.49	0.73	0.73	49.18
'ZAC'	17.17	0.90	29.82	2.26	42.46	92.61	6.32	4.97	6.32	0.00	4.52	0.00	0.00	0.00	66.41	7.68	7.23	0.45	8.13	2.71	92.61	1.81	0.00	1.81	67.76
'CHQ'	20.38	2.45	40.76	0.82	29.89	96.20	8.15	7.61	3.26	0.27	2.45	0.27	0.00	0.00	58.70	6.25	14.95	1.90	11.68	2.72	96.20	2.45	0.54	0.82	58.15
'JAL'	1.27	1.59	2.87	1.27	12.42	54.44	4.78	6.05	1.59	0.32	1.59	1.91	0.32	0.00	33.11	4.78	7.96	1.91	5.09	1.59	54.44	1.27	0.00	0.96	35.02
'JUT'	12.90	1.15	6.22	1.15	6.22	61.26	7.83	8.52	3.45	0.23	4.15	0.69	0.23	0.00	42.83	7.37	4.38	1.38	4.84	0.46	61.26	4.38	0.69	0.23	46.29

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234380&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]4 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=234380&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234380&T=0

As an alternative you can also use a 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 Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Rotated Factor Loadings
Variables	Factor1	Factor2	Factor3
Robo_vehiculos	0.179	0.956	-0.081
Capt_Veh�culos	0.253	0.744	-0.047
Motocicletas	0.475	0.472	-0.067
Capt_Motocicletas	0.465	0.604	0.065
Armas_robadas	0.855	0.226	-0.243
Homicidios	0.978	0.137	-0.028
Peatones	0.133	0.873	0.252
Capt_Peatones	0.066	0.888	0.267
Residencias	0.02	0.815	-0.225
Capt_Residencias	-0.085	0.118	0.827
Comercios	0.038	0.884	-0.101
Capt_Comercios	0.024	0.194	0.778
Buses	0.14	0.844	0.262
Capt_Buses	0.086	0.892	0.112
HArmaFuego_M	0.953	0.166	-0.048
HArmaFuego_F	0.865	0.282	-0.146
HArmaBlanca_M	0.881	-0.219	-0.074
HArmaBlanca_F	0.682	-0.338	0.267
HAsfixiaSec	0.799	0.173	0.241
MasfixiaSec	0.732	0.207	0.014
TOTHOM_	0.978	0.137	-0.028
MA_DETERMINAR_M	0.261	0.203	-0.616
MA_DETERMINAR_F	0.241	0.449	0.427
MA_DETERMINAR_IND	0.589	0.17	-0.176
M_TOTAL_conIND	0.813	0.516	-0.001

\begin{tabular}{lllllllll}
\hline
Rotated Factor Loadings \tabularnewline
Variables & Factor1 & Factor2 & Factor3 \tabularnewline
Robo_vehiculos & 0.179 & 0.956 & -0.081 \tabularnewline
Capt_Veh�culos & 0.253 & 0.744 & -0.047 \tabularnewline
Motocicletas & 0.475 & 0.472 & -0.067 \tabularnewline
Capt_Motocicletas & 0.465 & 0.604 & 0.065 \tabularnewline
Armas_robadas & 0.855 & 0.226 & -0.243 \tabularnewline
Homicidios & 0.978 & 0.137 & -0.028 \tabularnewline
Peatones & 0.133 & 0.873 & 0.252 \tabularnewline
Capt_Peatones & 0.066 & 0.888 & 0.267 \tabularnewline
Residencias & 0.02 & 0.815 & -0.225 \tabularnewline
Capt_Residencias & -0.085 & 0.118 & 0.827 \tabularnewline
Comercios & 0.038 & 0.884 & -0.101 \tabularnewline
Capt_Comercios & 0.024 & 0.194 & 0.778 \tabularnewline
Buses & 0.14 & 0.844 & 0.262 \tabularnewline
Capt_Buses & 0.086 & 0.892 & 0.112 \tabularnewline
HArmaFuego_M & 0.953 & 0.166 & -0.048 \tabularnewline
HArmaFuego_F & 0.865 & 0.282 & -0.146 \tabularnewline
HArmaBlanca_M & 0.881 & -0.219 & -0.074 \tabularnewline
HArmaBlanca_F & 0.682 & -0.338 & 0.267 \tabularnewline
HAsfixiaSec & 0.799 & 0.173 & 0.241 \tabularnewline
MasfixiaSec & 0.732 & 0.207 & 0.014 \tabularnewline
TOTHOM_ & 0.978 & 0.137 & -0.028 \tabularnewline
MA_DETERMINAR_M & 0.261 & 0.203 & -0.616 \tabularnewline
MA_DETERMINAR_F & 0.241 & 0.449 & 0.427 \tabularnewline
MA_DETERMINAR_IND & 0.589 & 0.17 & -0.176 \tabularnewline
M_TOTAL_conIND & 0.813 & 0.516 & -0.001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234380&T=1

[TABLE]
[ROW][C]Rotated Factor Loadings[/C][/ROW]
[ROW][C]Variables[/C][C]Factor1[/C][C]Factor2[/C][C]Factor3[/C][/ROW]
[ROW][C]Robo_vehiculos[/C][C]0.179[/C][C]0.956[/C][C]-0.081[/C][/ROW]
[ROW][C]Capt_Veh�culos[/C][C]0.253[/C][C]0.744[/C][C]-0.047[/C][/ROW]
[ROW][C]Motocicletas[/C][C]0.475[/C][C]0.472[/C][C]-0.067[/C][/ROW]
[ROW][C]Capt_Motocicletas[/C][C]0.465[/C][C]0.604[/C][C]0.065[/C][/ROW]
[ROW][C]Armas_robadas[/C][C]0.855[/C][C]0.226[/C][C]-0.243[/C][/ROW]
[ROW][C]Homicidios[/C][C]0.978[/C][C]0.137[/C][C]-0.028[/C][/ROW]
[ROW][C]Peatones[/C][C]0.133[/C][C]0.873[/C][C]0.252[/C][/ROW]
[ROW][C]Capt_Peatones[/C][C]0.066[/C][C]0.888[/C][C]0.267[/C][/ROW]
[ROW][C]Residencias[/C][C]0.02[/C][C]0.815[/C][C]-0.225[/C][/ROW]
[ROW][C]Capt_Residencias[/C][C]-0.085[/C][C]0.118[/C][C]0.827[/C][/ROW]
[ROW][C]Comercios[/C][C]0.038[/C][C]0.884[/C][C]-0.101[/C][/ROW]
[ROW][C]Capt_Comercios[/C][C]0.024[/C][C]0.194[/C][C]0.778[/C][/ROW]
[ROW][C]Buses[/C][C]0.14[/C][C]0.844[/C][C]0.262[/C][/ROW]
[ROW][C]Capt_Buses[/C][C]0.086[/C][C]0.892[/C][C]0.112[/C][/ROW]
[ROW][C]HArmaFuego_M[/C][C]0.953[/C][C]0.166[/C][C]-0.048[/C][/ROW]
[ROW][C]HArmaFuego_F[/C][C]0.865[/C][C]0.282[/C][C]-0.146[/C][/ROW]
[ROW][C]HArmaBlanca_M[/C][C]0.881[/C][C]-0.219[/C][C]-0.074[/C][/ROW]
[ROW][C]HArmaBlanca_F[/C][C]0.682[/C][C]-0.338[/C][C]0.267[/C][/ROW]
[ROW][C]HAsfixiaSec[/C][C]0.799[/C][C]0.173[/C][C]0.241[/C][/ROW]
[ROW][C]MasfixiaSec[/C][C]0.732[/C][C]0.207[/C][C]0.014[/C][/ROW]
[ROW][C]TOTHOM_[/C][C]0.978[/C][C]0.137[/C][C]-0.028[/C][/ROW]
[ROW][C]MA_DETERMINAR_M[/C][C]0.261[/C][C]0.203[/C][C]-0.616[/C][/ROW]
[ROW][C]MA_DETERMINAR_F[/C][C]0.241[/C][C]0.449[/C][C]0.427[/C][/ROW]
[ROW][C]MA_DETERMINAR_IND[/C][C]0.589[/C][C]0.17[/C][C]-0.176[/C][/ROW]
[ROW][C]M_TOTAL_conIND[/C][C]0.813[/C][C]0.516[/C][C]-0.001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234380&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234380&T=1

As an alternative you can also use a QR Code:

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

Rotated Factor Loadings
Variables	Factor1	Factor2	Factor3
Robo_vehiculos	0.179	0.956	-0.081
Capt_Veh�culos	0.253	0.744	-0.047
Motocicletas	0.475	0.472	-0.067
Capt_Motocicletas	0.465	0.604	0.065
Armas_robadas	0.855	0.226	-0.243
Homicidios	0.978	0.137	-0.028
Peatones	0.133	0.873	0.252
Capt_Peatones	0.066	0.888	0.267
Residencias	0.02	0.815	-0.225
Capt_Residencias	-0.085	0.118	0.827
Comercios	0.038	0.884	-0.101
Capt_Comercios	0.024	0.194	0.778
Buses	0.14	0.844	0.262
Capt_Buses	0.086	0.892	0.112
HArmaFuego_M	0.953	0.166	-0.048
HArmaFuego_F	0.865	0.282	-0.146
HArmaBlanca_M	0.881	-0.219	-0.074
HArmaBlanca_F	0.682	-0.338	0.267
HAsfixiaSec	0.799	0.173	0.241
MasfixiaSec	0.732	0.207	0.014
TOTHOM_	0.978	0.137	-0.028
MA_DETERMINAR_M	0.261	0.203	-0.616
MA_DETERMINAR_F	0.241	0.449	0.427
MA_DETERMINAR_IND	0.589	0.17	-0.176
M_TOTAL_conIND	0.813	0.516	-0.001

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 3 ;

Parameters (R input):

par1 = 3 ;

R code (references can be found in the software module):

par1 <- '3'
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]
y
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$scores,pch=20)
text(fs$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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code