Initial Extraction of the Components
Jogglew03NeupsyPCA<-principal(Jogglew03Neupsy,nfactors=21,rotate="none")
principal(r=Jogglew03Neupsy,nfactors=21,rotate="none")
Eigen Values
ev <- eigen(cor(Jogglew03Neupsy))
ev
Scree Plot
ap <- parallel(subject=nrow(Jogglew03Neupsy),var=ncol(Jogglew03Neupsy), rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS)
Rotation to Final Solution
Jogglew03NeupsyPCA.r<-principal(Jogglew03Neupsy,nfactors=4,rotate="varimax",scores=T)
principal(r=Jogglew03Neupsy,nfactors=4,rotate="varimax",scores=T)
## Principal Components Analysis
## Call: principal(r = Jogglew03Neupsy, nfactors = 4, rotate = "varimax",
## scores = T)
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC2 RC3 RC4 RC1 h2 u2
## BARTaccuracy3 0.06 0.38 -0.02 0.27 0.22 0.776
## DSSTefficiency3 0.22 0.19 0.42 0.62 0.64 0.361
## LOTefficiency3 0.19 0.17 0.54 0.38 0.50 0.497
## PVTefficiency3 0.24 0.05 0.59 0.12 0.42 0.576
## AMefficiency3 0.12 0.37 -0.27 0.60 0.59 0.409
## NBACKaccuracy3 0.11 -0.07 0.02 0.29 0.10 0.900
## VOLTefficiency3 -0.02 0.13 0.13 0.76 0.61 0.394
## MPTspeed3 0.01 0.32 0.51 0.36 0.49 0.511
## BVRtot3 -0.37 -0.14 -0.66 -0.06 0.59 0.405
## StroopColors3 0.48 0.14 0.29 0.39 0.48 0.517
## StroopWords3 0.57 0.08 0.19 0.23 0.42 0.583
## StroopMixed3 0.58 0.12 0.20 0.32 0.50 0.503
## CVLtca3 0.41 0.34 0.11 0.24 0.36 0.644
## DigitSpanFwd3 0.69 0.02 -0.05 0.05 0.49 0.512
## DigitSpanBck3 0.80 0.14 0.06 -0.15 0.68 0.316
## FluencyWord3 0.35 0.08 0.20 0.24 0.22 0.776
## TrailsAtestSec3 -0.19 -0.32 -0.44 -0.37 0.47 0.534
## TrailsBtestSec3 -0.19 -0.91 -0.27 -0.03 0.94 0.058
## TrailsBminusA3 -0.18 -0.92 -0.23 0.00 0.93 0.071
## Attention3 0.50 0.23 0.33 0.07 0.42 0.582
## ClockTotal3 -0.03 0.01 0.44 -0.08 0.21 0.794
##
## RC2 RC3 RC4 RC1
## SS loadings 2.97 2.50 2.45 2.36
## Proportion Var 0.14 0.12 0.12 0.11
## Cumulative Var 0.14 0.26 0.38 0.49
## Proportion Explained 0.29 0.24 0.24 0.23
## Cumulative Proportion 0.29 0.53 0.77 1.00
##
## Test of the hypothesis that 4 components are sufficient.
##
## The degrees of freedom for the null model are 210 and the objective function was NaN
## The degrees of freedom for the model are 132 and the objective function was NaN
## The total number of observations was 293 with MLE Chi Square = NaN with prob < NaN
##
## Fit based upon off diagonal values = 0.94