Initial Extraction of the Components
AJData2PCA<-principal(AJData2,nfactors=14,rotate="none")
principal(r=AJData2,nfactors=14,rotate="none")
Eigen Values
ev <- eigen(cor(AJData2))
ev
Scree Plot
ap <- parallel(subject=nrow(AJData2),var=ncol(AJData2), rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS)
Rotation to Final Solution
AJData2PCA.r<-principal(AJData2,nfactors=5,rotate="varimax",scores=T)
principal(r=AJData2,nfactors=5,rotate="varimax",scores=T)
## Principal Components Analysis
## Call: principal(r = AJData2, nfactors = 5, rotate = "varimax", scores = T)
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC1 RC4 RC5 RC3 RC2 h2 u2
## VTCorrectResponses 0.04 -0.02 0.78 0.26 -0.26 0.75 0.25
## VTEfficiencyScore 0.22 0.11 0.88 0.09 0.11 0.85 0.15
## MTSpeedScore 0.54 0.14 0.25 -0.16 0.33 0.51 0.49
## NBCorrectResponses 0.12 -0.06 0.08 0.92 0.05 0.87 0.13
## NBAccuracyScore 0.10 0.07 0.08 0.75 -0.05 0.59 0.41
## AMCorrectResponses -0.16 -0.04 -0.21 0.04 0.88 0.84 0.16
## AMEfficiencyScore -0.02 0.13 0.07 -0.09 0.90 0.84 0.16
## LTCorrectResponses 0.10 0.88 0.01 0.08 0.01 0.80 0.20
## LTEfficiencyScore 0.08 0.92 0.08 0.07 0.13 0.88 0.12
## DTCorrectResponses 0.68 0.42 0.42 0.20 -0.01 0.86 0.14
## DTEfficiencyScore 0.66 0.41 0.42 0.11 0.00 0.79 0.21
## BTAccuracyScore -0.04 0.31 0.21 0.54 -0.10 0.44 0.56
## PTAggregateScore 0.91 -0.06 -0.06 0.15 -0.11 0.86 0.14
## PTEfficiencyScore 0.90 0.01 0.05 0.06 -0.20 0.86 0.14
##
## RC1 RC4 RC5 RC3 RC2
## SS loadings 2.94 2.14 1.91 1.89 1.86
## Proportion Var 0.21 0.15 0.14 0.13 0.13
## Cumulative Var 0.21 0.36 0.50 0.63 0.77
## Proportion Explained 0.27 0.20 0.18 0.18 0.17
## Cumulative Proportion 0.27 0.47 0.65 0.83 1.00
##
## Test of the hypothesis that 5 components are sufficient.
##
## The degrees of freedom for the null model are 91 and the objective function was 9.58
## The degrees of freedom for the model are 31 and the objective function was 3.35
## The total number of observations was 44 with MLE Chi Square = 114.4 with prob < 1.8e-11
##
## Fit based upon off diagonal values = 0.95
Initial Extraction of the Components
AJData1PCA<-principal(AJData1,nfactors=,rotate="none")
principal(r=AJData1,nfactors=35,rotate="none")
Eigen Values
ev <- eigen(cor(AJData1))
ev
Scree Plot
ap <- parallel(subject=nrow(AJData1),var=ncol(AJData1), rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS)
Rotation to Final Solution
AJData1PCA.r<-principal(AJData1,nfactors=5,rotate="varimax",scores=T)
principal(r=AJData1,nfactors=5,rotate="varimax",scores=T)
## Principal Components Analysis
## Call: principal(r = AJData1, nfactors = 5, rotate = "varimax", scores = T)
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC1 RC2 RC3 RC4 RC5 h2 u2
## MOCAdsf 0.57 0.07 0.31 0.07 0.34 0.54 0.46
## MOCAdsb 0.54 -0.13 -0.14 -0.43 0.16 0.54 0.46
## MOCADS 0.76 -0.03 0.13 -0.23 0.35 0.78 0.22
## MOCATTL 0.73 0.53 0.10 0.06 -0.05 0.83 0.17
## MOCAATT 0.83 0.16 -0.02 -0.08 0.03 0.73 0.27
## MOCAVIS 0.38 0.40 0.09 -0.21 -0.35 0.47 0.53
## MOCANAM 0.27 0.08 0.43 -0.14 -0.41 0.45 0.55
## MOCALAN 0.65 -0.06 0.31 0.43 0.06 0.71 0.29
## MOCAABS 0.03 0.64 0.08 0.24 0.20 0.51 0.49
## MOCAORI 0.03 0.12 -0.67 -0.21 -0.05 0.50 0.50
## rMOCATTL 0.70 0.54 0.07 0.04 -0.07 0.80 0.20
## FWRATTL 0.81 0.05 0.19 0.30 0.25 0.84 0.16
## MMSEtemporal 0.12 0.18 -0.08 -0.02 0.53 0.33 0.67
## MMSEspatial 0.11 -0.12 0.28 -0.10 0.74 0.66 0.34
## MMSElang 0.07 0.15 -0.06 0.84 -0.11 0.74 0.26
## MMSEattcal 0.50 -0.14 0.08 -0.09 -0.35 0.41 0.59
## MMSETTL 0.40 0.14 -0.02 0.48 0.15 0.43 0.57
## MMSEATTcalSUM 0.68 0.05 -0.04 -0.05 -0.20 0.51 0.49
## BVRTTTL 0.42 0.28 0.22 0.20 -0.40 0.50 0.50
## PMTTL 0.25 0.20 0.45 0.28 -0.28 0.46 0.54
## TMTAT -0.10 -0.83 0.03 0.01 0.12 0.71 0.29
## TMTBT -0.50 -0.31 -0.26 0.14 0.21 0.48 0.52
## CRCorrect -0.07 0.46 0.38 -0.02 0.17 0.39 0.61
## MWords 0.65 0.05 0.23 0.41 -0.04 0.65 0.35
## AWords 0.72 0.02 0.36 0.24 -0.10 0.72 0.28
## SWords 0.69 0.11 0.31 0.38 -0.10 0.75 0.25
## FrWords 0.12 0.46 0.67 -0.13 0.03 0.70 0.30
## AnWords 0.07 0.71 0.37 0.09 0.12 0.67 0.33
## VgWords 0.05 0.03 0.64 -0.11 0.16 0.46 0.54
## BWDTTL 0.69 0.16 -0.17 0.05 -0.03 0.54 0.46
## FWDTTL 0.70 -0.07 -0.08 0.10 0.03 0.50 0.50
## DSTTL 0.10 0.62 -0.04 0.31 -0.39 0.64 0.36
## MQTTL -0.35 -0.11 -0.35 0.08 -0.01 0.26 0.74
## BNT1 -0.03 0.25 0.59 0.11 -0.26 0.49 0.51
## alphsptt 0.64 0.43 0.04 0.29 -0.07 0.68 0.32
##
## RC1 RC2 RC3 RC4 RC5
## SS loadings 8.57 3.84 3.28 2.39 2.31
## Proportion Var 0.24 0.11 0.09 0.07 0.07
## Cumulative Var 0.24 0.35 0.45 0.52 0.58
## Proportion Explained 0.42 0.19 0.16 0.12 0.11
## Cumulative Proportion 0.42 0.61 0.77 0.89 1.00
##
## Test of the hypothesis that 5 components are sufficient.
##
## The degrees of freedom for the null model are 595 and the objective function was 74.55
## The degrees of freedom for the model are 430 and the objective function was NaN
## The total number of observations was 42 with MLE Chi Square = NaN with prob < NaN
##
## Fit based upon off diagonal values = 0.94
Initial Extraction of the Components
AJDataAbbrPCA<-principal(AJDataAbbr,nfactors=31,rotate="none")
principal(r=AJDataAbbr,nfactors=31,rotate="none")
Eigen Values
ev <- eigen(cor(AJDataAbbr))
ev
Scree Plot
ap <- parallel(subject=nrow(AJDataAbbr),var=ncol(AJDataAbbr), rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS)
Rotation to Final Solution
AJDataAbbrPCA.r<-principal(AJDataAbbr,nfactors=5,rotate="varimax",scores=T)
principal(r=AJDataAbbr,nfactors=5,rotate="varimax",scores=T)
## Principal Components Analysis
## Call: principal(r = AJDataAbbr, nfactors = 5, rotate = "varimax", scores = T)
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC1 RC2 RC3 RC4 RC5 h2 u2
## MOCADS 0.69 -0.26 0.02 0.17 0.07 0.57 0.43
## MOCAATT 0.87 0.00 -0.02 0.13 -0.08 0.78 0.22
## MOCAVIS 0.38 0.42 0.23 -0.11 -0.27 0.46 0.54
## MOCAABS 0.10 0.05 0.65 -0.03 0.12 0.45 0.55
## MOCAORI 0.02 0.07 -0.16 -0.09 -0.55 0.34 0.66
## rMOCATTL 0.76 0.19 0.39 0.06 -0.11 0.78 0.22
## FWRATTL 0.77 -0.11 0.13 0.31 0.23 0.77 0.23
## MMSEtemporal 0.03 -0.13 0.08 0.77 -0.19 0.65 0.35
## MMSEspatial -0.01 -0.56 0.28 0.45 0.09 0.61 0.39
## MMSETTL 0.29 0.18 0.13 0.60 0.28 0.57 0.43
## MMSEATTcalSUM 0.64 0.15 -0.07 0.25 -0.13 0.52 0.48
## BVRTTTL 0.46 0.51 0.11 0.03 0.39 0.64 0.36
## PMTTL 0.26 0.24 0.40 -0.04 0.46 0.50 0.50
## TMTAT -0.16 -0.52 -0.52 -0.09 0.26 0.64 0.36
## TMTBT -0.55 -0.02 -0.43 0.11 -0.02 0.50 0.50
## CRCorrect -0.02 -0.02 0.59 0.02 0.09 0.36 0.64
## AnWords 0.15 0.28 0.68 0.09 0.02 0.57 0.43
## BWDTTL 0.72 0.22 -0.20 0.30 -0.07 0.69 0.31
## FWDTTL 0.76 -0.05 -0.21 -0.09 0.10 0.64 0.36
## DSTTL 0.11 0.81 0.29 0.05 0.03 0.75 0.25
## MQTTL -0.21 -0.12 -0.32 -0.21 0.18 0.24 0.76
## BNT1 -0.03 0.18 0.52 0.18 0.05 0.34 0.66
## alphsptt 0.76 0.18 0.30 -0.18 0.17 0.77 0.23
## VTEfficiencyScore 0.19 0.64 0.25 -0.03 -0.12 0.52 0.48
## MTSpeedScore -0.04 0.56 -0.03 -0.10 0.03 0.33 0.67
## NBAccuracyScore 0.56 0.19 0.13 0.00 -0.28 0.44 0.56
## AMEfficiencyScore -0.22 0.02 0.50 -0.48 -0.02 0.54 0.46
## LTEfficiencyScore -0.14 0.46 0.42 -0.02 -0.23 0.46 0.54
## DTEfficiencyScore -0.07 0.87 0.18 0.19 -0.07 0.83 0.17
## BTAccuracyScore 0.22 0.17 0.18 0.37 -0.58 0.58 0.42
## PTEfficiencyScore 0.13 0.59 0.01 0.44 0.36 0.69 0.31
##
## RC1 RC2 RC3 RC4 RC5
## SS loadings 5.90 4.23 3.41 2.23 1.74
## Proportion Var 0.19 0.14 0.11 0.07 0.06
## Cumulative Var 0.19 0.33 0.44 0.51 0.57
## Proportion Explained 0.34 0.24 0.19 0.13 0.10
## Cumulative Proportion 0.34 0.58 0.77 0.90 1.00
##
## Test of the hypothesis that 5 components are sufficient.
##
## The degrees of freedom for the null model are 465 and the objective function was 25.26
## The degrees of freedom for the model are 320 and the objective function was 12.31
## The total number of observations was 43 with MLE Chi Square = 338.6 with prob < 0.23
##
## Fit based upon off diagonal values = 0.92
Initial Extraction of the Components
ANeupsyJogglePCA<-principal(AJNeupsyJoggle,nfactors=49,rotate="none")
principal(r=AJNeupsyJoggle,nfactors=49,rotate="none")
Eigen Values
ev <- eigen(cor(AJNeupsyJoggle))
ev
Scree Plot
ap <- parallel(subject=nrow(AJNeupsyJoggle),var=ncol(AJNeupsyJoggle), rep=100,cent=.05)
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS)
Rotation to Final Solution
AJNeupsyJogglePCA.r<-principal(AJNeupsyJoggle,nfactors=5,rotate="varimax",scores=T)
principal(r=AJNeupsyJoggle,nfactors=5,rotate="varimax",scores=T)
## Principal Components Analysis
## Call: principal(r = AJNeupsyJoggle, nfactors = 5, rotate = "varimax",
## scores = T)
## Standardized loadings (pattern matrix) based upon correlation matrix
## RC1 RC2 RC4 RC3 RC5 h2 u2
## MOCAdsf 0.62 -0.22 0.18 0.27 -0.04 0.54 0.46
## MOCAdsb 0.26 -0.26 0.35 -0.16 0.19 0.32 0.68
## MOCADS 0.61 -0.33 0.36 0.09 0.10 0.63 0.37
## MOCATTL 0.62 0.21 0.61 0.18 -0.03 0.83 0.17
## MOCAATT 0.64 -0.08 0.56 -0.03 0.15 0.76 0.24
## MOCAVIS 0.27 0.36 0.37 0.12 -0.18 0.39 0.61
## MOCANAM 0.31 0.21 0.06 0.27 -0.04 0.22 0.78
## MOCALAN 0.80 0.01 0.02 0.12 -0.01 0.65 0.35
## MOCAABS 0.09 0.20 0.24 0.34 -0.16 0.25 0.75
## MOCAORI -0.20 0.08 0.32 -0.53 -0.07 0.44 0.56
## rMOCATTL 0.58 0.21 0.61 0.17 -0.05 0.79 0.21
## FWRATTL 0.85 -0.09 0.20 0.13 0.15 0.80 0.20
## MMSEtemporal -0.01 -0.13 0.15 0.16 0.54 0.35 0.65
## MMSEspatial 0.16 -0.48 -0.13 0.34 0.21 0.43 0.57
## MMSElang 0.37 0.49 -0.23 -0.11 -0.31 0.55 0.45
## MMSEattcal 0.38 -0.01 0.13 -0.13 0.36 0.31 0.69
## MMSETTL 0.45 0.22 0.00 0.00 0.47 0.46 0.54
## MMSEATTcalSUM 0.50 0.06 0.40 -0.14 0.27 0.51 0.49
## BVRTTTL 0.45 0.49 0.12 0.14 0.17 0.50 0.50
## PMTTL 0.40 0.28 0.00 0.32 -0.06 0.34 0.66
## TMTAT 0.06 -0.51 -0.57 -0.31 -0.03 0.68 0.32
## TMTBT -0.37 0.03 -0.48 -0.25 0.04 0.43 0.57
## CRCorrect -0.02 0.08 0.07 0.59 -0.05 0.36 0.64
## MWords 0.81 0.17 0.00 0.09 -0.10 0.70 0.30
## AWords 0.82 0.10 0.06 0.20 0.10 0.73 0.27
## SWords 0.82 0.28 0.04 0.17 0.11 0.79 0.21
## FrWords 0.12 0.08 0.25 0.80 0.10 0.72 0.28
## AnWords 0.07 0.31 0.34 0.63 0.05 0.61 0.39
## VgWords 0.13 -0.14 0.00 0.60 -0.09 0.40 0.60
## BWDTTL 0.51 0.10 0.44 -0.15 0.34 0.60 0.40
## FWDTTL 0.62 -0.18 0.29 -0.15 -0.03 0.52 0.48
## DSTTL 0.09 0.79 0.27 0.12 0.06 0.72 0.28
## MQTTL -0.35 -0.13 -0.10 -0.27 0.03 0.23 0.77
## BNT1 0.13 0.31 -0.11 0.55 -0.03 0.43 0.57
## alphsptt 0.64 0.21 0.46 0.08 -0.25 0.73 0.27
## VTCorrectResponses 0.03 0.32 0.52 -0.39 0.31 0.62 0.38
## VTEfficiencyScore -0.07 0.51 0.51 0.12 0.12 0.56 0.44
## MTSpeedScore -0.05 0.44 0.02 0.11 -0.05 0.21 0.79
## NBCorrectResponses 0.33 0.01 0.64 0.02 0.03 0.52 0.48
## NBAccuracyScore 0.28 0.20 0.56 0.00 -0.01 0.43 0.57
## AMCorrectResponses 0.08 -0.19 -0.03 0.03 -0.79 0.66 0.34
## AMEfficiencyScore -0.10 0.11 -0.07 0.32 -0.78 0.73 0.27
## LTCorrectResponses 0.03 0.58 0.12 -0.26 -0.31 0.51 0.49
## LTEfficiencyScore 0.01 0.60 0.03 0.03 -0.26 0.43 0.57
## DTCorrectResponses 0.05 0.86 0.23 0.12 0.18 0.84 0.16
## DTEfficiencyScore -0.04 0.84 0.13 0.16 0.20 0.80 0.20
## BTAccuracyScore 0.02 0.07 0.54 0.09 0.22 0.36 0.64
## PTAggregateScore 0.42 0.59 -0.07 0.04 0.20 0.57 0.43
## PTEfficiencyScore 0.30 0.64 -0.09 0.13 0.37 0.66 0.34
##
## RC1 RC2 RC4 RC3 RC5
## SS loadings 8.49 6.30 4.96 3.82 3.08
## Proportion Var 0.17 0.13 0.10 0.08 0.06
## Cumulative Var 0.17 0.30 0.40 0.48 0.54
## Proportion Explained 0.32 0.24 0.19 0.14 0.12
## Cumulative Proportion 0.32 0.55 0.74 0.88 1.00
##
## Test of the hypothesis that 5 components are sufficient.
##
## The degrees of freedom for the null model are 1176 and the objective function was 222
## The degrees of freedom for the model are 941 and the objective function was 196
## The total number of observations was 42 with MLE Chi Square = 4018 with prob < 0
##
## Fit based upon off diagonal values = 0.91