Output EFA model
Examples
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="minres",oblique.scores=TRUE)
report_efa(model=model,df=mtcars,file="efa")
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> [[1]]
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> [[1]]
#>
#> $correlations
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg reproduced correlations 0.852823 -0.8884146808 -0.873769 -0.768821 0.686482 -0.81533 0.44815 0.69572 0.5662638214 0.500723 -0.47287
#> cyl reproduced correlations -0.888415 0.9357702443 0.904939 0.844320 -0.675159 0.82878 -0.54193 -0.76269 -0.5226064376 -0.449493 0.55638
#> disp reproduced correlations -0.873769 0.9049385295 0.897960 0.765333 -0.723941 0.84596 -0.42046 -0.69325 -0.6148474388 -0.550190 0.45162
#> hp reproduced correlations -0.768821 0.8443196149 0.765333 0.876451 -0.450036 0.64811 -0.72112 -0.78744 -0.2262826359 -0.146767 0.69565
#> drat reproduced correlations 0.686482 -0.6751587018 -0.723941 -0.450036 0.708034 -0.73632 0.06876 0.41247 0.7174986941 0.683553 -0.13262
#> wt reproduced correlations -0.815327 0.8287773365 0.845956 0.648112 -0.736318 0.82067 -0.27814 -0.58917 -0.6760709963 -0.623095 0.32441
#> qsec reproduced correlations 0.448148 -0.5419313490 -0.420465 -0.721115 0.068757 -0.27814 0.78392 0.64274 -0.1939562742 -0.263731 -0.71433
#> vs reproduced correlations 0.695716 -0.7626924007 -0.693252 -0.787439 0.412467 -0.58917 0.64274 0.70761 0.2135589724 0.142237 -0.62117
#> am reproduced correlations 0.566264 -0.5226064376 -0.614847 -0.226283 0.717499 -0.67607 -0.19396 0.21356 0.8165109988 0.804660 0.10353
#> gear reproduced correlations 0.500723 -0.4494934108 -0.550190 -0.146767 0.683553 -0.62310 -0.26373 0.14224 0.8046601612 0.800123 0.16989
#> carb reproduced correlations -0.472869 0.5563784693 0.451619 0.695652 -0.132621 0.32441 -0.71433 -0.62117 0.1035312105 0.169885 0.65790
#> mpg1 observed correlations 1.000000 -0.8521619594 -0.847551 -0.776168 0.681172 -0.86766 0.41868 0.66404 0.5998324295 0.480285 -0.55093
#> cyl1 observed correlations -0.852162 1.0000000000 0.902033 0.832447 -0.699938 0.78250 -0.59124 -0.81081 -0.5226070469 -0.492687 0.52699
#> disp1 observed correlations -0.847551 0.9020328721 1.000000 0.790949 -0.710214 0.88798 -0.43370 -0.71042 -0.5912270401 -0.555569 0.39498
#> hp1 observed correlations -0.776168 0.8324474527 0.790949 1.000000 -0.448759 0.65875 -0.70822 -0.72310 -0.2432042572 -0.125704 0.74981
#> drat1 observed correlations 0.681172 -0.6999381138 -0.710214 -0.448759 1.000000 -0.71244 0.09120 0.44028 0.7127111272 0.699610 -0.09079
#> wt1 observed correlations -0.867659 0.7824957945 0.887980 0.658748 -0.712441 1.00000 -0.17472 -0.55492 -0.6924952588 -0.583287 0.42761
#> qsec1 observed correlations 0.418684 -0.5912420738 -0.433698 -0.708223 0.091205 -0.17472 1.00000 0.74454 -0.2298608622 -0.212682 -0.65625
#> vs1 observed correlations 0.664039 -0.8108117961 -0.710416 -0.723097 0.440278 -0.55492 0.74454 1.00000 0.1683451246 0.206023 -0.56961
#> am1 observed correlations 0.599832 -0.5226070469 -0.591227 -0.243204 0.712711 -0.69250 -0.22986 0.16835 1.0000000000 0.794059 0.05753
#> gear1 observed correlations 0.480285 -0.4926865994 -0.555569 -0.125704 0.699610 -0.58329 -0.21268 0.20602 0.7940587603 1.000000 0.27407
#> carb1 observed correlations -0.550925 0.5269882937 0.394977 0.749812 -0.090790 0.42761 -0.65625 -0.56961 0.0575343511 0.274073 1.00000
#> mpg2 residual correlations 0.147177 0.0362527213 0.026218 -0.007347 -0.005310 -0.05233 -0.02946 -0.03168 0.0335686081 -0.020439 -0.07806
#> cyl2 residual correlations 0.036253 0.0642297557 -0.002906 -0.011872 -0.024779 -0.04628 -0.04931 -0.04812 -0.0000006093 -0.043193 -0.02939
#> disp2 residual correlations 0.026218 -0.0029056573 0.102040 0.025615 0.013727 0.04202 -0.01323 -0.01716 0.0236203988 -0.005379 -0.05664
#> hp2 residual correlations -0.007347 -0.0118721621 0.025615 0.123549 0.001277 0.01064 0.01289 0.06434 -0.0169216212 0.021063 0.05416
#> drat2 residual correlations -0.005310 -0.0247794121 0.013727 0.001277 0.291966 0.02388 0.02245 0.02781 -0.0047875668 0.016057 0.04183
#> wt2 residual correlations -0.052333 -0.0462815420 0.042024 0.010635 0.023877 0.17933 0.10343 0.03426 -0.0164242626 0.039808 0.10320
#> qsec2 residual correlations -0.029464 -0.0493107248 -0.013233 0.012892 0.022448 0.10343 0.21608 0.10180 -0.0359045879 0.051048 0.05809
#> vs2 residual correlations -0.031677 -0.0481193954 -0.017164 0.064342 0.027811 0.03426 0.10180 0.29239 -0.0452138479 0.063787 0.05156
#> am2 residual correlations 0.033569 -0.0000006093 0.023620 -0.016922 -0.004788 -0.01642 -0.03590 -0.04521 0.1834890012 -0.010601 -0.04600
#> gear2 residual correlations -0.020439 -0.0431931885 -0.005379 0.021063 0.016057 0.03981 0.05105 0.06379 -0.0106014009 0.199877 0.10419
#> carb2 residual correlations -0.078056 -0.0293901755 -0.056642 0.054160 0.041831 0.10320 0.05809 0.05156 -0.0459968594 0.104188 0.34210
#>
#> $npobs
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg 32 32 32 32 32 32 32 32 32 32 32
#> cyl 32 32 32 32 32 32 32 32 32 32 32
#> disp 32 32 32 32 32 32 32 32 32 32 32
#> hp 32 32 32 32 32 32 32 32 32 32 32
#> drat 32 32 32 32 32 32 32 32 32 32 32
#> wt 32 32 32 32 32 32 32 32 32 32 32
#> qsec 32 32 32 32 32 32 32 32 32 32 32
#> vs 32 32 32 32 32 32 32 32 32 32 32
#> am 32 32 32 32 32 32 32 32 32 32 32
#> gear 32 32 32 32 32 32 32 32 32 32 32
#> carb 32 32 32 32 32 32 32 32 32 32 32
#>
#> $residual_stats
#> residual_statistics value critical formula
#> 1 Root Mean Squared Residual 0.04419 NA sqrt(mean(residuals^2))
#> 2 Number of absolute residuals > 0.05 13.00000 NA abs(residuals)>0.05
#> 3 Proportion of absolute residuals > 0.05 0.23636 0.5 numberLargeResiduals/nrow(residuals)
#>
#> $determinant_test
#> determinant above_critical
#> 1 0.0000002057 FALSE
#>
#> $bartlett_test
#> x_squared[bartlett] df[bartlett] p[bartlett]
#> 1 408 55 0.0000000000000000000000000000000000000000000000000000002227
#>
#> $kmo_test
#> Overall_MSA MSA Kaiser_1974
#> mpg 0.8266 0.9276 NA
#> cyl 0.8266 0.8966 NA
#> disp 0.8266 0.7647 NA
#> hp 0.8266 0.8386 NA
#> drat 0.8266 0.9497 NA
#> wt 0.8266 0.7410 NA
#> qsec 0.8266 0.7401 NA
#> vs 0.8266 0.9067 NA
#> am 0.8266 0.8778 NA
#> gear 0.8266 0.8470 NA
#> carb 0.8266 0.6211 NA
#>
#> $loadings
#> Matrix variable MR1 MR2 type row.names.model.Vaccounted.
#> 1 Pattern qsec -0.9200 -0.3500 <NA> <NA>
#> 2 Pattern hp 0.8900 -0.1200 <NA> <NA>
#> 3 Pattern carb 0.8500 0.2400 <NA> <NA>
#> 4 Pattern vs -0.8000 0.1200 <NA> <NA>
#> 5 Pattern cyl 0.7100 -0.4800 <NA> <NA>
#> 6 Pattern mpg -0.6100 0.5400 <NA> <NA>
#> 7 Pattern am 0.1300 0.9300 <NA> <NA>
#> 8 Pattern gear 0.2200 0.9300 <NA> <NA>
#> 9 Pattern drat -0.1700 0.7800 <NA> <NA>
#> 10 Pattern wt 0.4200 -0.7000 <NA> <NA>
#> 11 Pattern disp 0.5800 -0.6000 <NA> <NA>
#> 12 Structure hp 0.9300 -0.3800 <NA> <NA>
#> 13 Structure cyl 0.8500 -0.6800 <NA> <NA>
#> 14 Structure vs -0.8300 0.3500 <NA> <NA>
#> 15 Structure qsec -0.8200 -0.0900 <NA> <NA>
#> 16 Structure carb 0.7800 0.0000 <NA> <NA>
#> 17 Structure mpg -0.7600 0.7200 <NA> <NA>
#> 18 Structure am -0.1300 0.8900 <NA> <NA>
#> 19 Structure gear -0.0400 0.8700 <NA> <NA>
#> 20 Structure drat -0.3900 0.8300 <NA> <NA>
#> 21 Structure wt 0.6100 -0.8100 <NA> <NA>
#> 22 Structure disp 0.7500 -0.7700 <NA> <NA>
#> 23 <NA> <NA> 4.7068 4.1509 variance accounted SS loadings
#> 24 <NA> <NA> 0.4279 0.3774 variance accounted Proportion Var
#> 25 <NA> <NA> 0.4279 0.8053 variance accounted Cumulative Var
#> 26 <NA> <NA> 0.5314 0.4686 variance accounted Proportion Explained
#> 27 <NA> <NA> 0.5314 1.0000 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
#> MR1 MR2
#> mpg -0.12826 0.12270
#> cyl 0.44703 -0.08595
#> disp -0.23041 -0.46242
#> hp 0.39184 0.29700
#> drat 0.10511 0.15092
#> wt 0.22363 0.11141
#> qsec -0.24689 -0.27788
#> vs -0.10199 -0.08728
#> am 0.21179 0.28057
#> gear 0.29637 0.32215
#> carb -0.06294 -0.05850
#>
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="uls",oblique.scores=TRUE)
report_efa(model=model,df=mtcars)
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> [[1]]
#>
#> $correlations
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg reproduced correlations 0.852823 -0.8884146808 -0.873769 -0.768821 0.686482 -0.81533 0.44815 0.69572 0.5662638214 0.500723 -0.47287
#> cyl reproduced correlations -0.888415 0.9357702443 0.904939 0.844320 -0.675159 0.82878 -0.54193 -0.76269 -0.5226064376 -0.449493 0.55638
#> disp reproduced correlations -0.873769 0.9049385295 0.897960 0.765333 -0.723941 0.84596 -0.42046 -0.69325 -0.6148474388 -0.550190 0.45162
#> hp reproduced correlations -0.768821 0.8443196149 0.765333 0.876451 -0.450036 0.64811 -0.72112 -0.78744 -0.2262826359 -0.146767 0.69565
#> drat reproduced correlations 0.686482 -0.6751587018 -0.723941 -0.450036 0.708034 -0.73632 0.06876 0.41247 0.7174986941 0.683553 -0.13262
#> wt reproduced correlations -0.815327 0.8287773365 0.845956 0.648112 -0.736318 0.82067 -0.27814 -0.58917 -0.6760709963 -0.623095 0.32441
#> qsec reproduced correlations 0.448148 -0.5419313490 -0.420465 -0.721115 0.068757 -0.27814 0.78392 0.64274 -0.1939562742 -0.263731 -0.71433
#> vs reproduced correlations 0.695716 -0.7626924007 -0.693252 -0.787439 0.412467 -0.58917 0.64274 0.70761 0.2135589724 0.142237 -0.62117
#> am reproduced correlations 0.566264 -0.5226064376 -0.614847 -0.226283 0.717499 -0.67607 -0.19396 0.21356 0.8165109988 0.804660 0.10353
#> gear reproduced correlations 0.500723 -0.4494934108 -0.550190 -0.146767 0.683553 -0.62310 -0.26373 0.14224 0.8046601612 0.800123 0.16989
#> carb reproduced correlations -0.472869 0.5563784693 0.451619 0.695652 -0.132621 0.32441 -0.71433 -0.62117 0.1035312105 0.169885 0.65790
#> mpg1 observed correlations 1.000000 -0.8521619594 -0.847551 -0.776168 0.681172 -0.86766 0.41868 0.66404 0.5998324295 0.480285 -0.55093
#> cyl1 observed correlations -0.852162 1.0000000000 0.902033 0.832447 -0.699938 0.78250 -0.59124 -0.81081 -0.5226070469 -0.492687 0.52699
#> disp1 observed correlations -0.847551 0.9020328721 1.000000 0.790949 -0.710214 0.88798 -0.43370 -0.71042 -0.5912270401 -0.555569 0.39498
#> hp1 observed correlations -0.776168 0.8324474527 0.790949 1.000000 -0.448759 0.65875 -0.70822 -0.72310 -0.2432042572 -0.125704 0.74981
#> drat1 observed correlations 0.681172 -0.6999381138 -0.710214 -0.448759 1.000000 -0.71244 0.09120 0.44028 0.7127111272 0.699610 -0.09079
#> wt1 observed correlations -0.867659 0.7824957945 0.887980 0.658748 -0.712441 1.00000 -0.17472 -0.55492 -0.6924952588 -0.583287 0.42761
#> qsec1 observed correlations 0.418684 -0.5912420738 -0.433698 -0.708223 0.091205 -0.17472 1.00000 0.74454 -0.2298608622 -0.212682 -0.65625
#> vs1 observed correlations 0.664039 -0.8108117961 -0.710416 -0.723097 0.440278 -0.55492 0.74454 1.00000 0.1683451246 0.206023 -0.56961
#> am1 observed correlations 0.599832 -0.5226070469 -0.591227 -0.243204 0.712711 -0.69250 -0.22986 0.16835 1.0000000000 0.794059 0.05753
#> gear1 observed correlations 0.480285 -0.4926865994 -0.555569 -0.125704 0.699610 -0.58329 -0.21268 0.20602 0.7940587603 1.000000 0.27407
#> carb1 observed correlations -0.550925 0.5269882937 0.394977 0.749812 -0.090790 0.42761 -0.65625 -0.56961 0.0575343511 0.274073 1.00000
#> mpg2 residual correlations 0.147177 0.0362527213 0.026218 -0.007347 -0.005310 -0.05233 -0.02946 -0.03168 0.0335686081 -0.020439 -0.07806
#> cyl2 residual correlations 0.036253 0.0642297557 -0.002906 -0.011872 -0.024779 -0.04628 -0.04931 -0.04812 -0.0000006093 -0.043193 -0.02939
#> disp2 residual correlations 0.026218 -0.0029056573 0.102040 0.025615 0.013727 0.04202 -0.01323 -0.01716 0.0236203988 -0.005379 -0.05664
#> hp2 residual correlations -0.007347 -0.0118721621 0.025615 0.123549 0.001277 0.01064 0.01289 0.06434 -0.0169216212 0.021063 0.05416
#> drat2 residual correlations -0.005310 -0.0247794121 0.013727 0.001277 0.291966 0.02388 0.02245 0.02781 -0.0047875668 0.016057 0.04183
#> wt2 residual correlations -0.052333 -0.0462815420 0.042024 0.010635 0.023877 0.17933 0.10343 0.03426 -0.0164242626 0.039808 0.10320
#> qsec2 residual correlations -0.029464 -0.0493107248 -0.013233 0.012892 0.022448 0.10343 0.21608 0.10180 -0.0359045879 0.051048 0.05809
#> vs2 residual correlations -0.031677 -0.0481193954 -0.017164 0.064342 0.027811 0.03426 0.10180 0.29239 -0.0452138479 0.063787 0.05156
#> am2 residual correlations 0.033569 -0.0000006093 0.023620 -0.016922 -0.004788 -0.01642 -0.03590 -0.04521 0.1834890012 -0.010601 -0.04600
#> gear2 residual correlations -0.020439 -0.0431931885 -0.005379 0.021063 0.016057 0.03981 0.05105 0.06379 -0.0106014009 0.199877 0.10419
#> carb2 residual correlations -0.078056 -0.0293901755 -0.056642 0.054160 0.041831 0.10320 0.05809 0.05156 -0.0459968594 0.104188 0.34210
#>
#> $npobs
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg 32 32 32 32 32 32 32 32 32 32 32
#> cyl 32 32 32 32 32 32 32 32 32 32 32
#> disp 32 32 32 32 32 32 32 32 32 32 32
#> hp 32 32 32 32 32 32 32 32 32 32 32
#> drat 32 32 32 32 32 32 32 32 32 32 32
#> wt 32 32 32 32 32 32 32 32 32 32 32
#> qsec 32 32 32 32 32 32 32 32 32 32 32
#> vs 32 32 32 32 32 32 32 32 32 32 32
#> am 32 32 32 32 32 32 32 32 32 32 32
#> gear 32 32 32 32 32 32 32 32 32 32 32
#> carb 32 32 32 32 32 32 32 32 32 32 32
#>
#> $residual_stats
#> residual_statistics value critical formula
#> 1 Root Mean Squared Residual 0.04419 NA sqrt(mean(residuals^2))
#> 2 Number of absolute residuals > 0.05 13.00000 NA abs(residuals)>0.05
#> 3 Proportion of absolute residuals > 0.05 0.23636 0.5 numberLargeResiduals/nrow(residuals)
#>
#> $determinant_test
#> determinant above_critical
#> 1 0.0000002057 FALSE
#>
#> $bartlett_test
#> x_squared[bartlett] df[bartlett] p[bartlett]
#> 1 408 55 0.0000000000000000000000000000000000000000000000000000002227
#>
#> $kmo_test
#> Overall_MSA MSA Kaiser_1974
#> mpg 0.8266 0.9276 NA
#> cyl 0.8266 0.8966 NA
#> disp 0.8266 0.7647 NA
#> hp 0.8266 0.8386 NA
#> drat 0.8266 0.9497 NA
#> wt 0.8266 0.7410 NA
#> qsec 0.8266 0.7401 NA
#> vs 0.8266 0.9067 NA
#> am 0.8266 0.8778 NA
#> gear 0.8266 0.8470 NA
#> carb 0.8266 0.6211 NA
#>
#> $loadings
#> Matrix variable ULS2 ULS1 type row.names.model.Vaccounted.
#> 1 Pattern qsec -0.9200 -0.3500 <NA> <NA>
#> 2 Pattern hp 0.8900 -0.1200 <NA> <NA>
#> 3 Pattern carb 0.8500 0.2400 <NA> <NA>
#> 4 Pattern vs -0.8000 0.1200 <NA> <NA>
#> 5 Pattern cyl 0.7100 -0.4800 <NA> <NA>
#> 6 Pattern mpg -0.6100 0.5400 <NA> <NA>
#> 7 Pattern am 0.1300 0.9300 <NA> <NA>
#> 8 Pattern gear 0.2200 0.9300 <NA> <NA>
#> 9 Pattern drat -0.1700 0.7800 <NA> <NA>
#> 10 Pattern wt 0.4200 -0.7000 <NA> <NA>
#> 11 Pattern disp 0.5800 -0.6000 <NA> <NA>
#> 12 Structure hp 0.9300 -0.3800 <NA> <NA>
#> 13 Structure cyl 0.8500 -0.6800 <NA> <NA>
#> 14 Structure vs -0.8300 0.3500 <NA> <NA>
#> 15 Structure qsec -0.8200 -0.0900 <NA> <NA>
#> 16 Structure carb 0.7800 0.0000 <NA> <NA>
#> 17 Structure mpg -0.7600 0.7200 <NA> <NA>
#> 18 Structure am -0.1300 0.8900 <NA> <NA>
#> 19 Structure gear -0.0400 0.8700 <NA> <NA>
#> 20 Structure drat -0.3900 0.8300 <NA> <NA>
#> 21 Structure wt 0.6100 -0.8100 <NA> <NA>
#> 22 Structure disp 0.7500 -0.7700 <NA> <NA>
#> 23 <NA> <NA> 4.7069 4.1509 variance accounted SS loadings
#> 24 <NA> <NA> 0.4279 0.3774 variance accounted Proportion Var
#> 25 <NA> <NA> 0.4279 0.8053 variance accounted Cumulative Var
#> 26 <NA> <NA> 0.5314 0.4686 variance accounted Proportion Explained
#> 27 <NA> <NA> 0.5314 1.0000 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
#> ULS2 ULS1
#> mpg -0.12826 0.12270
#> cyl 0.44703 -0.08595
#> disp -0.23041 -0.46243
#> hp 0.39184 0.29700
#> drat 0.10510 0.15093
#> wt 0.22363 0.11141
#> qsec -0.24689 -0.27788
#> vs -0.10199 -0.08729
#> am 0.21179 0.28057
#> gear 0.29636 0.32215
#> carb -0.06294 -0.05850
#>
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="ols",oblique.scores=TRUE)
report_efa(model=model,df=mtcars)
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> [[1]]
#>
#> $correlations
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg reproduced correlations 0.852823 -0.8884143060 -0.873769 -0.768821 0.686481 -0.81533 0.44815 0.69572 0.5662636845 0.500723 -0.47287
#> cyl reproduced correlations -0.888414 0.9357700096 0.904938 0.844320 -0.675159 0.82878 -0.54193 -0.76269 -0.5226064484 -0.449493 0.55638
#> disp reproduced correlations -0.873769 0.9049382824 0.897960 0.765333 -0.723941 0.84596 -0.42046 -0.69325 -0.6148473484 -0.550190 0.45162
#> hp reproduced correlations -0.768821 0.8443195096 0.765333 0.876451 -0.450037 0.64811 -0.72112 -0.78744 -0.2262827617 -0.146767 0.69565
#> drat reproduced correlations 0.686481 -0.6751585946 -0.723941 -0.450037 0.708034 -0.73632 0.06876 0.41247 0.7174984685 0.683553 -0.13262
#> wt reproduced correlations -0.815326 0.8287770200 0.845955 0.648112 -0.736318 0.82067 -0.27814 -0.58917 -0.6760707159 -0.623095 0.32441
#> qsec reproduced correlations 0.448148 -0.5419313034 -0.420465 -0.721115 0.068757 -0.27814 0.78392 0.64274 -0.1939562582 -0.263731 -0.71433
#> vs reproduced correlations 0.695716 -0.7626925196 -0.693252 -0.787439 0.412467 -0.58917 0.64274 0.70761 0.2135590316 0.142237 -0.62117
#> am reproduced correlations 0.566264 -0.5226064484 -0.614847 -0.226283 0.717498 -0.67607 -0.19396 0.21356 0.8165108101 0.804660 0.10353
#> gear reproduced correlations 0.500723 -0.4494934932 -0.550190 -0.146767 0.683553 -0.62310 -0.26373 0.14224 0.8046601408 0.800123 0.16988
#> carb reproduced correlations -0.472868 0.5563783015 0.451618 0.695652 -0.132621 0.32441 -0.71433 -0.62117 0.1035310367 0.169885 0.65790
#> mpg1 observed correlations 1.000000 -0.8521619594 -0.847551 -0.776168 0.681172 -0.86766 0.41868 0.66404 0.5998324295 0.480285 -0.55093
#> cyl1 observed correlations -0.852162 1.0000000000 0.902033 0.832447 -0.699938 0.78250 -0.59124 -0.81081 -0.5226070469 -0.492687 0.52699
#> disp1 observed correlations -0.847551 0.9020328721 1.000000 0.790949 -0.710214 0.88798 -0.43370 -0.71042 -0.5912270401 -0.555569 0.39498
#> hp1 observed correlations -0.776168 0.8324474527 0.790949 1.000000 -0.448759 0.65875 -0.70822 -0.72310 -0.2432042572 -0.125704 0.74981
#> drat1 observed correlations 0.681172 -0.6999381138 -0.710214 -0.448759 1.000000 -0.71244 0.09120 0.44028 0.7127111272 0.699610 -0.09079
#> wt1 observed correlations -0.867659 0.7824957945 0.887980 0.658748 -0.712441 1.00000 -0.17472 -0.55492 -0.6924952588 -0.583287 0.42761
#> qsec1 observed correlations 0.418684 -0.5912420738 -0.433698 -0.708223 0.091205 -0.17472 1.00000 0.74454 -0.2298608622 -0.212682 -0.65625
#> vs1 observed correlations 0.664039 -0.8108117961 -0.710416 -0.723097 0.440278 -0.55492 0.74454 1.00000 0.1683451246 0.206023 -0.56961
#> am1 observed correlations 0.599832 -0.5226070469 -0.591227 -0.243204 0.712711 -0.69250 -0.22986 0.16835 1.0000000000 0.794059 0.05753
#> gear1 observed correlations 0.480285 -0.4926865994 -0.555569 -0.125704 0.699610 -0.58329 -0.21268 0.20602 0.7940587603 1.000000 0.27407
#> carb1 observed correlations -0.550925 0.5269882937 0.394977 0.749812 -0.090790 0.42761 -0.65625 -0.56961 0.0575343511 0.274073 1.00000
#> mpg2 residual correlations 0.147177 0.0362523465 0.026218 -0.007347 -0.005309 -0.05233 -0.02946 -0.03168 0.0335687449 -0.020438 -0.07806
#> cyl2 residual correlations 0.036252 0.0642299904 -0.002905 -0.011872 -0.024780 -0.04628 -0.04931 -0.04812 -0.0000005985 -0.043193 -0.02939
#> disp2 residual correlations 0.026218 -0.0029054103 0.102040 0.025616 0.013727 0.04202 -0.01323 -0.01716 0.0236203084 -0.005379 -0.05664
#> hp2 residual correlations -0.007347 -0.0118720568 0.025616 0.123549 0.001277 0.01064 0.01289 0.06434 -0.0169214954 0.021063 0.05416
#> drat2 residual correlations -0.005309 -0.0247795192 0.013727 0.001277 0.291966 0.02388 0.02245 0.02781 -0.0047873413 0.016057 0.04183
#> wt2 residual correlations -0.052333 -0.0462812255 0.042025 0.010636 0.023877 0.17933 0.10343 0.03426 -0.0164245429 0.039808 0.10320
#> qsec2 residual correlations -0.029464 -0.0493107703 -0.013233 0.012892 0.022448 0.10343 0.21608 0.10180 -0.0359046039 0.051049 0.05809
#> vs2 residual correlations -0.031677 -0.0481192764 -0.017164 0.064342 0.027811 0.03426 0.10180 0.29239 -0.0452139070 0.063787 0.05156
#> am2 residual correlations 0.033569 -0.0000005985 0.023620 -0.016921 -0.004787 -0.01642 -0.03590 -0.04521 0.1834891899 -0.010601 -0.04600
#> gear2 residual correlations -0.020438 -0.0431931062 -0.005379 0.021063 0.016057 0.03981 0.05105 0.06379 -0.0106013806 0.199877 0.10419
#> carb2 residual correlations -0.078057 -0.0293900077 -0.056642 0.054160 0.041831 0.10320 0.05809 0.05156 -0.0459966857 0.104188 0.34210
#>
#> $npobs
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg 32 32 32 32 32 32 32 32 32 32 32
#> cyl 32 32 32 32 32 32 32 32 32 32 32
#> disp 32 32 32 32 32 32 32 32 32 32 32
#> hp 32 32 32 32 32 32 32 32 32 32 32
#> drat 32 32 32 32 32 32 32 32 32 32 32
#> wt 32 32 32 32 32 32 32 32 32 32 32
#> qsec 32 32 32 32 32 32 32 32 32 32 32
#> vs 32 32 32 32 32 32 32 32 32 32 32
#> am 32 32 32 32 32 32 32 32 32 32 32
#> gear 32 32 32 32 32 32 32 32 32 32 32
#> carb 32 32 32 32 32 32 32 32 32 32 32
#>
#> $residual_stats
#> residual_statistics value critical formula
#> 1 Root Mean Squared Residual 0.04419 NA sqrt(mean(residuals^2))
#> 2 Number of absolute residuals > 0.05 13.00000 NA abs(residuals)>0.05
#> 3 Proportion of absolute residuals > 0.05 0.23636 0.5 numberLargeResiduals/nrow(residuals)
#>
#> $determinant_test
#> determinant above_critical
#> 1 0.0000002057 FALSE
#>
#> $bartlett_test
#> x_squared[bartlett] df[bartlett] p[bartlett]
#> 1 408 55 0.0000000000000000000000000000000000000000000000000000002227
#>
#> $kmo_test
#> Overall_MSA MSA Kaiser_1974
#> mpg 0.8266 0.9276 NA
#> cyl 0.8266 0.8966 NA
#> disp 0.8266 0.7647 NA
#> hp 0.8266 0.8386 NA
#> drat 0.8266 0.9497 NA
#> wt 0.8266 0.7410 NA
#> qsec 0.8266 0.7401 NA
#> vs 0.8266 0.9067 NA
#> am 0.8266 0.8778 NA
#> gear 0.8266 0.8470 NA
#> carb 0.8266 0.6211 NA
#>
#> $loadings
#> Matrix variable X1 X2 type row.names.model.Vaccounted.
#> 1 Pattern qsec -0.9200 -0.3500 <NA> <NA>
#> 2 Pattern hp 0.8900 -0.1200 <NA> <NA>
#> 3 Pattern carb 0.8500 0.2400 <NA> <NA>
#> 4 Pattern vs -0.8000 0.1200 <NA> <NA>
#> 5 Pattern cyl 0.7100 -0.4800 <NA> <NA>
#> 6 Pattern mpg -0.6100 0.5400 <NA> <NA>
#> 7 Pattern am 0.1300 0.9300 <NA> <NA>
#> 8 Pattern gear 0.2200 0.9300 <NA> <NA>
#> 9 Pattern drat -0.1700 0.7800 <NA> <NA>
#> 10 Pattern wt 0.4200 -0.7000 <NA> <NA>
#> 11 Pattern disp 0.5800 -0.6000 <NA> <NA>
#> 12 Structure hp 0.9300 -0.3800 <NA> <NA>
#> 13 Structure cyl 0.8500 -0.6800 <NA> <NA>
#> 14 Structure vs -0.8300 0.3500 <NA> <NA>
#> 15 Structure qsec -0.8200 -0.0900 <NA> <NA>
#> 16 Structure carb 0.7800 0.0000 <NA> <NA>
#> 17 Structure mpg -0.7600 0.7200 <NA> <NA>
#> 18 Structure am -0.1300 0.8900 <NA> <NA>
#> 19 Structure gear -0.0400 0.8700 <NA> <NA>
#> 20 Structure drat -0.3900 0.8300 <NA> <NA>
#> 21 Structure wt 0.6100 -0.8100 <NA> <NA>
#> 22 Structure disp 0.7500 -0.7700 <NA> <NA>
#> 23 <NA> <NA> 4.7069 4.1509 variance accounted SS loadings
#> 24 <NA> <NA> 0.4279 0.3774 variance accounted Proportion Var
#> 25 <NA> <NA> 0.4279 0.8053 variance accounted Cumulative Var
#> 26 <NA> <NA> 0.5314 0.4686 variance accounted Proportion Explained
#> 27 <NA> <NA> 0.5314 1.0000 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
#> [,1] [,2]
#> mpg -0.12826 0.12270
#> cyl 0.44703 -0.08595
#> disp -0.23041 -0.46243
#> hp 0.39185 0.29700
#> drat 0.10510 0.15092
#> wt 0.22363 0.11142
#> qsec -0.24689 -0.27788
#> vs -0.10199 -0.08728
#> am 0.21179 0.28057
#> gear 0.29636 0.32215
#> carb -0.06294 -0.05850
#>
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="wls",oblique.scores=TRUE)
report_efa(model=model,df=mtcars)
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> [[1]]
#>
#> $correlations
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg reproduced correlations 0.84978 -0.8820571 -0.86177 -0.7623918 0.691675 -0.80474 0.445250 0.70148 0.564761 0.497727 -0.47265
#> cyl reproduced correlations -0.88206 0.9254161 0.88952 0.8329133 -0.677753 0.81583 -0.533876 -0.76602 -0.520211 -0.446436 0.55280
#> disp reproduced correlations -0.86177 0.8895151 0.87645 0.7521300 -0.721754 0.82594 -0.415270 -0.69221 -0.606099 -0.540241 0.44787
#> hp reproduced correlations -0.76239 0.8329133 0.75213 0.8592082 -0.451132 0.63989 -0.701769 -0.78910 -0.228477 -0.150654 0.68624
#> drat reproduced correlations 0.69168 -0.6777531 -0.72175 -0.4511315 0.726775 -0.73439 0.070124 0.41650 0.728659 0.691194 -0.13120
#> wt reproduced correlations -0.80474 0.8158284 0.82594 0.6398865 -0.734386 0.80055 -0.280011 -0.58945 -0.665170 -0.609974 0.32475
#> qsec reproduced correlations 0.44525 -0.5338764 -0.41527 -0.7017691 0.070124 -0.28001 0.754864 0.64318 -0.184073 -0.249704 -0.70002
#> vs reproduced correlations 0.70148 -0.7660214 -0.69221 -0.7890992 0.416504 -0.58945 0.643177 0.72472 0.212547 0.141089 -0.62922
#> am reproduced correlations 0.56476 -0.5202105 -0.60610 -0.2284775 0.728659 -0.66517 -0.184073 0.21255 0.817045 0.800571 0.10218
#> gear reproduced correlations 0.49773 -0.4464355 -0.54024 -0.1506539 0.691194 -0.60997 -0.249704 0.14109 0.800571 0.791169 0.16593
#> carb reproduced correlations -0.47265 0.5528000 0.44787 0.6862405 -0.131203 0.32475 -0.700020 -0.62922 0.102182 0.165927 0.65524
#> mpg1 observed correlations 1.00000 -0.8521620 -0.84755 -0.7761684 0.681172 -0.86766 0.418684 0.66404 0.599832 0.480285 -0.55093
#> cyl1 observed correlations -0.85216 1.0000000 0.90203 0.8324475 -0.699938 0.78250 -0.591242 -0.81081 -0.522607 -0.492687 0.52699
#> disp1 observed correlations -0.84755 0.9020329 1.00000 0.7909486 -0.710214 0.88798 -0.433698 -0.71042 -0.591227 -0.555569 0.39498
#> hp1 observed correlations -0.77617 0.8324475 0.79095 1.0000000 -0.448759 0.65875 -0.708223 -0.72310 -0.243204 -0.125704 0.74981
#> drat1 observed correlations 0.68117 -0.6999381 -0.71021 -0.4487591 1.000000 -0.71244 0.091205 0.44028 0.712711 0.699610 -0.09079
#> wt1 observed correlations -0.86766 0.7824958 0.88798 0.6587479 -0.712441 1.00000 -0.174716 -0.55492 -0.692495 -0.583287 0.42761
#> qsec1 observed correlations 0.41868 -0.5912421 -0.43370 -0.7082234 0.091205 -0.17472 1.000000 0.74454 -0.229861 -0.212682 -0.65625
#> vs1 observed correlations 0.66404 -0.8108118 -0.71042 -0.7230967 0.440278 -0.55492 0.744535 1.00000 0.168345 0.206023 -0.56961
#> am1 observed correlations 0.59983 -0.5226070 -0.59123 -0.2432043 0.712711 -0.69250 -0.229861 0.16835 1.000000 0.794059 0.05753
#> gear1 observed correlations 0.48028 -0.4926866 -0.55557 -0.1257043 0.699610 -0.58329 -0.212682 0.20602 0.794059 1.000000 0.27407
#> carb1 observed correlations -0.55093 0.5269883 0.39498 0.7498125 -0.090790 0.42761 -0.656249 -0.56961 0.057534 0.274073 1.00000
#> mpg2 residual correlations 0.15022 0.0298951 0.01422 -0.0137765 -0.010503 -0.06292 -0.026566 -0.03744 0.035071 -0.017443 -0.07828
#> cyl2 residual correlations 0.02990 0.0745839 0.01252 -0.0004659 -0.022185 -0.03333 -0.057366 -0.04479 -0.002397 -0.046251 -0.02581
#> disp2 residual correlations 0.01422 0.0125178 0.12355 0.0388186 0.011540 0.06204 -0.018428 -0.01820 0.014872 -0.015329 -0.05289
#> hp2 residual correlations -0.01378 -0.0004659 0.03882 0.1407918 0.002372 0.01886 -0.006454 0.06600 -0.014727 0.024950 0.06357
#> drat2 residual correlations -0.01050 -0.0221850 0.01154 0.0023724 0.273225 0.02195 0.021081 0.02377 -0.015948 0.008416 0.04041
#> wt2 residual correlations -0.06292 -0.0333326 0.06204 0.0188614 0.021945 0.19945 0.105295 0.03453 -0.027325 0.026687 0.10286
#> qsec2 residual correlations -0.02657 -0.0573656 -0.01843 -0.0064543 0.021081 0.10530 0.245136 0.10136 -0.045788 0.037022 0.04377
#> vs2 residual correlations -0.03744 -0.0447904 -0.01820 0.0660025 0.023775 0.03453 0.101358 0.27528 -0.044202 0.064935 0.05962
#> am2 residual correlations 0.03507 -0.0023965 0.01487 -0.0147268 -0.015948 -0.02732 -0.045788 -0.04420 0.182955 -0.006512 -0.04465
#> gear2 residual correlations -0.01744 -0.0462511 -0.01533 0.0249496 0.008416 0.02669 0.037022 0.06493 -0.006512 0.208831 0.10815
#> carb2 residual correlations -0.07828 -0.0258117 -0.05289 0.0635719 0.040413 0.10286 0.043770 0.05962 -0.044647 0.108146 0.34476
#>
#> $npobs
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg 32 32 32 32 32 32 32 32 32 32 32
#> cyl 32 32 32 32 32 32 32 32 32 32 32
#> disp 32 32 32 32 32 32 32 32 32 32 32
#> hp 32 32 32 32 32 32 32 32 32 32 32
#> drat 32 32 32 32 32 32 32 32 32 32 32
#> wt 32 32 32 32 32 32 32 32 32 32 32
#> qsec 32 32 32 32 32 32 32 32 32 32 32
#> vs 32 32 32 32 32 32 32 32 32 32 32
#> am 32 32 32 32 32 32 32 32 32 32 32
#> gear 32 32 32 32 32 32 32 32 32 32 32
#> carb 32 32 32 32 32 32 32 32 32 32 32
#>
#> $residual_stats
#> residual_statistics value critical formula
#> 1 Root Mean Squared Residual 0.04491 NA sqrt(mean(residuals^2))
#> 2 Number of absolute residuals > 0.05 13.00000 NA abs(residuals)>0.05
#> 3 Proportion of absolute residuals > 0.05 0.23636 0.5 numberLargeResiduals/nrow(residuals)
#>
#> $determinant_test
#> determinant above_critical
#> 1 0.0000002057 FALSE
#>
#> $bartlett_test
#> x_squared[bartlett] df[bartlett] p[bartlett]
#> 1 408 55 0.0000000000000000000000000000000000000000000000000000002227
#>
#> $kmo_test
#> Overall_MSA MSA Kaiser_1974
#> mpg 0.8266 0.9276 NA
#> cyl 0.8266 0.8966 NA
#> disp 0.8266 0.7647 NA
#> hp 0.8266 0.8386 NA
#> drat 0.8266 0.9497 NA
#> wt 0.8266 0.7410 NA
#> qsec 0.8266 0.7401 NA
#> vs 0.8266 0.9067 NA
#> am 0.8266 0.8778 NA
#> gear 0.8266 0.8470 NA
#> carb 0.8266 0.6211 NA
#>
#> $loadings
#> Matrix variable WLS1 WLS2 type row.names.model.Vaccounted.
#> 1 Pattern qsec -0.9000 -0.3500 <NA> <NA>
#> 2 Pattern hp 0.8900 -0.1100 <NA> <NA>
#> 3 Pattern carb 0.8400 0.2500 <NA> <NA>
#> 4 Pattern vs -0.8200 0.1000 <NA> <NA>
#> 5 Pattern cyl 0.7200 -0.4600 <NA> <NA>
#> 6 Pattern mpg -0.6200 0.5300 <NA> <NA>
#> 7 Pattern disp 0.5900 -0.5800 <NA> <NA>
#> 8 Pattern am 0.1200 0.9300 <NA> <NA>
#> 9 Pattern gear 0.2000 0.9300 <NA> <NA>
#> 10 Pattern drat -0.1800 0.7800 <NA> <NA>
#> 11 Pattern wt 0.4300 -0.6700 <NA> <NA>
#> 12 Structure hp 0.9200 -0.3600 <NA> <NA>
#> 13 Structure cyl 0.8600 -0.6700 <NA> <NA>
#> 14 Structure vs -0.8500 0.3400 <NA> <NA>
#> 15 Structure qsec -0.8000 -0.1000 <NA> <NA>
#> 16 Structure mpg -0.7700 0.7000 <NA> <NA>
#> 17 Structure carb 0.7700 0.0100 <NA> <NA>
#> 18 Structure disp 0.7500 -0.7500 <NA> <NA>
#> 19 Structure am -0.1500 0.9000 <NA> <NA>
#> 20 Structure gear -0.0600 0.8700 <NA> <NA>
#> 21 Structure drat -0.4100 0.8300 <NA> <NA>
#> 22 Structure wt 0.6200 -0.7900 <NA> <NA>
#> 23 <NA> <NA> 4.7374 4.0438 variance accounted SS loadings
#> 24 <NA> <NA> 0.4307 0.3676 variance accounted Proportion Var
#> 25 <NA> <NA> 0.4307 0.7983 variance accounted Cumulative Var
#> 26 <NA> <NA> 0.5395 0.4605 variance accounted Proportion Explained
#> 27 <NA> <NA> 0.5395 1.0000 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
#> WLS1 WLS2
#> mpg -0.16930 0.10619
#> cyl 0.44769 -0.01193
#> disp -0.18246 -0.39136
#> hp 0.36842 0.25800
#> drat 0.11254 0.21029
#> wt 0.14174 0.10278
#> qsec -0.13074 -0.20271
#> vs -0.19067 -0.10342
#> am 0.22599 0.32521
#> gear 0.29046 0.32941
#> carb -0.02599 -0.04586
#>
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="gls",oblique.scores=TRUE)
report_efa(model=model,df=mtcars)
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> [[1]]
#>
#> $correlations
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg reproduced correlations 0.870010 -0.8897877 -0.870355 -0.7743318 0.721204 -0.81485 0.4541307 0.721264 0.573550 0.50426 -0.48227
#> cyl reproduced correlations -0.889788 0.9196736 0.885281 0.8332038 -0.694205 0.81409 -0.5355607 -0.776496 -0.519870 -0.44505 0.55529
#> disp reproduced correlations -0.870355 0.8852805 0.873145 0.7538748 -0.743322 0.82488 -0.4185352 -0.702011 -0.607345 -0.54001 0.45124
#> hp reproduced correlations -0.774332 0.8332038 0.753875 0.8655090 -0.456481 0.64283 -0.7080069 -0.807878 -0.225406 -0.14685 0.69438
#> drat reproduced correlations 0.721204 -0.6942052 -0.743322 -0.4564807 0.792803 -0.76214 0.0569993 0.423425 0.775193 0.73550 -0.12098
#> wt reproduced correlations -0.814854 0.8140890 0.824882 0.6428257 -0.762140 0.80171 -0.2833396 -0.597983 -0.670426 -0.61341 0.32777
#> qsec reproduced correlations 0.454131 -0.5355607 -0.418535 -0.7080069 0.056999 -0.28334 0.7605215 0.662389 -0.191784 -0.25704 -0.70858
#> vs reproduced correlations 0.721264 -0.7764962 -0.702011 -0.8078783 0.423425 -0.59798 0.6623887 0.754098 0.207234 0.13390 -0.64933
#> am reproduced correlations 0.573550 -0.5198704 -0.607345 -0.2254060 0.775193 -0.67043 -0.1917835 0.207234 0.838966 0.82057 0.11072
#> gear reproduced correlations 0.504256 -0.4450508 -0.540011 -0.1468540 0.735498 -0.61341 -0.2570352 0.133904 0.820570 0.80931 0.17451
#> carb reproduced correlations -0.482269 0.5552890 0.451238 0.6943828 -0.120984 0.32777 -0.7085850 -0.649329 0.110721 0.17451 0.66604
#> mpg1 observed correlations 1.000000 -0.8521620 -0.847551 -0.7761684 0.681172 -0.86766 0.4186840 0.664039 0.599832 0.48028 -0.55093
#> cyl1 observed correlations -0.852162 1.0000000 0.902033 0.8324475 -0.699938 0.78250 -0.5912421 -0.810812 -0.522607 -0.49269 0.52699
#> disp1 observed correlations -0.847551 0.9020329 1.000000 0.7909486 -0.710214 0.88798 -0.4336979 -0.710416 -0.591227 -0.55557 0.39498
#> hp1 observed correlations -0.776168 0.8324475 0.790949 1.0000000 -0.448759 0.65875 -0.7082234 -0.723097 -0.243204 -0.12570 0.74981
#> drat1 observed correlations 0.681172 -0.6999381 -0.710214 -0.4487591 1.000000 -0.71244 0.0912048 0.440278 0.712711 0.69961 -0.09079
#> wt1 observed correlations -0.867659 0.7824958 0.887980 0.6587479 -0.712441 1.00000 -0.1747159 -0.554916 -0.692495 -0.58329 0.42761
#> qsec1 observed correlations 0.418684 -0.5912421 -0.433698 -0.7082234 0.091205 -0.17472 1.0000000 0.744535 -0.229861 -0.21268 -0.65625
#> vs1 observed correlations 0.664039 -0.8108118 -0.710416 -0.7230967 0.440278 -0.55492 0.7445354 1.000000 0.168345 0.20602 -0.56961
#> am1 observed correlations 0.599832 -0.5226070 -0.591227 -0.2432043 0.712711 -0.69250 -0.2298609 0.168345 1.000000 0.79406 0.05753
#> gear1 observed correlations 0.480285 -0.4926866 -0.555569 -0.1257043 0.699610 -0.58329 -0.2126822 0.206023 0.794059 1.00000 0.27407
#> carb1 observed correlations -0.550925 0.5269883 0.394977 0.7498125 -0.090790 0.42761 -0.6562492 -0.569607 0.057534 0.27407 1.00000
#> mpg2 residual correlations 0.129990 0.0376257 0.022803 -0.0018366 -0.040032 -0.05281 -0.0354466 -0.057226 0.026282 -0.02397 -0.06866
#> cyl2 residual correlations 0.037626 0.0803264 0.016752 -0.0007564 -0.005733 -0.03159 -0.0556814 -0.034316 -0.002737 -0.04764 -0.02830
#> disp2 residual correlations 0.022803 0.0167523 0.126855 0.0370738 0.033108 0.06310 -0.0151627 -0.008405 0.016118 -0.01556 -0.05626
#> hp2 residual correlations -0.001837 -0.0007564 0.037074 0.1344910 0.007722 0.01592 -0.0002165 0.084782 -0.017798 0.02115 0.05543
#> drat2 residual correlations -0.040032 -0.0057329 0.033108 0.0077216 0.207197 0.04970 0.0342054 0.016854 -0.062482 -0.03589 0.03019
#> wt2 residual correlations -0.052805 -0.0315932 0.063098 0.0159222 0.049699 0.19829 0.1086237 0.043067 -0.022069 0.03013 0.09984
#> qsec2 residual correlations -0.035447 -0.0556814 -0.015163 -0.0002165 0.034205 0.10862 0.2394785 0.082147 -0.038077 0.04435 0.05234
#> vs2 residual correlations -0.057226 -0.0343156 -0.008405 0.0847816 0.016854 0.04307 0.0821467 0.245902 -0.038889 0.07212 0.07972
#> am2 residual correlations 0.026282 -0.0027367 0.016118 -0.0177982 -0.062482 -0.02207 -0.0380773 -0.038889 0.161034 -0.02651 -0.05319
#> gear2 residual correlations -0.023972 -0.0476358 -0.015558 0.0211498 -0.035888 0.03013 0.0443529 0.072120 -0.026512 0.19069 0.09956
#> carb2 residual correlations -0.068656 -0.0283007 -0.056261 0.0554297 0.030194 0.09984 0.0523358 0.079722 -0.053186 0.09956 0.33396
#>
#> $npobs
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg 32 32 32 32 32 32 32 32 32 32 32
#> cyl 32 32 32 32 32 32 32 32 32 32 32
#> disp 32 32 32 32 32 32 32 32 32 32 32
#> hp 32 32 32 32 32 32 32 32 32 32 32
#> drat 32 32 32 32 32 32 32 32 32 32 32
#> wt 32 32 32 32 32 32 32 32 32 32 32
#> qsec 32 32 32 32 32 32 32 32 32 32 32
#> vs 32 32 32 32 32 32 32 32 32 32 32
#> am 32 32 32 32 32 32 32 32 32 32 32
#> gear 32 32 32 32 32 32 32 32 32 32 32
#> carb 32 32 32 32 32 32 32 32 32 32 32
#>
#> $residual_stats
#> residual_statistics value critical formula
#> 1 Root Mean Squared Residual 0.04704 NA sqrt(mean(residuals^2))
#> 2 Number of absolute residuals > 0.05 17.00000 NA abs(residuals)>0.05
#> 3 Proportion of absolute residuals > 0.05 0.30909 0.5 numberLargeResiduals/nrow(residuals)
#>
#> $determinant_test
#> determinant above_critical
#> 1 0.0000002057 FALSE
#>
#> $bartlett_test
#> x_squared[bartlett] df[bartlett] p[bartlett]
#> 1 408 55 0.0000000000000000000000000000000000000000000000000000002227
#>
#> $kmo_test
#> Overall_MSA MSA Kaiser_1974
#> mpg 0.8266 0.9276 NA
#> cyl 0.8266 0.8966 NA
#> disp 0.8266 0.7647 NA
#> hp 0.8266 0.8386 NA
#> drat 0.8266 0.9497 NA
#> wt 0.8266 0.7410 NA
#> qsec 0.8266 0.7401 NA
#> vs 0.8266 0.9067 NA
#> am 0.8266 0.8778 NA
#> gear 0.8266 0.8470 NA
#> carb 0.8266 0.6211 NA
#>
#> $loadings
#> Matrix variable GLS1 GLS2 type row.names.model.Vaccounted.
#> 1 Pattern hp 0.9000 -0.1000 <NA> <NA>
#> 2 Pattern qsec -0.9000 -0.3600 <NA> <NA>
#> 3 Pattern carb 0.8500 0.2600 <NA> <NA>
#> 4 Pattern vs -0.8400 0.0900 <NA> <NA>
#> 5 Pattern cyl 0.7300 -0.4500 <NA> <NA>
#> 6 Pattern mpg -0.6400 0.5300 <NA> <NA>
#> 7 Pattern disp 0.6000 -0.5700 <NA> <NA>
#> 8 Pattern am 0.1200 0.9400 <NA> <NA>
#> 9 Pattern gear 0.2000 0.9400 <NA> <NA>
#> 10 Pattern drat -0.1800 0.8200 <NA> <NA>
#> 11 Pattern wt 0.4400 -0.6700 <NA> <NA>
#> 12 Structure hp 0.9300 -0.3500 <NA> <NA>
#> 13 Structure cyl 0.8600 -0.6600 <NA> <NA>
#> 14 Structure vs -0.8600 0.3300 <NA> <NA>
#> 15 Structure qsec -0.8000 -0.1000 <NA> <NA>
#> 16 Structure mpg -0.7800 0.7100 <NA> <NA>
#> 17 Structure carb 0.7800 0.0200 <NA> <NA>
#> 18 Structure disp 0.7600 -0.7400 <NA> <NA>
#> 19 Structure am -0.1500 0.9100 <NA> <NA>
#> 20 Structure gear -0.0600 0.8800 <NA> <NA>
#> 21 Structure drat -0.4100 0.8700 <NA> <NA>
#> 22 Structure wt 0.6300 -0.7900 <NA> <NA>
#> 23 <NA> <NA> 4.8303 4.1215 variance accounted SS loadings
#> 24 <NA> <NA> 0.4391 0.3747 variance accounted Proportion Var
#> 25 <NA> <NA> 0.4391 0.8138 variance accounted Cumulative Var
#> 26 <NA> <NA> 0.5396 0.4604 variance accounted Proportion Explained
#> 27 <NA> <NA> 0.5396 1.0000 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
#> GLS1 GLS2
#> mpg -0.23450 0.09205
#> cyl 0.39574 0.13478
#> disp -0.17875 -0.37862
#> hp 0.39163 0.24708
#> drat 0.14230 0.34925
#> wt 0.11186 0.12424
#> qsec -0.07003 -0.13058
#> vs -0.27851 -0.11901
#> am 0.22848 0.34634
#> gear 0.29491 0.35148
#> carb -0.04401 -0.08598
#>
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="pa",oblique.scores=TRUE)
report_efa(model=model,df=mtcars)
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> [[1]]
#>
#> $correlations
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg reproduced correlations 0.852824 -0.8883476 -0.873810 -0.768684 0.686518 -0.81545 0.44814 0.69566 0.5660838 0.500755 -0.47308
#> cyl reproduced correlations -0.888348 0.9356154 0.904908 0.844066 -0.675164 0.82885 -0.54184 -0.76254 -0.5224894 -0.449535 0.55660
#> disp reproduced correlations -0.873810 0.9049076 0.898051 0.765207 -0.724043 0.84614 -0.42041 -0.69321 -0.6147040 -0.550301 0.45176
#> hp reproduced correlations -0.768684 0.8440659 0.765207 0.876037 -0.450011 0.64810 -0.72092 -0.78715 -0.2264498 -0.146847 0.69602
#> drat reproduced correlations 0.686518 -0.6751641 -0.724043 -0.450011 0.708135 -0.73649 0.06870 0.41248 0.7171450 0.683645 -0.13243
#> wt reproduced correlations -0.815448 0.8288468 0.846137 0.648103 -0.736486 0.82093 -0.27814 -0.58922 -0.6758818 -0.623244 0.32446
#> qsec reproduced correlations 0.448136 -0.5418377 -0.420414 -0.720917 0.068699 -0.27814 0.78401 0.64263 -0.1935984 -0.263780 -0.71513
#> vs reproduced correlations 0.695662 -0.7625429 -0.693208 -0.787154 0.412480 -0.58922 0.64263 0.70743 0.2137105 0.142310 -0.62157
#> am reproduced correlations 0.566084 -0.5224894 -0.614704 -0.226450 0.717145 -0.67588 -0.19360 0.21371 0.8153664 0.804100 0.10364
#> gear reproduced correlations 0.500755 -0.4495352 -0.550301 -0.146847 0.683645 -0.62324 -0.26378 0.14231 0.8040996 0.800180 0.17038
#> carb reproduced correlations -0.473079 0.5566050 0.451764 0.696018 -0.132431 0.32446 -0.71513 -0.62157 0.1036423 0.170375 0.65923
#> mpg1 observed correlations 1.000000 -0.8521620 -0.847551 -0.776168 0.681172 -0.86766 0.41868 0.66404 0.5998324 0.480285 -0.55093
#> cyl1 observed correlations -0.852162 1.0000000 0.902033 0.832447 -0.699938 0.78250 -0.59124 -0.81081 -0.5226070 -0.492687 0.52699
#> disp1 observed correlations -0.847551 0.9020329 1.000000 0.790949 -0.710214 0.88798 -0.43370 -0.71042 -0.5912270 -0.555569 0.39498
#> hp1 observed correlations -0.776168 0.8324475 0.790949 1.000000 -0.448759 0.65875 -0.70822 -0.72310 -0.2432043 -0.125704 0.74981
#> drat1 observed correlations 0.681172 -0.6999381 -0.710214 -0.448759 1.000000 -0.71244 0.09120 0.44028 0.7127111 0.699610 -0.09079
#> wt1 observed correlations -0.867659 0.7824958 0.887980 0.658748 -0.712441 1.00000 -0.17472 -0.55492 -0.6924953 -0.583287 0.42761
#> qsec1 observed correlations 0.418684 -0.5912421 -0.433698 -0.708223 0.091205 -0.17472 1.00000 0.74454 -0.2298609 -0.212682 -0.65625
#> vs1 observed correlations 0.664039 -0.8108118 -0.710416 -0.723097 0.440278 -0.55492 0.74454 1.00000 0.1683451 0.206023 -0.56961
#> am1 observed correlations 0.599832 -0.5226070 -0.591227 -0.243204 0.712711 -0.69250 -0.22986 0.16835 1.0000000 0.794059 0.05753
#> gear1 observed correlations 0.480285 -0.4926866 -0.555569 -0.125704 0.699610 -0.58329 -0.21268 0.20602 0.7940588 1.000000 0.27407
#> carb1 observed correlations -0.550925 0.5269883 0.394977 0.749812 -0.090790 0.42761 -0.65625 -0.56961 0.0575344 0.274073 1.00000
#> mpg2 residual correlations 0.147176 0.0361856 0.026259 -0.007484 -0.005346 -0.05221 -0.02945 -0.03162 0.0337486 -0.020471 -0.07785
#> cyl2 residual correlations 0.036186 0.0643846 -0.002875 -0.011618 -0.024774 -0.04635 -0.04940 -0.04827 -0.0001176 -0.043151 -0.02962
#> disp2 residual correlations 0.026259 -0.0028747 0.101949 0.025742 0.013829 0.04184 -0.01328 -0.01721 0.0234769 -0.005269 -0.05679
#> hp2 residual correlations -0.007484 -0.0116184 0.025742 0.123963 0.001252 0.01064 0.01269 0.06406 -0.0167544 0.021143 0.05379
#> drat2 residual correlations -0.005346 -0.0247740 0.013829 0.001252 0.291865 0.02405 0.02251 0.02780 -0.0044339 0.015965 0.04164
#> wt2 residual correlations -0.052211 -0.0463510 0.041843 0.010645 0.024045 0.17907 0.10342 0.03431 -0.0166135 0.039957 0.10314
#> qsec2 residual correlations -0.029452 -0.0494044 -0.013284 0.012693 0.022506 0.10342 0.21599 0.10190 -0.0362625 0.051097 0.05888
#> vs2 residual correlations -0.031623 -0.0482689 -0.017208 0.064057 0.027799 0.03431 0.10190 0.29257 -0.0453654 0.063713 0.05196
#> am2 residual correlations 0.033749 -0.0001176 0.023477 -0.016754 -0.004434 -0.01661 -0.03626 -0.04537 0.1846336 -0.010041 -0.04611
#> gear2 residual correlations -0.020471 -0.0431514 -0.005269 0.021143 0.015965 0.03996 0.05110 0.06371 -0.0100408 0.199820 0.10370
#> carb2 residual correlations -0.077846 -0.0296167 -0.056787 0.053794 0.041641 0.10314 0.05888 0.05196 -0.0461079 0.103698 0.34077
#>
#> $npobs
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg 32 32 32 32 32 32 32 32 32 32 32
#> cyl 32 32 32 32 32 32 32 32 32 32 32
#> disp 32 32 32 32 32 32 32 32 32 32 32
#> hp 32 32 32 32 32 32 32 32 32 32 32
#> drat 32 32 32 32 32 32 32 32 32 32 32
#> wt 32 32 32 32 32 32 32 32 32 32 32
#> qsec 32 32 32 32 32 32 32 32 32 32 32
#> vs 32 32 32 32 32 32 32 32 32 32 32
#> am 32 32 32 32 32 32 32 32 32 32 32
#> gear 32 32 32 32 32 32 32 32 32 32 32
#> carb 32 32 32 32 32 32 32 32 32 32 32
#>
#> $residual_stats
#> residual_statistics value critical formula
#> 1 Root Mean Squared Residual 0.04419 NA sqrt(mean(residuals^2))
#> 2 Number of absolute residuals > 0.05 13.00000 NA abs(residuals)>0.05
#> 3 Proportion of absolute residuals > 0.05 0.23636 0.5 numberLargeResiduals/nrow(residuals)
#>
#> $determinant_test
#> determinant above_critical
#> 1 0.0000002057 FALSE
#>
#> $bartlett_test
#> x_squared[bartlett] df[bartlett] p[bartlett]
#> 1 408 55 0.0000000000000000000000000000000000000000000000000000002227
#>
#> $kmo_test
#> Overall_MSA MSA Kaiser_1974
#> mpg 0.8266 0.9276 NA
#> cyl 0.8266 0.8966 NA
#> disp 0.8266 0.7647 NA
#> hp 0.8266 0.8386 NA
#> drat 0.8266 0.9497 NA
#> wt 0.8266 0.7410 NA
#> qsec 0.8266 0.7401 NA
#> vs 0.8266 0.9067 NA
#> am 0.8266 0.8778 NA
#> gear 0.8266 0.8470 NA
#> carb 0.8266 0.6211 NA
#>
#> $loadings
#> Matrix variable PA1 PA2 type row.names.model.Vaccounted.
#> 1 Pattern qsec -0.9200 -0.3500 <NA> <NA>
#> 2 Pattern hp 0.8900 -0.1200 <NA> <NA>
#> 3 Pattern carb 0.8500 0.2400 <NA> <NA>
#> 4 Pattern vs -0.8000 0.1200 <NA> <NA>
#> 5 Pattern cyl 0.7100 -0.4800 <NA> <NA>
#> 6 Pattern mpg -0.6100 0.5500 <NA> <NA>
#> 7 Pattern am 0.1300 0.9300 <NA> <NA>
#> 8 Pattern gear 0.2200 0.9300 <NA> <NA>
#> 9 Pattern drat -0.1700 0.7800 <NA> <NA>
#> 10 Pattern wt 0.4200 -0.7000 <NA> <NA>
#> 11 Pattern disp 0.5800 -0.6000 <NA> <NA>
#> 12 Structure hp 0.9300 -0.3800 <NA> <NA>
#> 13 Structure cyl 0.8500 -0.6800 <NA> <NA>
#> 14 Structure vs -0.8300 0.3500 <NA> <NA>
#> 15 Structure qsec -0.8200 -0.0900 <NA> <NA>
#> 16 Structure carb 0.7800 0.0000 <NA> <NA>
#> 17 Structure mpg -0.7600 0.7200 <NA> <NA>
#> 18 Structure am -0.1300 0.8900 <NA> <NA>
#> 19 Structure gear -0.0400 0.8700 <NA> <NA>
#> 20 Structure drat -0.3900 0.8300 <NA> <NA>
#> 21 Structure wt 0.6100 -0.8100 <NA> <NA>
#> 22 Structure disp 0.7500 -0.7700 <NA> <NA>
#> 23 <NA> <NA> 4.7008 4.1570 variance accounted SS loadings
#> 24 <NA> <NA> 0.4273 0.3779 variance accounted Proportion Var
#> 25 <NA> <NA> 0.4273 0.8053 variance accounted Cumulative Var
#> 26 <NA> <NA> 0.5307 0.4693 variance accounted Proportion Explained
#> 27 <NA> <NA> 0.5307 1.0000 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
#> mpg -0.12719 0.12383
#> cyl 0.44305 -0.08942
#> disp -0.22261 -0.45697
#> hp 0.38630 0.29346
#> drat 0.10438 0.15061
#> wt 0.21774 0.10593
#> qsec -0.24784 -0.27803
#> vs -0.10108 -0.08715
#> am 0.21028 0.27688
#> gear 0.29479 0.32111
#> carb -0.05545 -0.05342
#>
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="ml",oblique.scores=TRUE)
report_efa(model=model,df=mtcars)
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> [[1]]
#>
#> $correlations
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg reproduced correlations 0.832842 -0.871663 -0.867526 -0.750332 0.689137 -0.808164 0.434978 0.688421 0.573534 0.514470 -0.45056
#> cyl reproduced correlations -0.871663 0.930250 0.904782 0.842341 -0.672580 0.816515 -0.561007 -0.776589 -0.511982 -0.449915 0.55312
#> disp reproduced correlations -0.867526 0.904782 0.904219 0.771474 -0.726462 0.847016 -0.434354 -0.707154 -0.613064 -0.551584 0.45487
#> hp reproduced correlations -0.750332 0.842341 0.771474 0.857149 -0.466249 0.634973 -0.727776 -0.798335 -0.236299 -0.182295 0.66498
#> drat reproduced correlations 0.689137 -0.672580 -0.726462 -0.466249 0.702192 -0.748201 0.073238 0.417613 0.713907 0.665710 -0.15170
#> wt reproduced correlations -0.808164 0.816515 0.847016 0.634973 -0.748201 0.832092 -0.249416 -0.576458 -0.700694 -0.643787 0.30403
#> qsec reproduced correlations 0.434978 -0.561007 -0.434354 -0.727776 0.073238 -0.249416 0.849989 0.689811 -0.220397 -0.252712 -0.71567
#> vs reproduced correlations 0.688421 -0.776589 -0.707154 -0.798335 0.417613 -0.576458 0.689811 0.744174 0.198365 0.148765 -0.62715
#> am reproduced correlations 0.573534 -0.511982 -0.613064 -0.236299 0.713907 -0.700694 -0.220397 0.198365 0.829030 0.789581 0.09074
#> gear reproduced correlations 0.514470 -0.449915 -0.551584 -0.182295 0.665710 -0.643787 -0.252712 0.148765 0.789581 0.754324 0.12383
#> carb reproduced correlations -0.450558 0.553124 0.454869 0.664984 -0.151703 0.304029 -0.715672 -0.627149 0.090742 0.123827 0.61423
#> mpg1 observed correlations 1.000000 -0.852162 -0.847551 -0.776168 0.681172 -0.867659 0.418684 0.664039 0.599832 0.480285 -0.55093
#> cyl1 observed correlations -0.852162 1.000000 0.902033 0.832447 -0.699938 0.782496 -0.591242 -0.810812 -0.522607 -0.492687 0.52699
#> disp1 observed correlations -0.847551 0.902033 1.000000 0.790949 -0.710214 0.887980 -0.433698 -0.710416 -0.591227 -0.555569 0.39498
#> hp1 observed correlations -0.776168 0.832447 0.790949 1.000000 -0.448759 0.658748 -0.708223 -0.723097 -0.243204 -0.125704 0.74981
#> drat1 observed correlations 0.681172 -0.699938 -0.710214 -0.448759 1.000000 -0.712441 0.091205 0.440278 0.712711 0.699610 -0.09079
#> wt1 observed correlations -0.867659 0.782496 0.887980 0.658748 -0.712441 1.000000 -0.174716 -0.554916 -0.692495 -0.583287 0.42761
#> qsec1 observed correlations 0.418684 -0.591242 -0.433698 -0.708223 0.091205 -0.174716 1.000000 0.744535 -0.229861 -0.212682 -0.65625
#> vs1 observed correlations 0.664039 -0.810812 -0.710416 -0.723097 0.440278 -0.554916 0.744535 1.000000 0.168345 0.206023 -0.56961
#> am1 observed correlations 0.599832 -0.522607 -0.591227 -0.243204 0.712711 -0.692495 -0.229861 0.168345 1.000000 0.794059 0.05753
#> gear1 observed correlations 0.480285 -0.492687 -0.555569 -0.125704 0.699610 -0.583287 -0.212682 0.206023 0.794059 1.000000 0.27407
#> carb1 observed correlations -0.550925 0.526988 0.394977 0.749812 -0.090790 0.427606 -0.656249 -0.569607 0.057534 0.274073 1.00000
#> mpg2 residual correlations 0.167158 0.019501 0.019975 -0.025836 -0.007965 -0.059495 -0.016294 -0.024382 0.026298 -0.034185 -0.10037
#> cyl2 residual correlations 0.019501 0.069750 -0.002749 -0.009894 -0.027358 -0.034019 -0.030236 -0.034223 -0.010625 -0.042772 -0.02614
#> disp2 residual correlations 0.019975 -0.002749 0.095781 0.019475 0.016248 0.040964 0.000656 -0.003262 0.021837 -0.003986 -0.05989
#> hp2 residual correlations -0.025836 -0.009894 0.019475 0.142851 0.017490 0.023775 0.019553 0.075238 -0.006905 0.056591 0.08483
#> drat2 residual correlations -0.007965 -0.027358 0.016248 0.017490 0.297808 0.035760 0.017967 0.022666 -0.001196 0.033900 0.06091
#> wt2 residual correlations -0.059495 -0.034019 0.040964 0.023775 0.035760 0.167908 0.074700 0.021542 0.008199 0.060500 0.12358
#> qsec2 residual correlations -0.016294 -0.030236 0.000656 0.019553 0.017967 0.074700 0.150011 0.054724 -0.009464 0.040030 0.05942
#> vs2 residual correlations -0.024382 -0.034223 -0.003262 0.075238 0.022666 0.021542 0.054724 0.255826 -0.030020 0.057259 0.05754
#> am2 residual correlations 0.026298 -0.010625 0.021837 -0.006905 -0.001196 0.008199 -0.009464 -0.030020 0.170970 0.004478 -0.03321
#> gear2 residual correlations -0.034185 -0.042772 -0.003986 0.056591 0.033900 0.060500 0.040030 0.057259 0.004478 0.245676 0.15025
#> carb2 residual correlations -0.100367 -0.026136 -0.059892 0.084828 0.060913 0.123577 0.059423 0.057542 -0.033207 0.150246 0.38577
#>
#> $npobs
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg 32 32 32 32 32 32 32 32 32 32 32
#> cyl 32 32 32 32 32 32 32 32 32 32 32
#> disp 32 32 32 32 32 32 32 32 32 32 32
#> hp 32 32 32 32 32 32 32 32 32 32 32
#> drat 32 32 32 32 32 32 32 32 32 32 32
#> wt 32 32 32 32 32 32 32 32 32 32 32
#> qsec 32 32 32 32 32 32 32 32 32 32 32
#> vs 32 32 32 32 32 32 32 32 32 32 32
#> am 32 32 32 32 32 32 32 32 32 32 32
#> gear 32 32 32 32 32 32 32 32 32 32 32
#> carb 32 32 32 32 32 32 32 32 32 32 32
#>
#> $residual_stats
#> residual_statistics value critical formula
#> 1 Root Mean Squared Residual 0.04666 NA sqrt(mean(residuals^2))
#> 2 Number of absolute residuals > 0.05 15.00000 NA abs(residuals)>0.05
#> 3 Proportion of absolute residuals > 0.05 0.27273 0.5 numberLargeResiduals/nrow(residuals)
#>
#> $determinant_test
#> determinant above_critical
#> 1 0.0000002057 FALSE
#>
#> $bartlett_test
#> x_squared[bartlett] df[bartlett] p[bartlett]
#> 1 408 55 0.0000000000000000000000000000000000000000000000000000002227
#>
#> $kmo_test
#> Overall_MSA MSA Kaiser_1974
#> mpg 0.8266 0.9276 NA
#> cyl 0.8266 0.8966 NA
#> disp 0.8266 0.7647 NA
#> hp 0.8266 0.8386 NA
#> drat 0.8266 0.9497 NA
#> wt 0.8266 0.7410 NA
#> qsec 0.8266 0.7401 NA
#> vs 0.8266 0.9067 NA
#> am 0.8266 0.8778 NA
#> gear 0.8266 0.8470 NA
#> carb 0.8266 0.6211 NA
#>
#> $loadings
#> Matrix variable ML1 ML2 type row.names.model.Vaccounted.
#> 1 Pattern qsec -0.9600 -0.3200 <NA> <NA>
#> 2 Pattern hp 0.8500 -0.2000 <NA> <NA>
#> 3 Pattern carb 0.8100 0.1600 <NA> <NA>
#> 4 Pattern vs -0.8000 0.1600 <NA> <NA>
#> 5 Pattern cyl 0.6700 -0.5200 <NA> <NA>
#> 6 Pattern am 0.2000 0.9500 <NA> <NA>
#> 7 Pattern gear 0.2400 0.9100 <NA> <NA>
#> 8 Pattern drat -0.1300 0.7900 <NA> <NA>
#> 9 Pattern wt 0.3300 -0.7600 <NA> <NA>
#> 10 Pattern disp 0.5300 -0.6500 <NA> <NA>
#> 11 Pattern mpg -0.5300 0.6000 <NA> <NA>
#> 12 Structure hp 0.9100 -0.4400 <NA> <NA>
#> 13 Structure qsec -0.8700 -0.0500 <NA> <NA>
#> 14 Structure vs -0.8500 0.3900 <NA> <NA>
#> 15 Structure cyl 0.8200 -0.7200 <NA> <NA>
#> 16 Structure carb 0.7700 -0.0700 <NA> <NA>
#> 17 Structure am -0.0700 0.8900 <NA> <NA>
#> 18 Structure wt 0.5500 -0.8600 <NA> <NA>
#> 19 Structure gear -0.0200 0.8400 <NA> <NA>
#> 20 Structure drat -0.3500 0.8300 <NA> <NA>
#> 21 Structure disp 0.7200 -0.8000 <NA> <NA>
#> 22 Structure mpg -0.7100 0.7600 <NA> <NA>
#> 23 <NA> <NA> 4.4305 4.4200 variance accounted SS loadings
#> 24 <NA> <NA> 0.4028 0.4018 variance accounted Proportion Var
#> 25 <NA> <NA> 0.4028 0.8046 variance accounted Cumulative Var
#> 26 <NA> <NA> 0.5006 0.4994 variance accounted Proportion Explained
#> 27 <NA> <NA> 0.5006 1.0000 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
#> ML1 ML2
#> mpg -0.04829 0.09421
#> cyl 0.24216 -0.11404
#> disp 0.07217 -0.18703
#> hp 0.23861 0.09028
#> drat 0.05442 0.12138
#> wt -0.03544 -0.17393
#> qsec -0.35954 -0.27994
#> vs -0.12914 -0.05321
#> am 0.21026 0.30864
#> gear 0.14913 0.21073
#> carb 0.11072 0.07832
#>
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="minchi",oblique.scores=TRUE)
report_efa(model=model,df=mtcars)
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> [[1]]
#>
#> $correlations
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg reproduced correlations 0.832877 -0.872506 -0.8676820 -0.756337 0.685461 -0.806173 0.4413483 0.691031 0.566491 0.508724 -0.45690
#> cyl reproduced correlations -0.872506 0.927403 0.9049457 0.842042 -0.674237 0.820318 -0.5503165 -0.769431 -0.517153 -0.453058 0.55195
#> disp reproduced correlations -0.867682 0.904946 0.9051498 0.773005 -0.727278 0.847138 -0.4333586 -0.706231 -0.613088 -0.553982 0.45396
#> hp reproduced correlations -0.756337 0.842042 0.7730047 0.871538 -0.459605 0.642130 -0.7276042 -0.796638 -0.230998 -0.165243 0.68727
#> drat reproduced correlations 0.685461 -0.674237 -0.7272777 -0.459605 0.707740 -0.742803 0.0750648 0.419609 0.716139 0.680321 -0.13588
#> wt reproduced correlations -0.806173 0.820318 0.8471384 0.642130 -0.742803 0.824139 -0.2680282 -0.586513 -0.686369 -0.636930 0.30960
#> qsec reproduced correlations 0.441348 -0.550316 -0.4333586 -0.727604 0.075065 -0.268028 0.8121313 0.665404 -0.201311 -0.255450 -0.72402
#> vs reproduced correlations 0.691031 -0.769431 -0.7062310 -0.796638 0.419609 -0.586513 0.6654038 0.728176 0.210511 0.150408 -0.62845
#> am reproduced correlations 0.566491 -0.517153 -0.6130882 -0.230998 0.716139 -0.686369 -0.2013114 0.210511 0.820236 0.801349 0.10718
#> gear reproduced correlations 0.508724 -0.453058 -0.5539824 -0.165243 0.680321 -0.636930 -0.2554497 0.150408 0.801349 0.787425 0.15847
#> carb reproduced correlations -0.456899 0.551950 0.4539638 0.687273 -0.135877 0.309601 -0.7240241 -0.628451 0.107176 0.158472 0.65226
#> mpg1 observed correlations 1.000000 -0.852162 -0.8475514 -0.776168 0.681172 -0.867659 0.4186840 0.664039 0.599832 0.480285 -0.55093
#> cyl1 observed correlations -0.852162 1.000000 0.9020329 0.832447 -0.699938 0.782496 -0.5912421 -0.810812 -0.522607 -0.492687 0.52699
#> disp1 observed correlations -0.847551 0.902033 1.0000000 0.790949 -0.710214 0.887980 -0.4336979 -0.710416 -0.591227 -0.555569 0.39498
#> hp1 observed correlations -0.776168 0.832447 0.7909486 1.000000 -0.448759 0.658748 -0.7082234 -0.723097 -0.243204 -0.125704 0.74981
#> drat1 observed correlations 0.681172 -0.699938 -0.7102139 -0.448759 1.000000 -0.712441 0.0912048 0.440278 0.712711 0.699610 -0.09079
#> wt1 observed correlations -0.867659 0.782496 0.8879799 0.658748 -0.712441 1.000000 -0.1747159 -0.554916 -0.692495 -0.583287 0.42761
#> qsec1 observed correlations 0.418684 -0.591242 -0.4336979 -0.708223 0.091205 -0.174716 1.0000000 0.744535 -0.229861 -0.212682 -0.65625
#> vs1 observed correlations 0.664039 -0.810812 -0.7104159 -0.723097 0.440278 -0.554916 0.7445354 1.000000 0.168345 0.206023 -0.56961
#> am1 observed correlations 0.599832 -0.522607 -0.5912270 -0.243204 0.712711 -0.692495 -0.2298609 0.168345 1.000000 0.794059 0.05753
#> gear1 observed correlations 0.480285 -0.492687 -0.5555692 -0.125704 0.699610 -0.583287 -0.2126822 0.206023 0.794059 1.000000 0.27407
#> carb1 observed correlations -0.550925 0.526988 0.3949769 0.749812 -0.090790 0.427606 -0.6562492 -0.569607 0.057534 0.274073 1.00000
#> mpg2 residual correlations 0.167123 0.020344 0.0201306 -0.019831 -0.004289 -0.061486 -0.0226642 -0.026993 0.033342 -0.028439 -0.09403
#> cyl2 residual correlations 0.020344 0.072597 -0.0029128 -0.009594 -0.025701 -0.037822 -0.0409256 -0.041381 -0.005454 -0.039628 -0.02496
#> disp2 residual correlations 0.020131 -0.002913 0.0948502 0.017944 0.017064 0.040841 -0.0003393 -0.004185 0.021861 -0.001587 -0.05899
#> hp2 residual correlations -0.019831 -0.009594 0.0179439 0.128462 0.010846 0.016618 0.0193808 0.073542 -0.012206 0.039538 0.06254
#> drat2 residual correlations -0.004289 -0.025701 0.0170638 0.010846 0.292260 0.030363 0.0161399 0.020670 -0.003428 0.019290 0.04509
#> wt2 residual correlations -0.061486 -0.037822 0.0408415 0.016618 0.030363 0.175861 0.0933124 0.031597 -0.006126 0.053643 0.11800
#> qsec2 residual correlations -0.022664 -0.040926 -0.0003393 0.019381 0.016140 0.093312 0.1878687 0.079132 -0.028549 0.042768 0.06777
#> vs2 residual correlations -0.026993 -0.041381 -0.0041849 0.073542 0.020670 0.031597 0.0791316 0.271824 -0.042166 0.055616 0.05884
#> am2 residual correlations 0.033342 -0.005454 0.0218611 -0.012206 -0.003428 -0.006126 -0.0285494 -0.042166 0.179764 -0.007290 -0.04964
#> gear2 residual correlations -0.028439 -0.039628 -0.0015868 0.039538 0.019290 0.053643 0.0427675 0.055616 -0.007290 0.212575 0.11560
#> carb2 residual correlations -0.094026 -0.024962 -0.0589869 0.062539 0.045087 0.118005 0.0677748 0.058844 -0.049641 0.115600 0.34774
#>
#> $npobs
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg 32 32 32 32 32 32 32 32 32 32 32
#> cyl 32 32 32 32 32 32 32 32 32 32 32
#> disp 32 32 32 32 32 32 32 32 32 32 32
#> hp 32 32 32 32 32 32 32 32 32 32 32
#> drat 32 32 32 32 32 32 32 32 32 32 32
#> wt 32 32 32 32 32 32 32 32 32 32 32
#> qsec 32 32 32 32 32 32 32 32 32 32 32
#> vs 32 32 32 32 32 32 32 32 32 32 32
#> am 32 32 32 32 32 32 32 32 32 32 32
#> gear 32 32 32 32 32 32 32 32 32 32 32
#> carb 32 32 32 32 32 32 32 32 32 32 32
#>
#> $residual_stats
#> residual_statistics value critical formula
#> 1 Root Mean Squared Residual 0.04496 NA sqrt(mean(residuals^2))
#> 2 Number of absolute residuals > 0.05 13.00000 NA abs(residuals)>0.05
#> 3 Proportion of absolute residuals > 0.05 0.23636 0.5 numberLargeResiduals/nrow(residuals)
#>
#> $determinant_test
#> determinant above_critical
#> 1 0.0000002057 FALSE
#>
#> $bartlett_test
#> x_squared[bartlett] df[bartlett] p[bartlett]
#> 1 408 55 0.0000000000000000000000000000000000000000000000000000002227
#>
#> $kmo_test
#> Overall_MSA MSA Kaiser_1974
#> mpg 0.8266 0.9276 NA
#> cyl 0.8266 0.8966 NA
#> disp 0.8266 0.7647 NA
#> hp 0.8266 0.8386 NA
#> drat 0.8266 0.9497 NA
#> wt 0.8266 0.7410 NA
#> qsec 0.8266 0.7401 NA
#> vs 0.8266 0.9067 NA
#> am 0.8266 0.8778 NA
#> gear 0.8266 0.8470 NA
#> carb 0.8266 0.6211 NA
#>
#> $loadings
#> Matrix variable MC1 MC2 type row.names.model.Vaccounted.
#> 1 Pattern qsec -0.9400 -0.3300 <NA> <NA>
#> 2 Pattern hp 0.8700 -0.1600 <NA> <NA>
#> 3 Pattern carb 0.8400 0.2100 <NA> <NA>
#> 4 Pattern vs -0.8000 0.1500 <NA> <NA>
#> 5 Pattern cyl 0.6900 -0.5000 <NA> <NA>
#> 6 Pattern mpg -0.5700 0.5700 <NA> <NA>
#> 7 Pattern am 0.1700 0.9400 <NA> <NA>
#> 8 Pattern gear 0.2300 0.9200 <NA> <NA>
#> 9 Pattern drat -0.1500 0.7900 <NA> <NA>
#> 10 Pattern wt 0.3700 -0.7300 <NA> <NA>
#> 11 Pattern disp 0.5600 -0.6300 <NA> <NA>
#> 12 Structure hp 0.9200 -0.4100 <NA> <NA>
#> 13 Structure qsec -0.8400 -0.0600 <NA> <NA>
#> 14 Structure vs -0.8400 0.3800 <NA> <NA>
#> 15 Structure cyl 0.8300 -0.7000 <NA> <NA>
#> 16 Structure carb 0.7800 -0.0300 <NA> <NA>
#> 17 Structure mpg -0.7300 0.7300 <NA> <NA>
#> 18 Structure am -0.1000 0.8900 <NA> <NA>
#> 19 Structure gear -0.0300 0.8600 <NA> <NA>
#> 20 Structure drat -0.3700 0.8300 <NA> <NA>
#> 21 Structure wt 0.5800 -0.8300 <NA> <NA>
#> 22 Structure disp 0.7400 -0.7900 <NA> <NA>
#> 23 <NA> <NA> 4.5715 4.2976 variance accounted SS loadings
#> 24 <NA> <NA> 0.4156 0.3907 variance accounted Proportion Var
#> 25 <NA> <NA> 0.4156 0.8063 variance accounted Cumulative Var
#> 26 <NA> <NA> 0.5154 0.4846 variance accounted Proportion Explained
#> 27 <NA> <NA> 0.5154 1.0000 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
#> MC1 MC2
#> mpg -0.05811 0.08597
#> cyl 0.24455 -0.09996
#> disp 0.08401 -0.18491
#> hp 0.28482 0.12510
#> drat 0.05601 0.12733
#> wt -0.02271 -0.15460
#> qsec -0.27061 -0.21888
#> vs -0.12571 -0.05534
#> am 0.19854 0.30023
#> gear 0.18521 0.26599
#> carb 0.13757 0.10421
#>
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="minrank",oblique.scores=TRUE)
#> Loading required namespace: Rcsdp
report_efa(model=model,df=mtcars)
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> [[1]]
#>
#> $correlations
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg reproduced correlations 0.857299 -0.889911 -0.883728 -0.7704939 0.687986 -0.8343292 0.45051 0.705796 0.574760 0.506023 -0.49027
#> cyl reproduced correlations -0.889911 0.933583 0.911732 0.8421967 -0.675629 0.8450696 -0.54333 -0.770782 -0.530668 -0.455251 0.57845
#> disp reproduced correlations -0.883728 0.911732 0.914182 0.7700065 -0.731226 0.8720576 -0.42113 -0.705748 -0.630194 -0.561662 0.46563
#> hp reproduced correlations -0.770494 0.842197 0.770007 0.8755111 -0.451974 0.6591860 -0.73166 -0.798988 -0.231787 -0.152460 0.74082
#> drat reproduced correlations 0.687986 -0.675629 -0.731226 -0.4519741 0.703300 -0.7519535 0.06456 0.416755 0.720068 0.680856 -0.12061
#> wt reproduced correlations -0.834329 0.845070 0.872058 0.6591860 -0.751953 0.8568854 -0.27658 -0.605324 -0.700423 -0.642221 0.32843
#> qsec reproduced correlations 0.450514 -0.543333 -0.421127 -0.7316603 0.064558 -0.2765828 0.82011 0.664855 -0.206364 -0.273817 -0.79357
#> vs reproduced correlations 0.705796 -0.770782 -0.705748 -0.7989882 0.416755 -0.6053244 0.66486 0.729193 0.217003 0.144625 -0.67368
#> am reproduced correlations 0.574760 -0.530668 -0.630194 -0.2317873 0.720068 -0.7004229 -0.20636 0.217003 0.828413 0.809635 0.13837
#> gear reproduced correlations 0.506023 -0.455251 -0.561662 -0.1524603 0.680856 -0.6422206 -0.27382 0.144625 0.809635 0.798051 0.20647
#> carb reproduced correlations -0.490270 0.578448 0.465627 0.7408221 -0.120610 0.3284342 -0.79357 -0.673684 0.138373 0.206466 0.77273
#> mpg1 observed correlations 1.000000 -0.852162 -0.847551 -0.7761684 0.681172 -0.8676594 0.41868 0.664039 0.599832 0.480285 -0.55093
#> cyl1 observed correlations -0.852162 1.000000 0.902033 0.8324475 -0.699938 0.7824958 -0.59124 -0.810812 -0.522607 -0.492687 0.52699
#> disp1 observed correlations -0.847551 0.902033 1.000000 0.7909486 -0.710214 0.8879799 -0.43370 -0.710416 -0.591227 -0.555569 0.39498
#> hp1 observed correlations -0.776168 0.832447 0.790949 1.0000000 -0.448759 0.6587479 -0.70822 -0.723097 -0.243204 -0.125704 0.74981
#> drat1 observed correlations 0.681172 -0.699938 -0.710214 -0.4487591 1.000000 -0.7124406 0.09120 0.440278 0.712711 0.699610 -0.09079
#> wt1 observed correlations -0.867659 0.782496 0.887980 0.6587479 -0.712441 1.0000000 -0.17472 -0.554916 -0.692495 -0.583287 0.42761
#> qsec1 observed correlations 0.418684 -0.591242 -0.433698 -0.7082234 0.091205 -0.1747159 1.00000 0.744535 -0.229861 -0.212682 -0.65625
#> vs1 observed correlations 0.664039 -0.810812 -0.710416 -0.7230967 0.440278 -0.5549157 0.74454 1.000000 0.168345 0.206023 -0.56961
#> am1 observed correlations 0.599832 -0.522607 -0.591227 -0.2432043 0.712711 -0.6924953 -0.22986 0.168345 1.000000 0.794059 0.05753
#> gear1 observed correlations 0.480285 -0.492687 -0.555569 -0.1257043 0.699610 -0.5832870 -0.21268 0.206023 0.794059 1.000000 0.27407
#> carb1 observed correlations -0.550925 0.526988 0.394977 0.7498125 -0.090790 0.4276059 -0.65625 -0.569607 0.057534 0.274073 1.00000
#> mpg2 residual correlations 0.142701 0.037749 0.036176 -0.0056745 -0.006814 -0.0333302 -0.03183 -0.041757 0.025073 -0.025738 -0.06066
#> cyl2 residual correlations 0.037749 0.066417 -0.009699 -0.0097493 -0.024309 -0.0625738 -0.04791 -0.040030 0.008061 -0.037436 -0.05146
#> disp2 residual correlations 0.036176 -0.009699 0.085818 0.0209420 0.021012 0.0159223 -0.01257 -0.004668 0.038967 0.006093 -0.07065
#> hp2 residual correlations -0.005674 -0.009749 0.020942 0.1244889 0.003215 -0.0004381 0.02344 0.075891 -0.011417 0.026756 0.00899
#> drat2 residual correlations -0.006814 -0.024309 0.021012 0.0032150 0.296700 0.0395128 0.02665 0.023523 -0.007357 0.018754 0.02982
#> wt2 residual correlations -0.033330 -0.062574 0.015922 -0.0004381 0.039513 0.1431146 0.10187 0.050409 0.007928 0.058934 0.09917
#> qsec2 residual correlations -0.031830 -0.047909 -0.012571 0.0234369 0.026646 0.1018669 0.17989 0.079680 -0.023497 0.061134 0.13732
#> vs2 residual correlations -0.041757 -0.040030 -0.004668 0.0758915 0.023523 0.0504087 0.07968 0.270807 -0.048658 0.061398 0.10408
#> am2 residual correlations 0.025073 0.008061 0.038967 -0.0114169 -0.007357 0.0079276 -0.02350 -0.048658 0.171587 -0.015576 -0.08084
#> gear2 residual correlations -0.025738 -0.037436 0.006093 0.0267561 0.018754 0.0589336 0.06113 0.061398 -0.015576 0.201949 0.06761
#> carb2 residual correlations -0.060655 -0.051460 -0.070650 0.0089904 0.029820 0.0991717 0.13732 0.104077 -0.080838 0.067607 0.22727
#>
#> $npobs
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg 32 32 32 32 32 32 32 32 32 32 32
#> cyl 32 32 32 32 32 32 32 32 32 32 32
#> disp 32 32 32 32 32 32 32 32 32 32 32
#> hp 32 32 32 32 32 32 32 32 32 32 32
#> drat 32 32 32 32 32 32 32 32 32 32 32
#> wt 32 32 32 32 32 32 32 32 32 32 32
#> qsec 32 32 32 32 32 32 32 32 32 32 32
#> vs 32 32 32 32 32 32 32 32 32 32 32
#> am 32 32 32 32 32 32 32 32 32 32 32
#> gear 32 32 32 32 32 32 32 32 32 32 32
#> carb 32 32 32 32 32 32 32 32 32 32 32
#>
#> $residual_stats
#> residual_statistics value critical formula
#> 1 Root Mean Squared Residual 0.04818 NA sqrt(mean(residuals^2))
#> 2 Number of absolute residuals > 0.05 16.00000 NA abs(residuals)>0.05
#> 3 Proportion of absolute residuals > 0.05 0.29091 0.5 numberLargeResiduals/nrow(residuals)
#>
#> $determinant_test
#> determinant above_critical
#> 1 0.0000002057 FALSE
#>
#> $bartlett_test
#> x_squared[bartlett] df[bartlett] p[bartlett]
#> 1 408 55 0.0000000000000000000000000000000000000000000000000000002227
#>
#> $kmo_test
#> Overall_MSA MSA Kaiser_1974
#> mpg 0.8266 0.9276 NA
#> cyl 0.8266 0.8966 NA
#> disp 0.8266 0.7647 NA
#> hp 0.8266 0.8386 NA
#> drat 0.8266 0.9497 NA
#> wt 0.8266 0.7410 NA
#> qsec 0.8266 0.7401 NA
#> vs 0.8266 0.9067 NA
#> am 0.8266 0.8778 NA
#> gear 0.8266 0.8470 NA
#> carb 0.8266 0.6211 NA
#>
#> $loadings
#> Matrix variable MRFA2 MRFA1 type row.names.model.Vaccounted.
#> 1 Pattern am 0.9400 0.2200 <NA> <NA>
#> 2 Pattern gear 0.9300 0.3000 <NA> <NA>
#> 3 Pattern drat 0.8100 -0.0900 <NA> <NA>
#> 4 Pattern wt -0.7800 0.3300 <NA> <NA>
#> 5 Pattern disp -0.6900 0.5000 <NA> <NA>
#> 6 Pattern mpg 0.6300 -0.5300 <NA> <NA>
#> 7 Pattern qsec -0.2700 -0.9400 <NA> <NA>
#> 8 Pattern carb 0.1900 0.9100 <NA> <NA>
#> 9 Pattern hp -0.2300 0.8500 <NA> <NA>
#> 10 Pattern vs 0.2200 -0.7700 <NA> <NA>
#> 11 Pattern cyl -0.5800 0.6400 <NA> <NA>
#> 12 Structure am 0.8800 -0.0300 <NA> <NA>
#> 13 Structure wt -0.8700 0.5400 <NA> <NA>
#> 14 Structure gear 0.8500 0.0500 <NA> <NA>
#> 15 Structure disp -0.8300 0.6800 <NA> <NA>
#> 16 Structure drat 0.8300 -0.3100 <NA> <NA>
#> 17 Structure mpg 0.7700 -0.7000 <NA> <NA>
#> 18 Structure hp -0.4600 0.9100 <NA> <NA>
#> 19 Structure qsec -0.0100 -0.8700 <NA> <NA>
#> 20 Structure carb -0.0600 0.8600 <NA> <NA>
#> 21 Structure vs 0.4200 -0.8300 <NA> <NA>
#> 22 Structure cyl -0.7500 0.7900 <NA> <NA>
#> 23 <NA> <NA> 4.6556 4.4336 variance accounted SS loadings
#> 24 <NA> <NA> 0.4232 0.4031 variance accounted Proportion Var
#> 25 <NA> <NA> 0.4232 0.8263 variance accounted Cumulative Var
#> 26 <NA> <NA> 0.5122 0.4878 variance accounted Proportion Explained
#> 27 <NA> <NA> 0.5122 1.0000 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
#> MRFA2 MRFA1
#> mpg 0.12574 -0.03050
#> cyl -0.31638 0.06544
#> disp -0.02264 0.26953
#> hp 0.04373 0.10023
#> drat 0.05627 0.01672
#> wt -0.34379 -0.17444
#> qsec -0.20538 -0.28699
#> vs -0.04278 -0.09511
#> am 0.26626 0.19282
#> gear 0.12166 0.06904
#> carb 0.34645 0.48165
#>
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="old.min",oblique.scores=TRUE)
report_efa(model=model,df=mtcars)
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> $correlation_loadings
#>
#> $plot_barplot
#>
#> [[1]]
#>
#> $correlations
#> type mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg reproduced correlations 0.847173 -0.8839678 -0.871480 -0.765672 0.682946 -0.81407 0.44743 0.69483 0.5655512 0.499096 -0.47106
#> cyl reproduced correlations -0.883968 0.9326071 0.904044 0.842179 -0.673037 0.82879 -0.54251 -0.76315 -0.5228804 -0.449176 0.55522
#> disp reproduced correlations -0.871480 0.9040442 0.899211 0.765326 -0.722957 0.84808 -0.42124 -0.69509 -0.6164656 -0.550355 0.45171
#> hp reproduced correlations -0.765672 0.8421792 0.765326 0.874591 -0.450203 0.64863 -0.72371 -0.78898 -0.2273294 -0.148850 0.69466
#> drat reproduced correlations 0.682946 -0.6730371 -0.722957 -0.450203 0.703381 -0.73597 0.06855 0.41284 0.7155789 0.678856 -0.13371
#> wt reproduced correlations -0.814070 0.8287870 0.848075 0.648627 -0.735972 0.82384 -0.27757 -0.59085 -0.6788875 -0.623818 0.32432
#> qsec reproduced correlations 0.447430 -0.5425085 -0.421240 -0.723707 0.068548 -0.27757 0.79478 0.64855 -0.1976830 -0.264993 -0.71912
#> vs reproduced correlations 0.694827 -0.7631453 -0.695086 -0.788981 0.412836 -0.59085 0.64855 0.71185 0.2135815 0.142836 -0.62348
#> am reproduced correlations 0.565551 -0.5228804 -0.616466 -0.227329 0.715579 -0.67889 -0.19768 0.21358 0.8187284 0.802994 0.10356
#> gear reproduced correlations 0.499096 -0.4491757 -0.550355 -0.148850 0.678856 -0.62382 -0.26499 0.14284 0.8029944 0.794331 0.16764
#> carb reproduced correlations -0.471057 0.5552227 0.451705 0.694661 -0.133706 0.32432 -0.71912 -0.62348 0.1035640 0.167643 0.65802
#> mpg1 observed correlations 1.000000 -0.8521620 -0.847551 -0.776168 0.681172 -0.86766 0.41868 0.66404 0.5998324 0.480285 -0.55093
#> cyl1 observed correlations -0.852162 1.0000000 0.902033 0.832447 -0.699938 0.78250 -0.59124 -0.81081 -0.5226070 -0.492687 0.52699
#> disp1 observed correlations -0.847551 0.9020329 1.000000 0.790949 -0.710214 0.88798 -0.43370 -0.71042 -0.5912270 -0.555569 0.39498
#> hp1 observed correlations -0.776168 0.8324475 0.790949 1.000000 -0.448759 0.65875 -0.70822 -0.72310 -0.2432043 -0.125704 0.74981
#> drat1 observed correlations 0.681172 -0.6999381 -0.710214 -0.448759 1.000000 -0.71244 0.09120 0.44028 0.7127111 0.699610 -0.09079
#> wt1 observed correlations -0.867659 0.7824958 0.887980 0.658748 -0.712441 1.00000 -0.17472 -0.55492 -0.6924953 -0.583287 0.42761
#> qsec1 observed correlations 0.418684 -0.5912421 -0.433698 -0.708223 0.091205 -0.17472 1.00000 0.74454 -0.2298609 -0.212682 -0.65625
#> vs1 observed correlations 0.664039 -0.8108118 -0.710416 -0.723097 0.440278 -0.55492 0.74454 1.00000 0.1683451 0.206023 -0.56961
#> am1 observed correlations 0.599832 -0.5226070 -0.591227 -0.243204 0.712711 -0.69250 -0.22986 0.16835 1.0000000 0.794059 0.05753
#> gear1 observed correlations 0.480285 -0.4926866 -0.555569 -0.125704 0.699610 -0.58329 -0.21268 0.20602 0.7940588 1.000000 0.27407
#> carb1 observed correlations -0.550925 0.5269883 0.394977 0.749812 -0.090790 0.42761 -0.65625 -0.56961 0.0575344 0.274073 1.00000
#> mpg2 residual correlations 0.152827 0.0318058 0.023928 -0.010496 -0.001774 -0.05359 -0.02875 -0.03079 0.0342812 -0.018811 -0.07987
#> cyl2 residual correlations 0.031806 0.0673929 -0.002011 -0.009732 -0.026901 -0.04629 -0.04873 -0.04767 0.0002734 -0.043511 -0.02823
#> disp2 residual correlations 0.023928 -0.0020113 0.100789 0.025623 0.012743 0.03990 -0.01246 -0.01533 0.0252386 -0.005215 -0.05673
#> hp2 residual correlations -0.010496 -0.0097317 0.025623 0.125409 0.001444 0.01012 0.01548 0.06588 -0.0158749 0.023145 0.05515
#> drat2 residual correlations -0.001774 -0.0269010 0.012743 0.001444 0.296619 0.02353 0.02266 0.02744 -0.0028677 0.020754 0.04292
#> wt2 residual correlations -0.053590 -0.0462912 0.039905 0.010121 0.023531 0.17616 0.10285 0.03593 -0.0136078 0.040531 0.10328
#> qsec2 residual correlations -0.028746 -0.0487336 -0.012458 0.015483 0.022657 0.10285 0.20522 0.09599 -0.0321779 0.052310 0.06287
#> vs2 residual correlations -0.030788 -0.0476665 -0.015330 0.065884 0.027443 0.03593 0.09599 0.28815 -0.0452364 0.063187 0.05388
#> am2 residual correlations 0.034281 0.0002734 0.025239 -0.015875 -0.002868 -0.01361 -0.03218 -0.04524 0.1812716 -0.008936 -0.04603
#> gear2 residual correlations -0.018811 -0.0435109 -0.005215 0.023145 0.020754 0.04053 0.05231 0.06319 -0.0089356 0.205669 0.10643
#> carb2 residual correlations -0.079868 -0.0282344 -0.056728 0.055152 0.042916 0.10328 0.06287 0.05388 -0.0460296 0.106430 0.34198
#>
#> $npobs
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> mpg 32 32 32 32 32 32 32 32 32 32 32
#> cyl 32 32 32 32 32 32 32 32 32 32 32
#> disp 32 32 32 32 32 32 32 32 32 32 32
#> hp 32 32 32 32 32 32 32 32 32 32 32
#> drat 32 32 32 32 32 32 32 32 32 32 32
#> wt 32 32 32 32 32 32 32 32 32 32 32
#> qsec 32 32 32 32 32 32 32 32 32 32 32
#> vs 32 32 32 32 32 32 32 32 32 32 32
#> am 32 32 32 32 32 32 32 32 32 32 32
#> gear 32 32 32 32 32 32 32 32 32 32 32
#> carb 32 32 32 32 32 32 32 32 32 32 32
#>
#> $residual_stats
#> residual_statistics value critical formula
#> 1 Root Mean Squared Residual 0.04424 NA sqrt(mean(residuals^2))
#> 2 Number of absolute residuals > 0.05 13.00000 NA abs(residuals)>0.05
#> 3 Proportion of absolute residuals > 0.05 0.23636 0.5 numberLargeResiduals/nrow(residuals)
#>
#> $determinant_test
#> determinant above_critical
#> 1 0.0000002057 FALSE
#>
#> $bartlett_test
#> x_squared[bartlett] df[bartlett] p[bartlett]
#> 1 408 55 0.0000000000000000000000000000000000000000000000000000002227
#>
#> $kmo_test
#> Overall_MSA MSA Kaiser_1974
#> mpg 0.8266 0.9276 NA
#> cyl 0.8266 0.8966 NA
#> disp 0.8266 0.7647 NA
#> hp 0.8266 0.8386 NA
#> drat 0.8266 0.9497 NA
#> wt 0.8266 0.7410 NA
#> qsec 0.8266 0.7401 NA
#> vs 0.8266 0.9067 NA
#> am 0.8266 0.8778 NA
#> gear 0.8266 0.8470 NA
#> carb 0.8266 0.6211 NA
#>
#> $loadings
#> Matrix variable oldmin1 oldmin2 type row.names.model.Vaccounted.
#> 1 Pattern qsec -0.9300 -0.3400 <NA> <NA>
#> 2 Pattern hp 0.8900 -0.1400 <NA> <NA>
#> 3 Pattern carb 0.8500 0.2300 <NA> <NA>
#> 4 Pattern vs -0.8000 0.1300 <NA> <NA>
#> 5 Pattern cyl 0.7000 -0.4900 <NA> <NA>
#> 6 Pattern mpg -0.6000 0.5500 <NA> <NA>
#> 7 Pattern am 0.1400 0.9400 <NA> <NA>
#> 8 Pattern gear 0.2300 0.9300 <NA> <NA>
#> 9 Pattern drat -0.1600 0.7800 <NA> <NA>
#> 10 Pattern wt 0.4100 -0.7100 <NA> <NA>
#> 11 Pattern disp 0.5700 -0.6100 <NA> <NA>
#> 12 Structure hp 0.9300 -0.3900 <NA> <NA>
#> 13 Structure cyl 0.8400 -0.6900 <NA> <NA>
#> 14 Structure qsec -0.8300 -0.0800 <NA> <NA>
#> 15 Structure vs -0.8300 0.3600 <NA> <NA>
#> 16 Structure carb 0.7800 -0.0100 <NA> <NA>
#> 17 Structure mpg -0.7500 0.7200 <NA> <NA>
#> 18 Structure am -0.1200 0.8900 <NA> <NA>
#> 19 Structure gear -0.0400 0.8600 <NA> <NA>
#> 20 Structure drat -0.3800 0.8200 <NA> <NA>
#> 21 Structure wt 0.6100 -0.8200 <NA> <NA>
#> 22 Structure disp 0.7400 -0.7700 <NA> <NA>
#> 23 <NA> <NA> 4.6601 4.1984 variance accounted SS loadings
#> 24 <NA> <NA> 0.4236 0.3817 variance accounted Proportion Var
#> 25 <NA> <NA> 0.4236 0.8053 variance accounted Cumulative Var
#> 26 <NA> <NA> 0.5261 0.4739 variance accounted Proportion Explained
#> 27 <NA> <NA> 0.5261 1.0000 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
#> oldmin1 oldmin2
#> mpg -0.09760 0.11552
#> cyl 0.38551 -0.11616
#> disp -0.24251 -0.45299
#> hp 0.39093 0.27781
#> drat 0.09590 0.13713
#> wt 0.25525 0.09710
#> qsec -0.30211 -0.29923
#> vs -0.10511 -0.07628
#> am 0.19842 0.28468
#> gear 0.27461 0.29736
#> carb -0.05401 -0.04296
#>
model<-psych::fa(mtcars,nfactors=2,rotate="oblimin",fm="alpha",oblique.scores=TRUE)
#report_efa(model=model,df=mtcars)