R version 3.0.3 (2014-03-06) -- "Warm Puppy"
Copyright (C) 2014 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- array(list('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)
+ ,dim=c(26
+ ,22)
+ ,dimnames=list(c('DEP'
+ ,'Robo_vehiculos'
+ ,'Capt_Vehículos'
+ ,'Motocicletas'
+ ,'Capt_Motocicletas'
+ ,'Armas_robadas'
+ ,'Homicidios'
+ ,'Peatones'
+ ,'Capt_Peatones'
+ ,'Residencias'
+ ,'Capt_Residencias'
+ ,'Comercios'
+ ,'Capt_Comercios'
+ ,'Buses'
+ ,'Capt_Buses'
+ ,'HArmaFuego_M'
+ ,'HArmaFuego_F'
+ ,'HArmaBlanca_M'
+ ,'HArmaBlanca_F'
+ ,'HAsfixiaSec'
+ ,'MasfixiaSec'
+ ,'TOTHOM_'
+ ,'MA_DETERMINAR_M'
+ ,'MA_DETERMINAR_F'
+ ,'MA_DETERMINAR_IND'
+ ,'M_TOTAL_conIND')
+ ,1:22))
>  y <- array(NA,dim=c(26,22),dimnames=list(c('DEP','Robo_vehiculos','Capt_Vehículos','Motocicletas','Capt_Motocicletas','Armas_robadas','Homicidios','Peatones','Capt_Peatones','Residencias','Capt_Residencias','Comercios','Capt_Comercios','Buses','Capt_Buses','HArmaFuego_M','HArmaFuego_F','HArmaBlanca_M','HArmaBlanca_F','HAsfixiaSec','MasfixiaSec','TOTHOM_','MA_DETERMINAR_M','MA_DETERMINAR_F','MA_DETERMINAR_IND','M_TOTAL_conIND'),1:22))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
There were 22 warnings (use warnings() to see them)
> par1 = '4'
> par1 <- '4'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P., 2012, Factor Analysis (v1.0.2) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_factor_analysis.wasp/
> #Source of accompanying publication: 
> #
> 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
    Robo_vehiculos Capt_Veh\355culos Motocicletas Capt_Motocicletas
GTM         110.39              6.80        52.90              3.48
PRO          23.49              6.35        10.16              1.90
SAC          66.63              7.58        39.79              2.21
CHM          34.49              3.47        25.74              2.64
ESC          74.73              5.41       200.57              7.40
SRO          33.63              3.48        24.93              2.32
SOL           2.55              0.70         1.16              0.23
TOT          14.60              1.06         1.27              0.00
QUT          42.78              1.89        30.92              3.03
SUC          32.74              1.45        43.51              1.86
RET          15.84              1.32        52.14              1.98
SMA           8.02              0.78         7.14              0.78
HUE           8.69              0.70        31.64              1.30
QUI           4.50              0.84        12.66              0.94
BVP           6.33              1.86        13.40              2.23
AVP           8.48              0.27        14.56              0.63
PET           3.82              1.11        38.03              1.75
IZA          12.66              1.95        80.82              3.65
ZAC          17.17              0.90        29.82              2.26
CHQ          20.38              2.45        40.76              0.82
JAL           1.27              1.59         2.87              1.27
JUT          12.90              1.15         6.22              1.15
    Armas_robadas Homicidios Peatones Capt_Peatones Residencias
GTM         21.38      67.64    26.64         32.86       27.47
PRO         15.24      58.42     2.54          2.54        6.98
SAC         15.79      21.16    13.89         18.63       22.74
CHM          7.43      14.36     5.78          4.79        4.29
ESC         26.33      98.22    18.08         20.36        2.14
SRO         20.29      82.34    11.31         12.18        4.35
SOL          0.93       8.59     3.25          3.25        1.86
TOT          0.63       7.83     2.75          2.96        0.21
QUT          9.47      26.88    18.43         21.33        3.66
SUC          9.95      30.87    18.23         20.93        4.35
RET          5.94      35.97    12.87         16.50        1.32
SMA          4.70      15.56     4.21          4.11        1.66
HUE          4.35       6.43     4.78          4.87        1.65
QUI          2.09       6.38     2.93          3.14        0.73
BVP          6.33      18.62    12.29         14.89        2.61
AVP          6.97      13.04     8.13          9.73        1.43
PET         11.30      59.36     5.89          3.66        1.27
IZA         20.69      79.85     5.60          5.84        2.43
ZAC         42.46      92.61     6.32          4.97        6.32
CHQ         29.89      96.20     8.15          7.61        3.26
JAL         12.42      54.44     4.78          6.05        1.59
JUT          6.22      61.26     7.83          8.52        3.45
    Capt_Residencias Comercios Capt_Comercios Buses Capt_Buses HArmaFuego_M
GTM             0.70     12.70           1.12  2.11       4.08        49.87
PRO             1.27      3.17           1.90  0.63       0.63        38.10
SAC             0.00      9.16           0.63  0.95       0.63        10.10
CHM             0.33      5.78           0.00  0.33       0.17         9.41
ESC             0.14      4.13           0.71  1.42       1.57        70.75
SRO             0.87      5.22           0.87  1.16       0.00        56.83
SOL             0.70      2.55           0.70  0.23       0.00         3.72
TOT             0.00      2.54           0.42  0.00       0.00         0.85
QUT             1.64      4.04           1.64  0.63       1.14        17.54
SUC             1.24      5.39           1.04  1.04       0.41        17.20
RET             1.65      2.64           1.98  1.32       0.66        23.10
SMA             0.49      1.66           0.10  0.10       0.00        10.67
HUE             0.52      1.13           0.09  0.17       0.00         3.82
QUI             0.10      2.41           0.42  0.21       0.00         2.30
BVP             0.37      4.84           2.61  0.00       0.00         7.45
AVP             0.63      1.07           0.27  0.18       0.00         7.41
PET             0.48      1.91           0.00  0.48       0.00        41.54
IZA             0.73      2.19           0.49  0.00       0.00        59.16
ZAC             0.00      4.52           0.00  0.00       0.00        66.41
CHQ             0.27      2.45           0.27  0.00       0.00        58.70
JAL             0.32      1.59           1.91  0.32       0.00        33.11
JUT             0.23      4.15           0.69  0.23       0.00        42.83
    HArmaFuego_F HArmaBlanca_M HArmaBlanca_F HAsfixiaSec MasfixiaSec TOTHOM_
GTM         7.88          2.84          0.38        5.01        1.66   67.64
PRO         3.17          5.08          1.90        8.89        1.27   58.42
SAC         1.58          1.26          0.00        6.32        1.89   21.16
CHM         0.83          1.16          0.33        2.15        0.50   14.36
ESC         8.11          5.98          0.28       11.25        1.85   98.22
SRO         4.64          6.67          1.74        8.99        3.48   82.34
SOL         0.00          0.70          0.23        3.48        0.46    8.59
TOT         0.42          0.63          0.21        4.23        1.48    7.83
QUT         1.77          1.77          0.38        4.29        1.14   26.88
SUC         2.28          2.69          0.21        7.25        1.24   30.87
RET         1.32          1.65          0.33        8.25        1.32   35.97
SMA         0.29          1.37          0.10        2.74        0.39   15.56
HUE         0.26          0.70          0.09        1.48        0.09    6.43
QUI         0.31          0.73          0.21        2.41        0.42    6.38
BVP         1.49          0.74          0.37        7.82        0.74   18.62
AVP         0.45          1.61          0.18        2.86        0.54   13.04
PET         3.66          7.48          0.80        4.93        0.95   59.36
IZA         6.82          5.60          1.46        5.84        0.97   79.85
ZAC         7.68          7.23          0.45        8.13        2.71   92.61
CHQ         6.25         14.95          1.90       11.68        2.72   96.20
JAL         4.78          7.96          1.91        5.09        1.59   54.44
JUT         7.37          4.38          1.38        4.84        0.46   61.26
    MA_DETERMINAR_M MA_DETERMINAR_F MA_DETERMINAR_IND M_TOTAL_conIND
GTM            1.31            0.99              0.57          77.34
PRO            0.63            0.63              0.63          44.45
SAC            4.11            0.32              0.63          36.95
CHM            0.83            0.50              0.17          35.81
ESC            3.27            1.00              1.57          73.88
SRO            0.58            0.87              0.00          67.84
SOL            1.39            0.23              0.00          18.35
TOT            0.85            0.63              0.42          15.87
QUT            0.25            0.76              0.63          34.83
SUC            0.41            0.21              0.41          36.68
RET            1.32            0.66              0.66          37.62
SMA            1.27            0.10              0.10          12.72
HUE            1.83            0.26              0.70          13.12
QUI            0.52            0.42              0.21          14.44
BVP            0.37            1.12              0.74          23.09
AVP            0.71            0.18              0.27          14.47
PET            2.23            0.64              0.16          25.30
IZA            0.49            0.73              0.73          49.18
ZAC            1.81            0.00              1.81          67.76
CHQ            2.45            0.54              0.82          58.15
JAL            1.27            0.00              0.96          35.02
JUT            4.38            0.69              0.23          46.29
> fit <- principal(y, nfactors=par1, rotate='varimax')
Loading required package: GPArotation
The determinant of the smoothed correlation was zero.
This means the objective function is not defined for the null model either.
The Chi square is thus based upon observed correlations.
In factor.scores, the correlation matrix is singular, an approximation is used
Warning messages:
1: In log(det(m.inv.r)) : NaNs produced
2: In cor.smooth(r) : Matrix was not positive definite, smoothing was done
3: In cor.smooth(r) : Matrix was not positive definite, smoothing was done
> fit
Principal Components Analysis
Call: principal(r = y, nfactors = par1, rotate = "varimax")
Standardized loadings (pattern matrix) based upon correlation matrix
                    RC1   RC2   RC4   RC3   h2    u2
Robo_vehiculos     0.14  0.92  0.29 -0.06 0.96 0.045
Capt_Veh\355culos  0.27  0.78  0.05 -0.03 0.68 0.316
Motocicletas       0.28  0.24  0.88 -0.06 0.91 0.086
Capt_Motocicletas  0.29  0.40  0.80  0.07 0.89 0.113
Armas_robadas      0.82  0.21  0.24 -0.26 0.84 0.157
Homicidios         0.94  0.11  0.26 -0.05 0.98 0.025
Peatones           0.07  0.80  0.37  0.27 0.85 0.149
Capt_Peatones      0.01  0.82  0.33  0.29 0.87 0.133
Residencias        0.09  0.92 -0.18 -0.21 0.93 0.068
Capt_Residencias  -0.07  0.10  0.00  0.83 0.71 0.294
Comercios          0.08  0.95 -0.05 -0.08 0.92 0.084
Capt_Comercios     0.05  0.19 -0.01  0.78 0.65 0.351
Buses              0.11  0.81  0.25  0.28 0.80 0.198
Capt_Buses         0.05  0.86  0.25  0.13 0.82 0.183
HArmaFuego_M       0.91  0.13  0.31 -0.06 0.94 0.060
HArmaFuego_F       0.82  0.25  0.29 -0.16 0.85 0.152
HArmaBlanca_M      0.91 -0.17 -0.03 -0.10 0.87 0.134
HArmaBlanca_F      0.79 -0.22 -0.34  0.24 0.84 0.163
HAsfixiaSec        0.77  0.13  0.27  0.23 0.73 0.273
MasfixiaSec        0.77  0.27 -0.05  0.00 0.67 0.327
TOTHOM_            0.94  0.11  0.26 -0.05 0.98 0.025
MA_DETERMINAR_M    0.23  0.20  0.14 -0.62 0.49 0.511
MA_DETERMINAR_F    0.18  0.36  0.34  0.43 0.47 0.533
MA_DETERMINAR_IND  0.47  0.03  0.56 -0.18 0.57 0.435
M_TOTAL_conIND     0.79  0.50  0.26 -0.01 0.94 0.065

                       RC1  RC2  RC4  RC3
SS loadings           7.81 6.89 2.98 2.44
Proportion Var        0.31 0.28 0.12 0.10
Cumulative Var        0.31 0.59 0.71 0.80
Proportion Explained  0.39 0.34 0.15 0.12
Cumulative Proportion 0.39 0.73 0.88 1.00

Test of the hypothesis that 4 components are sufficient.

The degrees of freedom for the null model are  300  and the objective function was  155.94
The degrees of freedom for the model are 206  and the objective function was  NaN 
The total number of observations was  22  with MLE Chi Square =  NaN  with prob <  NaN 

Fit based upon off diagonal values = 0.99> fs <- factor.scores(y,fit)
Warning messages:
1: In cor.smooth(r) : Matrix was not positive definite, smoothing was done
2: In invMatSqrt(t(L) %*% inv.r %*% L) :
  complex eigen values detected by invMatSqrt, results are suspect
> fs
$scores
           RC1          RC2         RC4         RC3
GTM  0.1965845  3.437897553 -0.27630336  0.06778484
PRO  0.9251552 -0.006428577 -1.02888585  1.33094589
SAC -0.5492093  1.987231608 -0.72697505 -1.76191689
CHM -0.9008150  0.176858196 -0.15359328 -0.67944531
ESC  0.7733316  0.451787623  3.73039378 -0.55065150
SRO  1.5261001  0.360842658 -1.00618885  1.06006367
SOL -0.9768555 -0.492220336 -0.68785660 -0.06648894
TOT -0.8431905 -0.512534871 -0.50451444 -0.42167594
QUT -0.6244977  0.392076074  0.57715636  1.68191371
SUC -0.4546541  0.409866837  0.17532105  0.91784790
RET -0.3313025 -0.061971867  0.66982209  1.83039369
SMA -0.9772944 -0.593357355 -0.34406888 -0.60962897
HUE -1.2539021 -0.688430954  0.44722305 -0.75072564
QUI -1.1051400 -0.594449626 -0.22208519 -0.39943355
BVP -0.6031725 -0.193908986  0.34307947  1.35500816
AVP -0.9672488 -0.571392052 -0.12701798 -0.15494127
PET  0.2660253 -0.686091740  0.01691691 -0.41827989
IZA  0.8695310 -0.853085714  0.86119484  0.22976065
ZAC  1.5391119 -0.475074195  0.33186622 -1.73412312
CHQ  2.1849627 -0.566929962 -0.70004421 -0.46960310
JAL  0.8594915 -0.739867265 -0.86832630  0.28376625
JUT  0.4469887 -0.180817052 -0.50711379 -0.74057066

$weights
                           RC1          RC2          RC4          RC3
Robo_vehiculos    -0.012216060  0.161734449  0.017782380 -0.057168871
Capt_Veh\355culos  0.018636870  0.135182842 -0.108969326 -0.044383710
Motocicletas      -0.057013538 -0.067961480  0.395029712 -0.033275637
Capt_Motocicletas -0.050633535 -0.039366811  0.324214036  0.016201982
Armas_robadas      0.104621912  0.014218745  0.006116123 -0.091619805
Homicidios         0.135755767 -0.027733618  0.011972869  0.007391426
Peatones          -0.038958413  0.089347824  0.076786766  0.078544192
Capt_Peatones     -0.041533938  0.101337582  0.059756230  0.082768750
Residencias        0.011673078  0.211856655 -0.225265676 -0.124212691
Capt_Residencias   0.013691108 -0.017753801 -0.008892515  0.347693779
Comercios          0.000153084  0.193945231 -0.157838910 -0.075505714
Capt_Comercios     0.037560819  0.009176023 -0.038845054  0.328895178
Buses             -0.011598140  0.114130113  0.010420241  0.086650584
Capt_Buses        -0.036749295  0.119360062  0.005825589  0.013991600
HArmaFuego_M       0.106968460 -0.020590872  0.041538035 -0.004465264
HArmaFuego_F       0.092160287  0.003423898  0.021744869 -0.053410714
HArmaBlanca_M      0.154064501 -0.038809120 -0.095107715 -0.003585163
HArmaBlanca_F      0.181212067 -0.022601624 -0.235107055  0.139835226
HAsfixiaSec        0.099752435 -0.033974491  0.034283874  0.114539222
MasfixiaSec        0.125783401  0.041428511 -0.149296457  0.012387281
TOTHOM_            0.129083717 -0.026795150  0.017974281  0.003403605
MA_DETERMINAR_M   -0.002407848  0.042822392  0.026481942 -0.265307537
MA_DETERMINAR_F    0.001812715  0.006258267  0.104475880  0.172992672
MA_DETERMINAR_IND  0.007664205 -0.066393526  0.232871285 -0.067929168
M_TOTAL_conIND     0.088531931  0.046209260 -0.019875904 -0.001656419

$r.scores
              RC1           RC2          RC4           RC3
RC1  1.0000000000 -0.0009356554 0.0006805161 -0.0009388735
RC2 -0.0009356554  1.0000000000 0.0010689562 -0.0021724269
RC4  0.0006805161  0.0010689562 1.0000000000  0.0006169662
RC3 -0.0009388735 -0.0021724269 0.0006169662  1.0000000000

$R2
      RC1       RC2       RC4       RC3 
1.0062306 0.9960234 1.0002447 1.0002840 

> postscript(file="/var/wessaorg/rcomp/tmp/1l9ab1396456337.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=16.666666666667,height=16.666666666667) 
> fa.diagram(fit)
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/2s4do1396456337.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=16.666666666667,height=16.666666666667) 
> plot(fs$scores,pch=20)
> text(fs$scores,labels=rownames(y),pos=3)
> dev.off()
null device 
          1 
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/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="/var/wessaorg/rcomp/tmp/3juv11396456337.tab") 
> 
> try(system("convert tmp/1l9ab1396456337.ps tmp/1l9ab1396456337.png",intern=TRUE))
character(0)
> try(system("convert tmp/2s4do1396456337.ps tmp/2s4do1396456337.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.732   1.065   4.797