Initial Extraction of the Components
Jogglew01NeupsyPCA<-principal(Jogglew01Neupsy,nfactors=16,rotate="none")
principal(r=Jogglew01Neupsy,nfactors=16,rotate="none")
Eigen Values
ev <- eigen(cor(Jogglew01Neupsy))
ev
Scree Plot
ap <- parallel(subject=nrow(Jogglew01Neupsy),var=ncol(Jogglew01Neupsy), rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS)
Rotation to Final Solution
Jogglew01NeupsyPCA.r<-principal(Jogglew01Neupsy,nfactors=5,rotate="promax",scores=T)
principal(r=Jogglew01Neupsy,nfactors=5,rotate="promax",scores=T)
## Principal Components Analysis
## Call: principal(r = Jogglew01Neupsy, nfactors = 5, rotate = "promax",
## scores = T)
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 PC3 PC2 PC4 PC5 h2 u2
## BARTaccuracy1 -0.06 0.14 0.37 0.30 0.16 0.35 0.65
## DSSTefficiency1 0.53 0.00 0.47 0.02 -0.04 0.66 0.34
## LOTefficiency1 0.59 0.04 0.22 0.16 -0.08 0.54 0.46
## PVTefficiency1 0.71 -0.05 -0.16 0.02 0.00 0.44 0.56
## AMefficiency1 -0.16 0.24 0.80 -0.15 -0.13 0.65 0.35
## NBACKaccuracy1 -0.15 -0.06 -0.10 -0.20 0.96 0.78 0.22
## VOLTefficiency1 0.20 -0.27 0.69 -0.01 0.01 0.58 0.42
## MPTspeed1 0.64 -0.27 0.34 -0.05 -0.23 0.51 0.49
## BVRtot1 -0.78 -0.03 0.01 -0.10 0.19 0.60 0.40
## CrdRot1 0.68 0.13 -0.04 -0.01 0.06 0.56 0.44
## IdentPicCor1 0.50 0.05 0.18 -0.13 0.26 0.52 0.48
## IdentPicErr1 0.01 -0.07 0.08 -0.80 0.18 0.59 0.41
## CVLtca1 0.24 0.20 0.13 0.24 0.16 0.37 0.63
## DigitSpanFwd1 -0.10 0.66 0.09 0.21 -0.02 0.46 0.54
## DigitSpanBck1 0.01 0.81 -0.04 0.12 0.01 0.68 0.32
## FluencyWord1 0.31 -0.11 -0.10 0.33 0.32 0.42 0.58
## TrailsAtestSec1 -0.62 -0.09 0.03 0.42 -0.11 0.50 0.50
## TrailsBtestSec1 -0.35 -0.34 0.02 0.26 -0.08 0.36 0.64
## Attention1 0.19 0.66 -0.01 -0.18 -0.14 0.56 0.44
## ClockTotal1 0.54 0.08 -0.38 0.31 -0.14 0.45 0.55
##
## PC1 PC3 PC2 PC4 PC5
## SS loadings 4.09 2.02 1.94 1.36 1.18
## Proportion Var 0.20 0.10 0.10 0.07 0.06
## Cumulative Var 0.20 0.31 0.40 0.47 0.53
## Proportion Explained 0.39 0.19 0.18 0.13 0.11
## Cumulative Proportion 0.39 0.58 0.76 0.89 1.00
##
## With component correlations of
## PC1 PC3 PC2 PC4 PC5
## PC1 1.00 0.39 0.34 0.24 0.32
## PC3 0.39 1.00 0.17 0.05 0.14
## PC2 0.34 0.17 1.00 0.12 0.26
## PC4 0.24 0.05 0.12 1.00 0.28
## PC5 0.32 0.14 0.26 0.28 1.00
##
## Test of the hypothesis that 5 components are sufficient.
##
## The degrees of freedom for the null model are 190 and the objective function was 5.2
## The degrees of freedom for the model are 100 and the objective function was 1.57
## The total number of observations was 229 with MLE Chi Square = 340.2 with prob < 6.2e-28
##
## Fit based upon off diagonal values = 0.91