Plot Loadings
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="pa",oblique.scores=TRUE)
plot_loadings(model=model,matrix_type="structure")
plot_loadings(model=model,matrix_type="pattern")
cm<-matrix(c(1,.8,.8,.1,.1,.1,
.8,1,.8,.1,.1,.1,
.8,.8,1,.1,.1,.1,
.1,.1,.1,1,.8,.8,
.1,.1,.1,.8,1,.8,
.1,.1,.1,.8,.8,1),
ncol=6,nrow=6)
df1<-generate_correlation_matrix(cm,nrows=10000)
model1<-psych::fa(df1,nfactors=2,rotate="oblimin",fm="pa",oblique.scores=TRUE)
plot_loadings(model=model1,matrix_type="pattern",base_size=10)
cm<-matrix(c(1,.1,.1,.1,.1,.1,
.1,1,.1,.1,.1,.1,
.1,.1,1,.1,.1,.1,
.1,.1,.1,1,.8,.8,
.1,.1,.1,.8,1,.8,
.1,.1,.1,.8,.8,1),
ncol=6,nrow=6)
df1<-generate_correlation_matrix(cm,nrows=10000)
model2<-psych::fa(df1,nfactors=2,rotate="oblimin",fm="pa",oblique.scores=TRUE)
plot_loadings(model=model2,matrix_type="pattern",base_size=10)
cm<-matrix(c(1,.01,.01,.01,.01,.01,
.01,1,.01,.01,.01,.01,
.01,.01,1,.01,.01,.01,
.01,.01,.01,1,.01,.01,
.01,.01,.01,.01,1,.01,
.01,.01,.01,.01,.01,1),
ncol=6,nrow=6)
df1<-generate_correlation_matrix(cm,nrows=10000)
model3<-psych::fa(df1,nfactors=2,rotate="oblimin",fm="pa",oblique.scores=TRUE)
plot_loadings(model=model3,matrix_type="pattern",base_size=10)
Model Loadings
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="pa",oblique.scores=TRUE)
model_loadings(model=model,cut=NULL,matrix_type="pattern")
## Matrix variable PA1 PA2
## qsec Pattern qsec -0.92
## hp Pattern hp 0.89
## carb Pattern carb 0.85
## vs Pattern vs -0.8
## cyl Pattern cyl
## mpg Pattern mpg
## am Pattern am 0.93
## gear Pattern gear 0.93
## drat Pattern drat 0.78
## wt Pattern wt
## disp Pattern disp
model_loadings(model=model,cut=0.4,matrix_type="structure")
## Matrix variable PA1 PA2
## hp Structure hp 0.93
## cyl Structure cyl 0.85 -0.68
## vs Structure vs -0.83
## qsec Structure qsec -0.82
## carb Structure carb 0.78
## mpg Structure mpg -0.76 0.72
## am Structure am 0.89
## gear Structure gear 0.87
## drat Structure drat 0.83
## wt Structure wt 0.61 -0.81
## disp Structure disp 0.75 -0.77
model_loadings(model=model,cut=0.4,matrix_type="all",sort=FALSE)
## Matrix variable PA1 PA2
## 1 Pattern mpg -0.61 0.55
## 2 Pattern cyl 0.71 -0.48
## 3 Pattern disp 0.58 -0.6
## 4 Pattern hp 0.89
## 5 Pattern drat 0.78
## 6 Pattern wt 0.42 -0.7
## 7 Pattern qsec -0.92
## 8 Pattern vs -0.8
## 9 Pattern am 0.93
## 10 Pattern gear 0.93
## 11 Pattern carb 0.85
## 12 Structure mpg -0.76 0.72
## 13 Structure cyl 0.85 -0.68
## 14 Structure disp 0.75 -0.77
## 15 Structure hp 0.93
## 16 Structure drat 0.83
## 17 Structure wt 0.61 -0.81
## 18 Structure qsec -0.82
## 19 Structure vs -0.83
## 20 Structure am 0.89
## 21 Structure gear 0.87
## 22 Structure carb 0.78
Report EFA
cm<-matrix(c(1,.8,.8,.1,.1,.1,
.8,1,.8,.1,.1,.1,
.8,.8,1,.1,.1,.1,
.1,.1,.1,1,.8,.8,
.1,.1,.1,.8,1,.8,
.1,.1,.1,.8,.8,1),
ncol=6,nrow=6)
df1<-generate_correlation_matrix(cm,nrows=10000)
model<-psych::fa(df1,nfactors=2,rotate="oblimin",fm="pa",oblique.scores=TRUE)
result<-report_efa(model=model,df=df1)
## $correlations
## type X1 X2 X3 X4 X5 X6
## X1 reproduced correlations 8.029710e-01 8.022465e-01 8.026759e-01 8.079004e-02 7.968185e-02 9.059877e-02
## X2 reproduced correlations 8.022465e-01 8.015498e-01 8.019680e-01 8.536400e-02 8.425188e-02 9.514245e-02
## X3 reproduced correlations 8.026759e-01 8.019680e-01 8.023908e-01 8.355765e-02 8.244689e-02 9.334987e-02
## X4 reproduced correlations 8.079004e-02 8.536400e-02 8.355765e-02 8.074858e-01 8.065270e-01 8.047871e-01
## X5 reproduced correlations 7.968185e-02 8.425188e-02 8.244689e-02 8.065270e-01 8.055707e-01 8.038187e-01
## X6 reproduced correlations 9.059877e-02 9.514245e-02 9.334987e-02 8.047871e-01 8.038187e-01 8.022253e-01
## X11 observed correlations 1.000000e+00 8.022812e-01 8.027364e-01 8.144221e-02 7.922614e-02 9.040050e-02
## X21 observed correlations 8.022812e-01 1.000000e+00 8.019934e-01 8.487331e-02 8.420571e-02 9.568204e-02
## X31 observed correlations 8.027364e-01 8.019934e-01 1.000000e+00 8.339506e-02 8.294894e-02 9.300951e-02
## X41 observed correlations 8.144221e-02 8.487331e-02 8.339506e-02 1.000000e+00 8.066169e-01 8.048166e-01
## X51 observed correlations 7.922614e-02 8.420571e-02 8.294894e-02 8.066169e-01 1.000000e+00 8.038186e-01
## X61 observed correlations 9.040050e-02 9.568204e-02 9.300951e-02 8.048166e-01 8.038186e-01 1.000000e+00
## X12 residual correlations 1.970290e-01 3.461689e-05 6.051535e-05 6.521679e-04 -4.557042e-04 -1.982738e-04
## X22 residual correlations 3.461689e-05 1.984502e-01 2.539922e-05 -4.906850e-04 -4.616466e-05 5.395892e-04
## X32 residual correlations 6.051535e-05 2.539922e-05 1.976092e-01 -1.625891e-04 5.020468e-04 -3.403591e-04
## X42 residual correlations 6.521679e-04 -4.906850e-04 -1.625891e-04 1.925142e-01 8.990569e-05 2.950289e-05
## X52 residual correlations -4.557042e-04 -4.616466e-05 5.020468e-04 8.990569e-05 1.944293e-01 -1.330340e-07
## X62 residual correlations -1.982738e-04 5.395892e-04 -3.403591e-04 2.950289e-05 -1.330340e-07 1.977747e-01
##
## $npobs
## X1 X2 X3 X4 X5 X6
## X1 10000 10000 10000 10000 10000 10000
## X2 10000 10000 10000 10000 10000 10000
## X3 10000 10000 10000 10000 10000 10000
## X4 10000 10000 10000 10000 10000 10000
## X5 10000 10000 10000 10000 10000 10000
## X6 10000 10000 10000 10000 10000 10000
##
## $residual_stats
## residual_statistics value critical formula
## 1 Root Mean Squared Residual 0.0003281413 NA sqrt(mean(residuals^2))
## 2 Number of absolute residuals > 0.05 0.0000000000 NA abs(residuals)>0.05
## 3 Proportion of absolute residuals > 0.05 0.0000000000 0.5 numberLargeResiduals/nrow(residuals)
##
## $determinant_test
## determinant above_critical
## 1 0.009986523 TRUE
##
## $bartlett_test
## x_squared[bartlett] df[bartlett] p[bartlett]
## 1 46047.53 15 0
##
## $kmo_test
## Overall_MSA MSA Kaiser_1974
## X1 0.7683298 0.7673630 Good
## X2 0.7683298 0.7688482 Good
## X3 0.7683298 0.7680652 Good
## X4 0.7683298 0.7665333 Good
## X5 0.7683298 0.7678210 Good
## X6 0.7683298 0.7713582 Good
##
## $loadings
## Matrix variable PA1 PA2 type row.names.model.Vaccounted.
## 1 Pattern X4 0.9000000 0.0000000
## 2 Pattern X5 0.9000000 0.0000000
## 3 Pattern X6 0.8900000 0.0100000
## 4 Pattern X1 0.0000000 0.9000000
## 5 Pattern X2 0.0000000 0.9000000
## 6 Pattern X3 0.0000000 0.9000000
## 7 Structure X4 0.9000000 0.0900000
## 8 Structure X5 0.9000000 0.0900000
## 9 Structure X6 0.9000000 0.1000000
## 10 Structure X1 0.0900000 0.9000000
## 11 Structure X2 0.1000000 0.9000000
## 12 Structure X3 0.1000000 0.9000000
## 13 2.4151943 2.4069989 variance accounted SS loadings
## 14 0.4025324 0.4011665 variance accounted Proportion Var
## 15 0.4025324 0.8036989 variance accounted Cumulative Var
## 16 0.5008498 0.4991502 variance accounted Proportion Explained
## 17 0.5008498 1.0000000 variance accounted Cumulative Proportion
##
## $instruction_loading_critical_values
## sample critical_loading
## 1 50 0.75
## 2 60 0.70
## 3 70 0.65
## 4 85 0.60
## 5 100 0.55
## 6 120 0.50
## 7 150 0.45
## 8 200 0.40
## 9 250 0.35
## 10 350 0.30
##
## $weights
## PA1 PA2
## X1 -0.03563484 0.34879769
## X2 -0.03330409 0.34562171
## X3 -0.03429605 0.34741996
## X4 0.35215475 -0.03618723
## X5 0.34840281 -0.03623816
## X6 0.34090284 -0.03065831
