Factor Analysis by Principal Factor Analysis (PFA)
factorScorePfa.RdPerform principal factor factor analysis on a covariance matrix or data matrix.
Arguments
- x
A numeric matrix or an object that can be coerced to a numeric matrix.
- factors
The number of factors to be fitted.
- covmat
A covariance matrix, or a covariance list as returned by
cov.wt. Of course, correlation matrices are covariance matrices.- cor
A logical value indicating whether the calculation should use the covariance matrix (
cor = FALSE) or the correlation matrix (cor = TRUE).- rotation
character. "none" or "varimax": it will be called with first argument the loadings matrix, and should return a list with component
loadingsgiving the rotated loadings, or just the rotated loadings.- scoresMethod
Type of scores to produce, if any. The default is
"none","regression"gives Thompson's scores,"Bartlett"gives Bartlett's weighted least-squares scores.
Details
Other feasible usages are:
factorScorePfa(factors, covmat)
factorScorePfa(x, factors, rotation, scoresMethod)
If x is missing, then the following components of the result will be NULL: scores, ScoringCoef, meanF, corF, and n.obs.
Value
An object of class "factorScorePfa" with components:
- call
The matched call.
- loadings
A matrix of loadings, one column for each factor. This is of class
"loadings"ifrotation = "varimax": seeloadingsfor its print method; It is a plain matrix ifrotation = "none".- communality
The common variance.
- uniquenesses
The uniquenesses/specific variance computed.
- covariance
The robust/classical covariance matrix.
- correlation
The robust/classical correlation matrix.
- usedMatrix
The used matrix (running matrix). It may be the covariance or correlation matrix according to the value of
cor.- reducedCorrelation
The last reduced correlation matrix.
- factors
The argument factors.
- method
The method: always
"pfa".- scores
If requested, a matrix of scores. NULL if
xis missing.- scoringCoef
The scoring coefficients. NULL if
xis missing.- meanF
The sample mean of the scores. NULL if
xis missing.- corF
The sample correlation matrix of the scores. NULL if
xis missing.- scoresMethod
The argument
scoresMethod.- n.obs
The number of observations if available. NULL if
xis missing.- center
The center of the data.
- eigenvalues
The eigenvalues of the usedMatrix.
Author
Ying-Ying Zhang (Robert) robertzhangyying@qq.com
Examples
data(stock611)
R611 = cor(stock611[,3:12]); R611
#> x1 x2 x3 x4 x5 x6
#> x1 1.000000000 0.993167953 0.97034436 0.95609585 0.025163276 -0.015083014
#> x2 0.993167953 1.000000000 0.98346739 0.97164822 0.045110192 -0.004994507
#> x3 0.970344358 0.983467387 1.00000000 0.99749137 0.108682670 0.019464360
#> x4 0.956095848 0.971648219 0.99749137 1.00000000 0.133591430 0.029278642
#> x5 0.025163276 0.045110192 0.10868267 0.13359143 1.000000000 0.575741829
#> x6 -0.015083014 -0.004994507 0.01946436 0.02927864 0.575741829 1.000000000
#> x7 0.009780105 0.014165548 0.03527696 0.04429804 0.326885328 0.013993207
#> x8 0.017155253 0.036225902 0.09193857 0.11691517 0.840607798 0.298489402
#> x9 0.905256744 0.898571356 0.91651307 0.91138685 0.041581017 -0.009413168
#> x10 0.989449715 0.989605604 0.97531937 0.96400047 0.001814838 -0.046691411
#> x7 x8 x9 x10
#> x1 0.009780105 0.01715525 0.905256744 0.989449715
#> x2 0.014165548 0.03622590 0.898571356 0.989605604
#> x3 0.035276960 0.09193857 0.916513072 0.975319373
#> x4 0.044298039 0.11691517 0.911386851 0.964000474
#> x5 0.326885328 0.84060780 0.041581017 0.001814838
#> x6 0.013993207 0.29848940 -0.009413168 -0.046691411
#> x7 1.000000000 0.35473163 0.012122296 0.007367798
#> x8 0.354731631 1.00000000 0.010355218 0.016828080
#> x9 0.012122296 0.01035522 1.000000000 0.911435130
#> x10 0.007367798 0.01682808 0.911435130 1.000000000
## covmat is a matrix
fsPfa1 = factorScorePfa(factors = 3, covmat = R611); fsPfa1
#> $call
#> factorScorePfa(factors = 3, covmat = R611)
#>
#> $loadings
#>
#> Loadings:
#> Factor1 Factor2 Factor3
#> X1 0.987
#> X2 0.992
#> X3 0.993
#> X4 0.982
#> X5 0.809 0.627
#> X6 0.123 0.758
#> X7 0.440
#> X8 0.839 0.257
#> X9 0.919
#> X10 0.992
#>
#> Factor1 Factor2 Factor3
#> SS loadings 5.739 1.582 1.041
#> Proportion Var 0.574 0.158 0.104
#> Cumulative Var 0.574 0.732 0.836
#>
#> $communality
#> X1 X2 X3 X4 X5 X6 X7 X8
#> 0.9750545 0.9851462 0.9910436 0.9743261 1.0496144 0.5901617 0.1962303 0.7707067
#> X9 X10
#> 0.8438532 0.9861559
#>
#> $uniquenesses
#> x1 x2 x3 x4 x5 x6
#> 0.024945502 0.014853820 0.008956431 0.025673931 -0.049614435 0.409838264
#> x7 x8 x9 x10
#> 0.803769729 0.229293266 0.156146817 0.013844079
#>
#> $covariance
#> x1 x2 x3 x4 x5 x6
#> x1 1.000000000 0.993167953 0.97034436 0.95609585 0.025163276 -0.015083014
#> x2 0.993167953 1.000000000 0.98346739 0.97164822 0.045110192 -0.004994507
#> x3 0.970344358 0.983467387 1.00000000 0.99749137 0.108682670 0.019464360
#> x4 0.956095848 0.971648219 0.99749137 1.00000000 0.133591430 0.029278642
#> x5 0.025163276 0.045110192 0.10868267 0.13359143 1.000000000 0.575741829
#> x6 -0.015083014 -0.004994507 0.01946436 0.02927864 0.575741829 1.000000000
#> x7 0.009780105 0.014165548 0.03527696 0.04429804 0.326885328 0.013993207
#> x8 0.017155253 0.036225902 0.09193857 0.11691517 0.840607798 0.298489402
#> x9 0.905256744 0.898571356 0.91651307 0.91138685 0.041581017 -0.009413168
#> x10 0.989449715 0.989605604 0.97531937 0.96400047 0.001814838 -0.046691411
#> x7 x8 x9 x10
#> x1 0.009780105 0.01715525 0.905256744 0.989449715
#> x2 0.014165548 0.03622590 0.898571356 0.989605604
#> x3 0.035276960 0.09193857 0.916513072 0.975319373
#> x4 0.044298039 0.11691517 0.911386851 0.964000474
#> x5 0.326885328 0.84060780 0.041581017 0.001814838
#> x6 0.013993207 0.29848940 -0.009413168 -0.046691411
#> x7 1.000000000 0.35473163 0.012122296 0.007367798
#> x8 0.354731631 1.00000000 0.010355218 0.016828080
#> x9 0.012122296 0.01035522 1.000000000 0.911435130
#> x10 0.007367798 0.01682808 0.911435130 1.000000000
#>
#> $correlation
#> x1 x2 x3 x4 x5 x6
#> x1 1.000000000 0.993167953 0.97034436 0.95609585 0.025163276 -0.015083014
#> x2 0.993167953 1.000000000 0.98346739 0.97164822 0.045110192 -0.004994507
#> x3 0.970344358 0.983467387 1.00000000 0.99749137 0.108682670 0.019464360
#> x4 0.956095848 0.971648219 0.99749137 1.00000000 0.133591430 0.029278642
#> x5 0.025163276 0.045110192 0.10868267 0.13359143 1.000000000 0.575741829
#> x6 -0.015083014 -0.004994507 0.01946436 0.02927864 0.575741829 1.000000000
#> x7 0.009780105 0.014165548 0.03527696 0.04429804 0.326885328 0.013993207
#> x8 0.017155253 0.036225902 0.09193857 0.11691517 0.840607798 0.298489402
#> x9 0.905256744 0.898571356 0.91651307 0.91138685 0.041581017 -0.009413168
#> x10 0.989449715 0.989605604 0.97531937 0.96400047 0.001814838 -0.046691411
#> x7 x8 x9 x10
#> x1 0.009780105 0.01715525 0.905256744 0.989449715
#> x2 0.014165548 0.03622590 0.898571356 0.989605604
#> x3 0.035276960 0.09193857 0.916513072 0.975319373
#> x4 0.044298039 0.11691517 0.911386851 0.964000474
#> x5 0.326885328 0.84060780 0.041581017 0.001814838
#> x6 0.013993207 0.29848940 -0.009413168 -0.046691411
#> x7 1.000000000 0.35473163 0.012122296 0.007367798
#> x8 0.354731631 1.00000000 0.010355218 0.016828080
#> x9 0.012122296 0.01035522 1.000000000 0.911435130
#> x10 0.007367798 0.01682808 0.911435130 1.000000000
#>
#> $usedMatrix
#> x1 x2 x3 x4 x5 x6
#> x1 1.000000000 0.993167953 0.97034436 0.95609585 0.025163276 -0.015083014
#> x2 0.993167953 1.000000000 0.98346739 0.97164822 0.045110192 -0.004994507
#> x3 0.970344358 0.983467387 1.00000000 0.99749137 0.108682670 0.019464360
#> x4 0.956095848 0.971648219 0.99749137 1.00000000 0.133591430 0.029278642
#> x5 0.025163276 0.045110192 0.10868267 0.13359143 1.000000000 0.575741829
#> x6 -0.015083014 -0.004994507 0.01946436 0.02927864 0.575741829 1.000000000
#> x7 0.009780105 0.014165548 0.03527696 0.04429804 0.326885328 0.013993207
#> x8 0.017155253 0.036225902 0.09193857 0.11691517 0.840607798 0.298489402
#> x9 0.905256744 0.898571356 0.91651307 0.91138685 0.041581017 -0.009413168
#> x10 0.989449715 0.989605604 0.97531937 0.96400047 0.001814838 -0.046691411
#> x7 x8 x9 x10
#> x1 0.009780105 0.01715525 0.905256744 0.989449715
#> x2 0.014165548 0.03622590 0.898571356 0.989605604
#> x3 0.035276960 0.09193857 0.916513072 0.975319373
#> x4 0.044298039 0.11691517 0.911386851 0.964000474
#> x5 0.326885328 0.84060780 0.041581017 0.001814838
#> x6 0.013993207 0.29848940 -0.009413168 -0.046691411
#> x7 1.000000000 0.35473163 0.012122296 0.007367798
#> x8 0.354731631 1.00000000 0.010355218 0.016828080
#> x9 0.012122296 0.01035522 1.000000000 0.911435130
#> x10 0.007367798 0.01682808 0.911435130 1.000000000
#>
#> $reducedCorrelation
#> x1 x2 x3 x4 x5 x6
#> x1 0.975053749 0.993167953 0.97034436 0.95609585 0.025163276 -0.015083014
#> x2 0.993167953 0.985145619 0.98346739 0.97164822 0.045110192 -0.004994507
#> x3 0.970344358 0.983467387 0.99104353 0.99749137 0.108682670 0.019464360
#> x4 0.956095848 0.971648219 0.99749137 0.97432563 0.133591430 0.029278642
#> x5 0.025163276 0.045110192 0.10868267 0.13359143 1.049119077 0.575741829
#> x6 -0.015083014 -0.004994507 0.01946436 0.02927864 0.575741829 0.589483277
#> x7 0.009780105 0.014165548 0.03527696 0.04429804 0.326885328 0.013993207
#> x8 0.017155253 0.036225902 0.09193857 0.11691517 0.840607798 0.298489402
#> x9 0.905256744 0.898571356 0.91651307 0.91138685 0.041581017 -0.009413168
#> x10 0.989449715 0.989605604 0.97531937 0.96400047 0.001814838 -0.046691411
#> x7 x8 x9 x10
#> x1 0.009780105 0.01715525 0.905256744 0.989449715
#> x2 0.014165548 0.03622590 0.898571356 0.989605604
#> x3 0.035276960 0.09193857 0.916513072 0.975319373
#> x4 0.044298039 0.11691517 0.911386851 0.964000474
#> x5 0.326885328 0.84060780 0.041581017 0.001814838
#> x6 0.013993207 0.29848940 -0.009413168 -0.046691411
#> x7 0.196169852 0.35473163 0.012122296 0.007367798
#> x8 0.354731631 0.77150802 0.010355218 0.016828080
#> x9 0.012122296 0.01035522 0.843854435 0.911435130
#> x10 0.007367798 0.01682808 0.911435130 0.986159101
#>
#> $factors
#> [1] 3
#>
#> $method
#> [1] "pfa"
#>
#> $scores
#> NULL
#>
#> $scoringCoef
#> NULL
#>
#> $meanF
#> NULL
#>
#> $corF
#> NULL
#>
#> $scoresMethod
#> [1] "none"
#>
#> $n.obs
#> NULL
#>
#> $center
#> NULL
#>
#> $eigenvalues
#> [1] 5.7900498488 2.3189552681 1.0086871619 0.5741391921 0.1432389680
#> [6] 0.0991672355 0.0516167794 0.0094032935 0.0039695244 0.0007727282
#>
#> attr(,"class")
#> [1] "factorScorePfa"
## covmat is a list
covx = rrcov::Cov(stock611[,3:12])
covmat = list(cov = rrcov::getCov(covx), center = rrcov::getCenter(covx), n.obs = covx@n.obs)
fsPfa2 = factorScorePfa(factors = 3, cor = TRUE, covmat = covmat); fsPfa2
#> $call
#> factorScorePfa(factors = 3, covmat = covmat, cor = TRUE)
#>
#> $loadings
#>
#> Loadings:
#> Factor1 Factor2 Factor3
#> X1 0.987
#> X2 0.992
#> X3 0.993
#> X4 0.982
#> X5 0.809 0.627
#> X6 0.123 0.758
#> X7 0.440
#> X8 0.839 0.257
#> X9 0.919
#> X10 0.992
#>
#> Factor1 Factor2 Factor3
#> SS loadings 5.739 1.582 1.041
#> Proportion Var 0.574 0.158 0.104
#> Cumulative Var 0.574 0.732 0.836
#>
#> $communality
#> X1 X2 X3 X4 X5 X6 X7 X8
#> 0.9750545 0.9851462 0.9910436 0.9743261 1.0496144 0.5901617 0.1962303 0.7707067
#> X9 X10
#> 0.8438532 0.9861559
#>
#> $uniquenesses
#> x1 x2 x3 x4 x5 x6
#> 0.024945502 0.014853820 0.008956431 0.025673931 -0.049614435 0.409838264
#> x7 x8 x9 x10
#> 0.803769729 0.229293266 0.156146817 0.013844079
#>
#> $covariance
#> x1 x2 x3 x4 x5
#> x1 1.555244e+20 2.863371e+19 1.110191e+19 7.219175e+18 7.345648e+07
#> x2 2.863371e+19 5.344552e+18 2.085874e+18 1.360040e+18 2.441148e+07
#> x3 1.110191e+19 2.085874e+18 8.416758e+17 5.540749e+17 2.333976e+07
#> x4 7.219175e+18 1.360040e+18 5.540749e+17 3.665843e+17 1.893343e+07
#> x5 7.345648e+07 2.441148e+07 2.333976e+07 1.893343e+07 5.479330e-02
#> x6 -2.598053e+08 -1.594810e+07 2.466455e+07 2.448491e+07 1.861453e-01
#> x7 3.466949e+09 9.308800e+08 9.199586e+08 7.623881e+08 2.175023e+00
#> x8 1.107546e+09 4.335511e+08 4.366522e+08 3.664572e+08 1.018644e+00
#> x9 1.863773e+20 3.429496e+19 1.388140e+19 9.109861e+18 1.606871e+08
#> x10 4.420202e+19 8.195339e+18 3.205298e+18 2.090804e+18 1.521776e+06
#> x6 x7 x8 x9 x10
#> x1 -2.598053e+08 3.466949e+09 1.107546e+09 1.863773e+20 4.420202e+19
#> x2 -1.594810e+07 9.308800e+08 4.335511e+08 3.429496e+19 8.195339e+18
#> x3 2.466455e+07 9.199586e+08 4.366522e+08 1.388140e+19 3.205298e+18
#> x4 2.448491e+07 7.623881e+08 3.664572e+08 9.109861e+18 2.090804e+18
#> x5 1.861453e-01 2.175023e+00 1.018644e+00 1.606871e+08 1.521776e+06
#> x6 1.907749e+00 5.493923e-01 2.134298e+00 -2.146439e+08 -2.310185e+08
#> x7 5.493923e-01 8.079963e+02 5.219995e+01 5.688682e+09 7.502250e+08
#> x8 2.134298e+00 5.219995e+01 2.679976e+01 8.850072e+08 3.120680e+08
#> x9 -2.146439e+08 5.688682e+09 8.850072e+08 2.725487e+20 5.390102e+19
#> x10 -2.310185e+08 7.502250e+08 3.120680e+08 5.390102e+19 1.283212e+19
#>
#> $correlation
#> x1 x2 x3 x4 x5 x6
#> x1 1.000000000 0.993167953 0.97034436 0.95609585 0.025163276 -0.015083014
#> x2 0.993167953 1.000000000 0.98346739 0.97164822 0.045110192 -0.004994507
#> x3 0.970344358 0.983467387 1.00000000 0.99749137 0.108682670 0.019464360
#> x4 0.956095848 0.971648219 0.99749137 1.00000000 0.133591430 0.029278642
#> x5 0.025163276 0.045110192 0.10868267 0.13359143 1.000000000 0.575741829
#> x6 -0.015083014 -0.004994507 0.01946436 0.02927864 0.575741829 1.000000000
#> x7 0.009780105 0.014165548 0.03527696 0.04429804 0.326885328 0.013993207
#> x8 0.017155253 0.036225902 0.09193857 0.11691517 0.840607798 0.298489402
#> x9 0.905256744 0.898571356 0.91651307 0.91138685 0.041581017 -0.009413168
#> x10 0.989449715 0.989605604 0.97531937 0.96400047 0.001814838 -0.046691411
#> x7 x8 x9 x10
#> x1 0.009780105 0.01715525 0.905256744 0.989449715
#> x2 0.014165548 0.03622590 0.898571356 0.989605604
#> x3 0.035276960 0.09193857 0.916513072 0.975319373
#> x4 0.044298039 0.11691517 0.911386851 0.964000474
#> x5 0.326885328 0.84060780 0.041581017 0.001814838
#> x6 0.013993207 0.29848940 -0.009413168 -0.046691411
#> x7 1.000000000 0.35473163 0.012122296 0.007367798
#> x8 0.354731631 1.00000000 0.010355218 0.016828080
#> x9 0.012122296 0.01035522 1.000000000 0.911435130
#> x10 0.007367798 0.01682808 0.911435130 1.000000000
#>
#> $usedMatrix
#> x1 x2 x3 x4 x5 x6
#> x1 1.000000000 0.993167953 0.97034436 0.95609585 0.025163276 -0.015083014
#> x2 0.993167953 1.000000000 0.98346739 0.97164822 0.045110192 -0.004994507
#> x3 0.970344358 0.983467387 1.00000000 0.99749137 0.108682670 0.019464360
#> x4 0.956095848 0.971648219 0.99749137 1.00000000 0.133591430 0.029278642
#> x5 0.025163276 0.045110192 0.10868267 0.13359143 1.000000000 0.575741829
#> x6 -0.015083014 -0.004994507 0.01946436 0.02927864 0.575741829 1.000000000
#> x7 0.009780105 0.014165548 0.03527696 0.04429804 0.326885328 0.013993207
#> x8 0.017155253 0.036225902 0.09193857 0.11691517 0.840607798 0.298489402
#> x9 0.905256744 0.898571356 0.91651307 0.91138685 0.041581017 -0.009413168
#> x10 0.989449715 0.989605604 0.97531937 0.96400047 0.001814838 -0.046691411
#> x7 x8 x9 x10
#> x1 0.009780105 0.01715525 0.905256744 0.989449715
#> x2 0.014165548 0.03622590 0.898571356 0.989605604
#> x3 0.035276960 0.09193857 0.916513072 0.975319373
#> x4 0.044298039 0.11691517 0.911386851 0.964000474
#> x5 0.326885328 0.84060780 0.041581017 0.001814838
#> x6 0.013993207 0.29848940 -0.009413168 -0.046691411
#> x7 1.000000000 0.35473163 0.012122296 0.007367798
#> x8 0.354731631 1.00000000 0.010355218 0.016828080
#> x9 0.012122296 0.01035522 1.000000000 0.911435130
#> x10 0.007367798 0.01682808 0.911435130 1.000000000
#>
#> $reducedCorrelation
#> x1 x2 x3 x4 x5 x6
#> x1 0.975053749 0.993167953 0.97034436 0.95609585 0.025163276 -0.015083014
#> x2 0.993167953 0.985145619 0.98346739 0.97164822 0.045110192 -0.004994507
#> x3 0.970344358 0.983467387 0.99104353 0.99749137 0.108682670 0.019464360
#> x4 0.956095848 0.971648219 0.99749137 0.97432563 0.133591430 0.029278642
#> x5 0.025163276 0.045110192 0.10868267 0.13359143 1.049119077 0.575741829
#> x6 -0.015083014 -0.004994507 0.01946436 0.02927864 0.575741829 0.589483277
#> x7 0.009780105 0.014165548 0.03527696 0.04429804 0.326885328 0.013993207
#> x8 0.017155253 0.036225902 0.09193857 0.11691517 0.840607798 0.298489402
#> x9 0.905256744 0.898571356 0.91651307 0.91138685 0.041581017 -0.009413168
#> x10 0.989449715 0.989605604 0.97531937 0.96400047 0.001814838 -0.046691411
#> x7 x8 x9 x10
#> x1 0.009780105 0.01715525 0.905256744 0.989449715
#> x2 0.014165548 0.03622590 0.898571356 0.989605604
#> x3 0.035276960 0.09193857 0.916513072 0.975319373
#> x4 0.044298039 0.11691517 0.911386851 0.964000474
#> x5 0.326885328 0.84060780 0.041581017 0.001814838
#> x6 0.013993207 0.29848940 -0.009413168 -0.046691411
#> x7 0.196169852 0.35473163 0.012122296 0.007367798
#> x8 0.354731631 0.77150802 0.010355218 0.016828080
#> x9 0.012122296 0.01035522 0.843854435 0.911435130
#> x10 0.007367798 0.01682808 0.911435130 0.986159101
#>
#> $factors
#> [1] 3
#>
#> $method
#> [1] "pfa"
#>
#> $scores
#> NULL
#>
#> $scoringCoef
#> NULL
#>
#> $meanF
#> NULL
#>
#> $corF
#> NULL
#>
#> $scoresMethod
#> [1] "none"
#>
#> $n.obs
#> NULL
#>
#> $center
#> x1 x2 x3 x4 x5 x6
#> 1.679190e+09 3.252545e+08 1.439706e+08 1.071863e+08 1.904953e-01 2.906124e+00
#> x7 x8 x9 x10
#> 6.366363e+00 3.265233e+00 3.012056e+09 5.546872e+08
#>
#> $eigenvalues
#> [1] 5.7900498488 2.3189552681 1.0086871619 0.5741391921 0.1432389680
#> [6] 0.0991672355 0.0516167794 0.0094032935 0.0039695244 0.0007727282
#>
#> attr(,"class")
#> [1] "factorScorePfa"
## fsPfa3 contains scores etc.
fsPfa3 = factorScorePfa(x = stock611[,3:12], factors = 2,
cor = TRUE, rotation = "varimax", scoresMethod = "regression"); fsPfa3
#> $call
#> factorScorePfa(x = stock611[, 3:12], factors = 2, cor = TRUE,
#> rotation = "varimax", scoresMethod = "regression")
#>
#> $loadings
#>
#> Loadings:
#> Factor1 Factor2
#> X1 0.987
#> X2 0.992
#> X3 0.994
#> X4 0.983
#> X5 1.131
#> X6 0.453
#> X7 0.312
#> X8 0.757
#> X9 0.918
#> X10 0.992
#>
#> Factor1 Factor2
#> SS loadings 5.745 2.168
#> Proportion Var 0.575 0.217
#> Cumulative Var 0.575 0.791
#>
#> $communality
#> X1 X2 X3 X4 X5 X6 X7
#> 0.97483545 0.98498499 0.99101663 0.97409584 1.28243672 0.20526961 0.09797297
#> X8 X9 X10
#> 0.57415573 0.84318885 0.98555970
#>
#> $uniquenesses
#> x1 x2 x3 x4 x5 x6
#> 0.025164549 0.015015010 0.008983373 0.025904159 -0.282436723 0.794730389
#> x7 x8 x9 x10
#> 0.902027030 0.425844269 0.156811145 0.014440298
#>
#> $covariance
#> x1 x2 x3 x4 x5
#> x1 1.555244e+20 2.863371e+19 1.110191e+19 7.219175e+18 7.345648e+07
#> x2 2.863371e+19 5.344552e+18 2.085874e+18 1.360040e+18 2.441148e+07
#> x3 1.110191e+19 2.085874e+18 8.416758e+17 5.540749e+17 2.333976e+07
#> x4 7.219175e+18 1.360040e+18 5.540749e+17 3.665843e+17 1.893343e+07
#> x5 7.345648e+07 2.441148e+07 2.333976e+07 1.893343e+07 5.479330e-02
#> x6 -2.598053e+08 -1.594810e+07 2.466455e+07 2.448491e+07 1.861453e-01
#> x7 3.466949e+09 9.308800e+08 9.199586e+08 7.623881e+08 2.175023e+00
#> x8 1.107546e+09 4.335511e+08 4.366522e+08 3.664572e+08 1.018644e+00
#> x9 1.863773e+20 3.429496e+19 1.388140e+19 9.109861e+18 1.606871e+08
#> x10 4.420202e+19 8.195339e+18 3.205298e+18 2.090804e+18 1.521776e+06
#> x6 x7 x8 x9 x10
#> x1 -2.598053e+08 3.466949e+09 1.107546e+09 1.863773e+20 4.420202e+19
#> x2 -1.594810e+07 9.308800e+08 4.335511e+08 3.429496e+19 8.195339e+18
#> x3 2.466455e+07 9.199586e+08 4.366522e+08 1.388140e+19 3.205298e+18
#> x4 2.448491e+07 7.623881e+08 3.664572e+08 9.109861e+18 2.090804e+18
#> x5 1.861453e-01 2.175023e+00 1.018644e+00 1.606871e+08 1.521776e+06
#> x6 1.907749e+00 5.493923e-01 2.134298e+00 -2.146439e+08 -2.310185e+08
#> x7 5.493923e-01 8.079963e+02 5.219995e+01 5.688682e+09 7.502250e+08
#> x8 2.134298e+00 5.219995e+01 2.679976e+01 8.850072e+08 3.120680e+08
#> x9 -2.146439e+08 5.688682e+09 8.850072e+08 2.725487e+20 5.390102e+19
#> x10 -2.310185e+08 7.502250e+08 3.120680e+08 5.390102e+19 1.283212e+19
#>
#> $correlation
#> x1 x2 x3 x4 x5 x6
#> x1 1.000000000 0.993167953 0.97034436 0.95609585 0.025163276 -0.015083014
#> x2 0.993167953 1.000000000 0.98346739 0.97164822 0.045110192 -0.004994507
#> x3 0.970344358 0.983467387 1.00000000 0.99749137 0.108682670 0.019464360
#> x4 0.956095848 0.971648219 0.99749137 1.00000000 0.133591430 0.029278642
#> x5 0.025163276 0.045110192 0.10868267 0.13359143 1.000000000 0.575741829
#> x6 -0.015083014 -0.004994507 0.01946436 0.02927864 0.575741829 1.000000000
#> x7 0.009780105 0.014165548 0.03527696 0.04429804 0.326885328 0.013993207
#> x8 0.017155253 0.036225902 0.09193857 0.11691517 0.840607798 0.298489402
#> x9 0.905256744 0.898571356 0.91651307 0.91138685 0.041581017 -0.009413168
#> x10 0.989449715 0.989605604 0.97531937 0.96400047 0.001814838 -0.046691411
#> x7 x8 x9 x10
#> x1 0.009780105 0.01715525 0.905256744 0.989449715
#> x2 0.014165548 0.03622590 0.898571356 0.989605604
#> x3 0.035276960 0.09193857 0.916513072 0.975319373
#> x4 0.044298039 0.11691517 0.911386851 0.964000474
#> x5 0.326885328 0.84060780 0.041581017 0.001814838
#> x6 0.013993207 0.29848940 -0.009413168 -0.046691411
#> x7 1.000000000 0.35473163 0.012122296 0.007367798
#> x8 0.354731631 1.00000000 0.010355218 0.016828080
#> x9 0.012122296 0.01035522 1.000000000 0.911435130
#> x10 0.007367798 0.01682808 0.911435130 1.000000000
#>
#> $usedMatrix
#> x1 x2 x3 x4 x5 x6
#> x1 1.000000000 0.993167953 0.97034436 0.95609585 0.025163276 -0.015083014
#> x2 0.993167953 1.000000000 0.98346739 0.97164822 0.045110192 -0.004994507
#> x3 0.970344358 0.983467387 1.00000000 0.99749137 0.108682670 0.019464360
#> x4 0.956095848 0.971648219 0.99749137 1.00000000 0.133591430 0.029278642
#> x5 0.025163276 0.045110192 0.10868267 0.13359143 1.000000000 0.575741829
#> x6 -0.015083014 -0.004994507 0.01946436 0.02927864 0.575741829 1.000000000
#> x7 0.009780105 0.014165548 0.03527696 0.04429804 0.326885328 0.013993207
#> x8 0.017155253 0.036225902 0.09193857 0.11691517 0.840607798 0.298489402
#> x9 0.905256744 0.898571356 0.91651307 0.91138685 0.041581017 -0.009413168
#> x10 0.989449715 0.989605604 0.97531937 0.96400047 0.001814838 -0.046691411
#> x7 x8 x9 x10
#> x1 0.009780105 0.01715525 0.905256744 0.989449715
#> x2 0.014165548 0.03622590 0.898571356 0.989605604
#> x3 0.035276960 0.09193857 0.916513072 0.975319373
#> x4 0.044298039 0.11691517 0.911386851 0.964000474
#> x5 0.326885328 0.84060780 0.041581017 0.001814838
#> x6 0.013993207 0.29848940 -0.009413168 -0.046691411
#> x7 1.000000000 0.35473163 0.012122296 0.007367798
#> x8 0.354731631 1.00000000 0.010355218 0.016828080
#> x9 0.012122296 0.01035522 1.000000000 0.911435130
#> x10 0.007367798 0.01682808 0.911435130 1.000000000
#>
#> $reducedCorrelation
#> x1 x2 x3 x4 x5 x6
#> x1 0.974837883 0.993167953 0.97034436 0.95609585 0.025163276 -0.015083014
#> x2 0.993167953 0.984984347 0.98346739 0.97164822 0.045110192 -0.004994507
#> x3 0.970344358 0.983467387 0.99102095 0.99749137 0.108682670 0.019464360
#> x4 0.956095848 0.971648219 0.99749137 0.97410839 0.133591430 0.029278642
#> x5 0.025163276 0.045110192 0.10868267 0.13359143 1.277471757 0.575741829
#> x6 -0.015083014 -0.004994507 0.01946436 0.02927864 0.575741829 0.205466836
#> x7 0.009780105 0.014165548 0.03527696 0.04429804 0.326885328 0.013993207
#> x8 0.017155253 0.036225902 0.09193857 0.11691517 0.840607798 0.298489402
#> x9 0.905256744 0.898571356 0.91651307 0.91138685 0.041581017 -0.009413168
#> x10 0.989449715 0.989605604 0.97531937 0.96400047 0.001814838 -0.046691411
#> x7 x8 x9 x10
#> x1 0.009780105 0.01715525 0.905256744 0.989449715
#> x2 0.014165548 0.03622590 0.898571356 0.989605604
#> x3 0.035276960 0.09193857 0.916513072 0.975319373
#> x4 0.044298039 0.11691517 0.911386851 0.964000474
#> x5 0.326885328 0.84060780 0.041581017 0.001814838
#> x6 0.013993207 0.29848940 -0.009413168 -0.046691411
#> x7 0.098164353 0.35473163 0.012122296 0.007367798
#> x8 0.354731631 0.57603192 0.010355218 0.016828080
#> x9 0.012122296 0.01035522 0.843191079 0.911435130
#> x10 0.007367798 0.01682808 0.911435130 0.985564105
#>
#> $factors
#> [1] 2
#>
#> $method
#> [1] "pfa"
#>
#> $scores
#> Factor1 Factor2
#> 1 -0.106792384 -0.7235262275
#> 2 -0.129543790 1.7540249047
#> 3 -0.185957366 -0.9085174391
#> 4 -0.117112604 -1.7738845047
#> 5 -0.142945564 -1.5322906094
#> 6 -0.129254370 -4.7173034885
#> 7 -0.125484499 -0.8181458299
#> 8 -0.159680012 -5.4611567296
#> 9 -0.120291888 -2.4118547391
#> 10 -0.116724852 -6.1914650063
#> 11 -0.128582277 0.0175848472
#> 12 -0.135314227 -6.1686647194
#> 13 -0.127766372 -1.0093301327
#> 14 -0.132398003 -1.8342255233
#> 15 -0.135960582 -2.3765596775
#> 16 -0.122383712 -0.9699068442
#> 17 -0.139279527 -4.6733697191
#> 18 -0.116235822 -6.7374295033
#> 19 -0.111026362 1.2663702888
#> 20 -0.115609052 -1.1555301737
#> 21 -0.138400158 -3.4200477090
#> 22 -0.121974479 -4.0257998665
#> 23 -0.124375792 -2.5486745508
#> 24 -0.169579548 -2.2140323848
#> 25 -0.146102694 -4.0408315782
#> 26 -0.111272335 -4.3621509426
#> 27 -0.118206193 -3.4685879605
#> 28 -0.120914468 -1.6511771327
#> 29 -0.119663924 -2.0028644678
#> 30 -0.101710733 -2.0176887131
#> 31 -0.104055212 -1.9973922102
#> 32 -0.140059106 -1.6899285282
#> 33 -0.111984226 -3.8788683285
#> 34 -0.099350324 -1.5744602830
#> 35 -0.075158573 -1.9643378598
#> 36 -0.085495119 -1.5818743147
#> 37 -0.115549999 -2.4999246590
#> 38 -0.109285928 -2.1204116729
#> 39 -0.123625580 -2.8399581201
#> 40 -0.083527486 -2.1181221325
#> 41 -0.122431235 -0.7806133640
#> 42 -0.117318874 -2.3998585852
#> 43 -0.126522668 -0.4069132698
#> 44 0.004181504 -0.8240013312
#> 45 -0.127841687 -1.0482477124
#> 46 -0.154590671 -1.0311549003
#> 47 -0.107756972 -0.7581043729
#> 48 -0.108302945 -0.5391470656
#> 49 -0.113842684 -1.8775324640
#> 50 -0.114891272 -0.7422763230
#> 51 -0.108509649 -0.5682489532
#> 52 -0.121775592 -0.6362957180
#> 53 -0.110449419 -1.1562880815
#> 54 -0.104402020 -1.2436433426
#> 55 -0.106469517 -0.8418712964
#> 56 0.081886507 -0.7356547146
#> 57 -0.066362067 -0.2958614201
#> 58 -0.122708148 -1.1613864453
#> 59 0.508499126 0.8805356736
#> 60 -0.120894823 -0.6398031161
#> 61 -0.111154532 -0.5184594530
#> 62 -0.054075741 -0.6767324183
#> 63 -0.071815085 -0.4410627265
#> 64 -0.111378582 -0.7995555512
#> 65 -0.118716516 -0.9448062808
#> 66 -0.196417018 -0.9969556865
#> 67 -0.117590149 -0.7237045335
#> 68 -0.129736729 -0.7205912413
#> 69 -0.091832448 -1.9514457367
#> 70 -0.068176729 -1.2614531161
#> 71 -0.113331684 -0.4389830997
#> 72 -0.100117784 -1.1320854138
#> 73 -0.128148175 -0.0984283709
#> 74 0.086032675 0.5673735891
#> 75 0.023720738 -2.2215735357
#> 76 -0.123070182 -0.6398622757
#> 77 -0.108480332 -1.2568127809
#> 78 -0.128130123 -0.4353268346
#> 79 1.024200494 0.4655085076
#> 80 -0.088096014 -1.6533161322
#> 81 -0.010923867 -0.9465751013
#> 82 -0.130412403 -0.2093952465
#> 83 -0.056423899 -0.6345118384
#> 84 -0.089432859 -0.6669086291
#> 85 -0.128975476 -0.4673238294
#> 86 -0.111207890 -0.6092130520
#> 87 -0.124230600 -1.2804165477
#> 88 -0.124599725 -0.4975076208
#> 89 -0.125378979 -0.3123390581
#> 90 -0.128369593 -0.4447294413
#> 91 -0.113879333 -0.9224554828
#> 92 -0.151969099 2.7723489817
#> 93 -0.106170153 -1.0694680445
#> 94 -0.126347126 -0.1434736985
#> 95 -0.117456342 -0.7312043564
#> 96 -0.104129892 -0.0476003872
#> 97 0.036358357 -2.1905359280
#> 98 -0.122304951 -0.5166212934
#> 99 -0.126537595 -0.4788364059
#> 100 -0.075538846 -0.5548726437
#> 101 -0.101472255 -0.5642440466
#> 102 -0.083713952 -0.3678541961
#> 103 -0.039449620 -0.5987372576
#> 104 -0.102217083 -0.0718113351
#> 105 -0.108961555 -0.3762842436
#> 106 -0.112115338 -1.0429435950
#> 107 -0.115628843 -0.4745283909
#> 108 -0.034589539 0.0810353677
#> 109 -0.114411447 -0.8458110364
#> 110 -0.077988712 -1.0286683200
#> 111 -0.081679063 -0.6851636176
#> 112 -0.097741177 -0.8681569992
#> 113 0.652910026 1.2466099712
#> 114 -0.086237891 -0.6557991057
#> 115 -0.123932143 -0.2175359171
#> 116 -0.096979424 -0.9035179708
#> 117 -0.049214648 0.5205754957
#> 118 -0.110814882 -0.9646239623
#> 119 -0.115983380 -0.6557278007
#> 120 -0.102819041 -1.0360191679
#> 121 -0.110583761 0.0175650261
#> 122 -0.107250070 -0.6499942755
#> 123 -0.106585802 -0.8377568366
#> 124 -0.094961482 -0.4669685955
#> 125 -0.128148449 0.1674167150
#> 126 -0.120945857 -0.5150467738
#> 127 -0.107504751 0.0053232806
#> 128 -0.127300306 -0.3883270311
#> 129 0.371260667 0.1580877918
#> 130 -0.095713548 -0.7720565385
#> 131 -0.079142005 -1.4520795745
#> 132 -0.080259977 -0.5919088205
#> 133 -0.109914642 1.0355713167
#> 134 -0.094861150 -0.3051813764
#> 135 -0.116428266 -0.5090440424
#> 136 -0.079871010 -0.1122676985
#> 137 -0.110505381 -0.6903056130
#> 138 -0.038624866 0.0837120096
#> 139 -0.074002988 -0.7108450238
#> 140 -0.020003313 -0.1989234055
#> 141 -0.069891545 -0.2760792284
#> 142 -0.104762416 -0.6064719344
#> 143 -0.079430338 -0.3500538351
#> 144 -0.008161034 -0.8901555880
#> 145 -0.109468588 -0.4551860956
#> 146 -0.104663807 -0.4652981864
#> 147 -0.117935188 -0.3876362567
#> 148 -0.060465156 -0.3968552434
#> 149 -0.064365820 -0.7685257130
#> 150 -0.036626841 0.3146830110
#> 151 -0.114954939 -1.1305419305
#> 152 -0.092529990 -0.2872141844
#> 153 0.119028108 -0.0425695759
#> 154 -0.094887401 -0.9988432287
#> 155 -0.082674937 -0.8852047140
#> 156 -0.043527377 -0.3853444746
#> 157 -0.090598574 -0.4354878999
#> 158 -0.074336295 -1.3086305654
#> 159 -0.104963836 -0.3893918213
#> 160 -0.066497342 1.1928350480
#> 161 -0.099773696 -0.1756411178
#> 162 -0.078112097 -0.4883766825
#> 163 -0.098369581 -0.9080327283
#> 164 -0.074974136 -0.8320881615
#> 165 -0.107278964 -0.4040220263
#> 166 0.045385022 -0.0972034082
#> 167 -0.090119493 -0.2484257973
#> 168 -0.097204725 -0.5559184438
#> 169 -0.017071487 -0.5080260312
#> 170 -0.110908714 -0.1551656614
#> 171 -0.067695704 -0.6204838894
#> 172 -0.093889591 -0.5339891607
#> 173 -0.003880229 -0.4709415389
#> 174 -0.098672512 0.1754239742
#> 175 0.042662237 0.1246663071
#> 176 -0.028481626 -0.5197203608
#> 177 -0.101520645 -0.3759484274
#> 178 -0.116957032 -0.8254006284
#> 179 -0.099930444 -0.7993088147
#> 180 -0.089042804 -0.6359235392
#> 181 -0.104500947 -0.1109889282
#> 182 -0.057722443 0.3545102265
#> 183 -0.117933620 -0.6920499779
#> 184 -0.024430329 -0.2004750972
#> 185 -0.081411631 -0.6347130318
#> 186 -0.114965082 -0.1345080171
#> 187 -0.113525113 -0.7052189813
#> 188 -0.126125388 -0.4452813868
#> 189 -0.071766652 -0.6065890463
#> 190 -0.051896717 -0.0078813830
#> 191 -0.099249726 -0.3406934666
#> 192 -0.083284159 0.0251241454
#> 193 -0.263500833 -0.5161240011
#> 194 -0.101856099 1.1226289040
#> 195 -0.092907987 0.1368425634
#> 196 -0.122006183 -0.4072851959
#> 197 -0.101548074 -0.6909553917
#> 198 -0.084013830 0.4752387572
#> 199 -0.078072966 -1.0105358631
#> 200 -0.075052698 -0.3798132894
#> 201 -0.094309012 -0.1697002860
#> 202 -0.103120823 -0.5655672765
#> 203 -0.121687916 1.6173864486
#> 204 -0.068383131 -0.4556258495
#> 205 -0.083714013 -0.5901960582
#> 206 0.235678078 0.7225571463
#> 207 -0.057492429 -0.3822528155
#> 208 -0.079075554 -0.7256210465
#> 209 -0.077253807 -1.0421440494
#> 210 -0.101565340 -0.8896935912
#> 211 -0.096971681 0.1609691810
#> 212 -0.120365892 -0.6227326839
#> 213 -0.153602042 0.1647070090
#> 214 -0.068008226 0.2456055929
#> 215 -0.120452518 -0.5676072945
#> 216 -0.039549409 -0.1326781202
#> 217 -0.065319035 -0.5310291129
#> 218 -0.117730885 0.0171913665
#> 219 -0.091239246 -0.4149149230
#> 220 -0.060080170 0.3139424538
#> 221 -0.053385983 -0.2409343597
#> 222 -0.056098814 0.2546289268
#> 223 -0.114632403 -0.5735071767
#> 224 -0.083283482 -0.7495950390
#> 225 -0.053174355 -0.0479768567
#> 226 0.250038844 -0.1087574672
#> 227 -0.109336239 -0.3265877521
#> 228 -0.097434816 0.1552136819
#> 229 -0.097455589 -0.1895561764
#> 230 0.071813746 0.2250599540
#> 231 -0.100578210 -0.4222642618
#> 232 -0.077331230 0.1268101634
#> 233 -0.070413422 0.0764101236
#> 234 -0.092740862 -0.5313432225
#> 235 -0.088812888 0.1050200590
#> 236 -0.117107452 -0.0088842381
#> 237 0.272792068 0.0805516930
#> 238 -0.078735170 0.7264678237
#> 239 -0.109366029 -0.3379392064
#> 240 -0.075430344 0.0116177953
#> 241 -0.093657626 -0.7270894551
#> 242 -0.089237538 -0.5243400875
#> 243 -0.068878768 -0.4588361627
#> 244 -0.108975110 -0.4564136984
#> 245 -0.058135192 1.2082972125
#> 246 -0.078476761 -0.8463640608
#> 247 -0.091556337 -0.2195102944
#> 248 -0.121963767 -0.5315317188
#> 249 -0.111905968 -0.3786523815
#> 250 -0.115637565 -0.2286425294
#> 251 -0.124435483 -0.2052312899
#> 252 -0.089479476 -1.2810850617
#> 253 0.032906395 0.7513146715
#> 254 -0.042823330 -0.4689733199
#> 255 -0.103708967 -0.6109340790
#> 256 -0.115145927 -0.7610099009
#> 257 -0.073304976 -0.2021154770
#> 258 -0.101915843 -0.7733875844
#> 259 -0.117795167 0.9091288929
#> 260 -0.083326678 0.0076975040
#> 261 -0.097209100 1.6552092274
#> 262 -0.123601951 -0.1296352438
#> 263 -0.113472990 0.3267880560
#> 264 -0.128068693 -0.0702272896
#> 265 -0.092038523 -0.7281880796
#> 266 -0.037504856 -0.4864751835
#> 267 -0.071618705 0.0695186779
#> 268 -0.093195337 -0.4235545623
#> 269 -0.089980722 0.2525859517
#> 270 -0.119468981 0.1311428944
#> 271 -0.108731091 0.5363774733
#> 272 -0.009737256 0.1156882514
#> 273 -0.136732989 -0.3976941776
#> 274 -0.116135774 -0.2332724115
#> 275 -0.088576805 -0.6178150289
#> 276 -0.015669724 -0.1328099035
#> 277 -0.107003222 -0.4781409659
#> 278 -0.038806652 -0.2957156171
#> 279 -0.093213244 -0.3881278709
#> 280 -0.109617401 -0.1616466850
#> 281 -0.098163738 -0.1381540446
#> 282 -0.068989091 -0.6412435868
#> 283 -0.049676669 -0.0956042412
#> 284 -0.121291580 0.3905171502
#> 285 0.757907393 1.2611052893
#> 286 -0.103084277 0.0903590304
#> 287 -0.072203032 1.3674891640
#> 288 0.177564931 0.4150784447
#> 289 -0.044124261 0.9803975384
#> 290 -0.030626363 0.1254083251
#> 291 -0.088741045 -0.3082401734
#> 292 -0.099208503 -0.4893148950
#> 293 -0.078635475 0.2527926724
#> 294 -0.111977749 -0.5081495623
#> 295 -0.006282782 1.7166286361
#> 296 -0.099626462 -0.3814995458
#> 297 -0.065274406 -0.5511023501
#> 298 -0.105624779 -0.1554891435
#> 299 -0.112304427 0.2590058393
#> 300 -0.028626874 0.2203736295
#> 301 -0.086870740 -0.3847415667
#> 302 -0.068042910 0.6275290372
#> 303 0.383301432 -0.0486339440
#> 304 -0.073821681 -0.5706717307
#> 305 -0.111517434 1.0122832583
#> 306 -0.046334068 -0.0645248135
#> 307 0.102633052 0.3548445713
#> 308 -0.077133525 0.6068879851
#> 309 -0.005343290 -0.2612126366
#> 310 -0.092216242 -0.1957907365
#> 311 -0.090984656 0.4915136977
#> 312 -0.098454735 -0.2765945500
#> 313 -0.091224384 -0.5224094567
#> 314 0.015981781 0.1489770840
#> 315 -0.087508200 0.6232588067
#> 316 -0.106932156 -0.4930172899
#> 317 -0.086916110 -0.3098020689
#> 318 -0.086535416 -0.5395928598
#> 319 -0.063846999 1.0887404640
#> 320 -0.156507381 1.6377917002
#> 321 0.158736488 1.0737334336
#> 322 -0.030965655 -0.3013767867
#> 323 -0.060236038 0.5406289927
#> 324 -0.078576295 0.5622170170
#> 325 -0.102357160 -0.1089742728
#> 326 -0.004676693 -0.1526132553
#> 327 -0.074902217 0.2446924167
#> 328 -0.087199096 0.4034527792
#> 329 -0.074098981 0.5809777504
#> 330 -0.087030710 -0.0787027327
#> 331 -0.096566768 1.1936927279
#> 332 0.146106187 0.0003323595
#> 333 -0.137561250 1.1966464617
#> 334 -0.105081727 -0.3581616388
#> 335 -0.084765183 -0.2975782092
#> 336 -0.104590211 -0.6222593403
#> 337 -0.047561856 -2.4954796202
#> 338 23.992014079 -1.6074642119
#> 339 0.127020477 1.3226721843
#> 340 -0.058645304 -0.0897676307
#> 341 -0.066506589 1.2054841304
#> 342 -0.105623459 0.4785729576
#> 343 -0.017959314 1.3193364116
#> 344 -0.001855643 1.7802845062
#> 345 0.105359995 -0.3939748461
#> 346 -0.079171092 -0.4032741516
#> 347 -0.109593349 0.1024498922
#> 348 -0.067362701 -0.7021035138
#> 349 -0.101783445 -0.1617990785
#> 350 -0.032115206 0.2262691074
#> 351 -0.214873790 0.6475067564
#> 352 -0.026200137 -0.2221880792
#> 353 -0.080810973 -0.2049423059
#> 354 -0.070240978 1.1328891882
#> 355 -0.117342719 -0.2118720949
#> 356 -0.004183492 0.3064518752
#> 357 0.002221725 0.9055918985
#> 358 -0.107617695 0.5774473072
#> 359 -0.119768798 0.2428901002
#> 360 -0.094430218 -0.5751001838
#> 361 -0.079900788 0.2632808870
#> 362 -0.083844618 -0.4554455212
#> 363 -0.111050115 0.3135534537
#> 364 -0.083001785 0.2331153364
#> 365 -0.060783305 1.0439349904
#> 366 0.015962617 0.5295310146
#> 367 -0.110075165 -0.0639226458
#> 368 -0.083302459 -0.0440826400
#> 369 -0.112030943 0.1496468135
#> 370 -0.097045529 0.7779421534
#> 371 -0.020586359 1.6440399176
#> 372 -0.093836859 -0.2371256497
#> 373 -0.040161932 -0.4114716931
#> 374 -0.038884654 2.0262610805
#> 375 -0.031216151 -0.2354214160
#> 376 -0.117899888 0.4508903966
#> 377 0.203444455 1.0555619243
#> 378 -0.146965382 1.5851681409
#> 379 3.958575172 0.3558836866
#> 380 -0.063693677 -0.3089129884
#> 381 -0.094088154 0.1531344878
#> 382 -0.131785883 0.4737237062
#> 383 -0.053997656 -0.5293761961
#> 384 0.020096994 -0.0953099255
#> 385 -0.081928138 -0.2732320497
#> 386 -0.122062584 -0.0892595418
#> 387 -0.070062861 1.1383397250
#> 388 -0.113168321 -0.5284974822
#> 389 -0.042417582 -0.4660892382
#> 390 -0.106525923 -0.1931904234
#> 391 0.048754058 0.1076370984
#> 392 -0.127953500 -0.2451043991
#> 393 -0.126613140 0.1831445421
#> 394 -0.043239571 0.7133881949
#> 395 -0.095979308 0.5382167535
#> 396 -0.105579651 0.8675362304
#> 397 -0.107665948 1.0712953851
#> 398 -0.069063865 -0.4456862119
#> 399 -0.063108946 1.1323713162
#> 400 -0.075027926 -0.3261578403
#> 401 -0.139704008 -0.0679752690
#> 402 -0.094523900 0.1373397338
#> 403 -0.110528126 -0.1020777875
#> 404 -0.109791120 -0.3434264178
#> 405 -0.108856658 0.0756755818
#> 406 -0.009014597 -0.2420354700
#> 407 -0.102663939 0.9536981293
#> 408 -0.098676156 0.3456109286
#> 409 -0.102559751 1.2163461578
#> 410 -0.124421749 0.8442240251
#> 411 -0.110087384 -0.0712493549
#> 412 -0.066674406 0.0787299031
#> 413 0.250819625 1.8692386887
#> 414 -0.087587553 -0.0809400085
#> 415 0.173940841 0.1674618366
#> 416 -0.095249866 0.2977214550
#> 417 -0.024877141 0.5410435905
#> 418 0.021100817 0.2718868889
#> 419 -0.137848051 0.0242130164
#> 420 -0.038673041 -0.3983528596
#> 421 -0.114335651 -0.2886448883
#> 422 -0.068371940 -0.2652227775
#> 423 -0.103780625 0.6613512799
#> 424 -0.153686418 2.0253516605
#> 425 -0.069239330 -0.4905068132
#> 426 -0.056476113 0.0506273118
#> 427 -0.109204666 0.0736398474
#> 428 -0.111733089 0.7243622271
#> 429 -0.112631496 0.0218746181
#> 430 -0.092355949 -0.0965365985
#> 431 -0.107123652 -0.5838203824
#> 432 -0.098075803 -0.1360729587
#> 433 -0.077402053 -0.4378542940
#> 434 -0.080299450 -0.2943795728
#> 435 -0.099056242 2.6659777885
#> 436 -0.069511312 0.3972939982
#> 437 0.048867765 1.3545082035
#> 438 -0.074649101 -0.1575376776
#> 439 -0.086540500 -0.4019658367
#> 440 -0.091750677 0.2106649343
#> 441 -0.089665454 -0.0583898367
#> 442 -0.099870554 2.9273373064
#> 443 -0.121103819 0.0158083801
#> 444 0.818889399 0.5916351978
#> 445 -0.065090163 0.0678212844
#> 446 -0.068844107 0.0127613056
#> 447 -0.040281807 0.3942251449
#> 448 0.060789782 1.4445249664
#> 449 0.094614146 -0.3492436830
#> 450 -0.120805702 0.1886116294
#> 451 -0.057210410 1.4941555604
#> 452 -0.077078865 -0.3269983082
#> 453 -0.074494385 -0.2423112290
#> 454 -0.065820887 2.5526661550
#> 455 -0.095979664 0.7720604788
#> 456 -0.104797118 -0.5053567767
#> 457 -0.122113183 -0.5380107453
#> 458 -0.066058065 -0.4025751077
#> 459 -0.119633939 1.9772482207
#> 460 -0.090883473 0.6928225682
#> 461 -0.097982826 -0.3273167184
#> 462 -0.040796779 0.1793446268
#> 463 -0.151595652 0.6775708499
#> 464 -0.069273033 0.3190250599
#> 465 -0.069045162 0.0437041909
#> 466 -0.117439321 1.5403807572
#> 467 0.074367856 0.9081667114
#> 468 -0.081843459 0.8777322719
#> 469 -0.090189698 0.8980551119
#> 470 -0.086993597 -0.1110708776
#> 471 -0.133642740 1.9021822988
#> 472 0.115717926 0.2466887346
#> 473 -0.092993850 1.1131959708
#> 474 -0.092020463 0.5330179767
#> 475 -0.083129614 0.7206333109
#> 476 -0.127115232 1.0640956876
#> 477 -0.135057382 0.7291399086
#> 478 0.026830774 0.5691487618
#> 479 0.117340084 0.7926572599
#> 480 -0.081151969 0.6146598882
#> 481 -0.128931230 0.1839571626
#> 482 -0.063898545 0.9757912395
#> 483 -0.051520404 0.8937640553
#> 484 -0.120686581 0.2693473321
#> 485 -0.060922365 0.2359207745
#> 486 -0.121463731 1.8431802862
#> 487 -0.084261764 0.4073389765
#> 488 -0.094687100 0.9650064133
#> 489 -0.074546843 1.6678877986
#> 490 0.474695166 3.0518664325
#> 491 -0.085730287 -0.2943604938
#> 492 0.310886577 0.1965954953
#> 493 -0.146262723 0.3574493808
#> 494 -0.045017838 -0.0644953698
#> 495 0.004141519 1.4144292285
#> 496 -0.073419690 -0.5954377765
#> 497 -0.115376418 0.2986804746
#> 498 -0.050527687 0.6598713486
#> 499 -0.076295587 0.7840709059
#> 500 -0.049982825 0.4261623941
#> 501 -0.117985143 -0.1114176208
#> 502 -0.064471837 0.6391516173
#> 503 0.041619277 1.8576380529
#> 504 0.271310359 0.0005709786
#> 505 -0.102702814 -0.6172214167
#> 506 -0.120595005 1.9790152872
#> 507 -0.118252058 -0.6952153918
#> 508 -0.083173126 1.8479879447
#> 509 -0.075012434 0.2918913936
#> 510 -0.012441281 0.5272462084
#> 511 -0.098880655 0.1430594206
#> 512 -0.086339713 -0.2674353456
#> 513 -0.034964856 0.4156301411
#> 514 -0.062783994 -0.3321780568
#> 515 -0.093733139 0.6561139440
#> 516 -0.068351112 0.3810086481
#> 517 -0.087616592 0.6398513244
#> 518 0.001239067 1.3494776274
#> 519 -0.080668407 3.3646270618
#> 520 -0.067418881 1.2146488143
#> 521 -0.089478435 1.3006526941
#> 522 -0.096383473 0.4911360593
#> 523 -0.067733407 3.7424642074
#> 524 -0.121688488 0.9242171251
#> 525 -0.044134880 -0.3679240090
#> 526 -0.180444998 4.5263174458
#> 527 -0.056633667 -0.1827883334
#> 528 -0.077117318 0.7822498206
#> 529 -0.078726816 0.5508459020
#> 530 -0.062619470 0.2905485472
#> 531 0.018996596 1.0112122005
#> 532 -0.060708331 1.3712903956
#> 533 -0.106159727 -0.3974500830
#> 534 -0.081264469 0.1723049469
#> 535 -0.094835272 0.4648353300
#> 536 -0.078380853 0.3425499588
#> 537 -0.063757321 -0.2109208848
#> 538 -0.047784891 0.3676324033
#> 539 2.474780775 0.6304450740
#> 540 0.357447840 0.8661899001
#> 541 0.604941110 0.1956551980
#> 542 0.342281906 0.3823534773
#> 543 -0.125672291 -0.0991708374
#> 544 -0.073247640 0.8405290955
#> 545 -0.053703162 0.6450095947
#> 546 0.030630222 1.0039106697
#> 547 -0.038106625 2.0275912714
#> 548 0.079355834 -0.0797122848
#> 549 -0.024945658 1.2517997361
#> 550 -0.074497972 0.4655908816
#> 551 -0.082119035 0.5333983559
#> 552 0.060171744 0.5652828966
#> 553 0.167194200 1.8270098536
#> 554 0.031891037 1.1292786883
#> 555 -0.070096700 1.6284131305
#> 556 -0.063560949 0.5122348790
#> 557 -0.003210012 3.3008398742
#> 558 -0.060663761 4.0388583608
#> 559 -0.091867158 1.7767677677
#> 560 -0.081730119 -0.0430215941
#> 561 -0.127781244 0.4497442514
#> 562 0.309197384 0.8373744177
#> 563 -0.100389242 2.8068008660
#> 564 -0.073765306 1.5546606177
#> 565 -0.049118677 1.5080537900
#> 566 -0.011274628 0.0392835822
#> 567 -0.030481003 0.4419516188
#> 568 -0.038854943 0.8470799666
#> 569 0.495590300 2.1500511578
#> 570 -0.014528032 0.5481164955
#> 571 0.762255932 0.0797676646
#> 572 0.579219157 2.5450624777
#> 573 -0.078211175 1.6567556966
#> 574 -0.004174512 1.1910342449
#> 575 -0.124848908 1.3678026502
#> 576 1.234422462 3.2967937826
#> 577 -0.074001470 0.9315102903
#> 578 -0.090444274 1.7626872549
#> 579 -0.017598028 2.6231251823
#> 580 0.091312998 1.1590163732
#> 581 0.672964870 0.4947663323
#> 582 -0.044146721 -0.8200086763
#> 583 0.533036253 7.1718041737
#> 584 0.085602272 1.1331520290
#> 585 -0.042755984 0.2475069185
#> 586 -0.099117842 0.0740987473
#> 587 -0.101146686 0.0129776071
#> 588 0.408891103 3.2716598560
#> 589 0.974862182 3.2708419729
#> 590 -0.016024262 2.6160434700
#> 591 -0.104204258 0.0493829633
#> 592 0.052458516 2.6146025707
#> 593 0.460706469 0.1410043073
#> 594 0.174262135 1.7516766644
#> 595 0.125015093 1.6006622466
#> 596 -0.090199497 -0.2900569163
#> 597 -0.092246420 0.2353736950
#> 598 -0.082785744 3.7084703736
#> 599 -0.062012026 -0.1334813583
#> 600 -0.114230994 -1.5174365731
#> 601 -0.051541688 1.0097712189
#> 602 -0.038491237 0.4382067918
#> 603 -0.086059309 0.2201204585
#> 604 0.350393718 -1.2902436828
#> 605 0.064801239 1.4382888905
#> 606 -0.099327744 7.3859949186
#> 607 -0.108939112 -0.6929816617
#> 608 -0.106409291 2.3863414791
#> 609 -0.063042981 -0.3405280769
#> 610 -0.141954509 1.8227776161
#> 611 -0.131970065 1.2242320419
#>
#> $scoringCoef
#> x1 x2 x3 x4 x5 x6
#> Factor1 0.1542090 -0.53298976 2.542032 -1.5150207 -0.0107372 0.008643302
#> Factor2 -0.7468658 0.02288815 -1.195907 0.3787577 2.5796211 -0.613008768
#> x7 x8 x9 x10
#> Factor1 0.002632769 0.001954781 -0.04985304 0.3936587
#> Factor2 -0.093687621 -1.143722733 -0.21967488 1.6608499
#>
#> $meanF
#> Factor1 Factor2
#> 1.669877e-16 -1.898463e-15
#>
#> $corF
#> Factor1 Factor2
#> Factor1 1.0000000 -0.0022317
#> Factor2 -0.0022317 1.0000000
#>
#> $scoresMethod
#> [1] "regression"
#>
#> $n.obs
#> [1] 611
#>
#> $center
#> x1 x2 x3 x4 x5 x6
#> 1.679190e+09 3.252545e+08 1.439706e+08 1.071863e+08 1.904953e-01 2.906124e+00
#> x7 x8 x9 x10
#> 6.366363e+00 3.265233e+00 3.012056e+09 5.546872e+08
#>
#> $eigenvalues
#> [1] 5.7900498488 2.3189552681 1.0086871619 0.5741391921 0.1432389680
#> [6] 0.0991672355 0.0516167794 0.0094032935 0.0039695244 0.0007727282
#>
#> attr(,"class")
#> [1] "factorScorePfa"