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## Please cite as:
## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.2.14
## Current Matrix version is 1.2.12
## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' package
## Learn more about sjPlot with 'browseVignettes("sjPlot")'.
Redoing Aim 4.1 EFA without Sluggish
indiv_means_noslug <- all_70[c("anxious_mean", "nervous_mean", "upset_mean",
"irritable_mean", "content_mean", "relaxed_mean", "excited_mean",
"happy_mean", "attentive_mean")]
indiv_means_noslug <- data.frame(indiv_means_noslug)
View(indiv_means_noslug)
indiv_means_noslug_cor <- cor(indiv_means_noslug)
PCA for item means using an oblique rotation
means.pca.oblique.2 <- principal(indiv_means_noslug, nfactors = 1, rotate = "oblimin")
means.pca.oblique.2
## Principal Components Analysis
## Call: principal(r = indiv_means_noslug, nfactors = 1, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PC1 h2 u2 com
## anxious_mean -0.78 0.62 0.38 1
## nervous_mean -0.80 0.64 0.36 1
## upset_mean -0.83 0.69 0.31 1
## irritable_mean -0.82 0.68 0.32 1
## content_mean 0.86 0.74 0.26 1
## relaxed_mean 0.79 0.62 0.38 1
## excited_mean 0.56 0.31 0.69 1
## happy_mean 0.84 0.70 0.30 1
## attentive_mean 0.56 0.31 0.69 1
##
## PC1
## SS loadings 5.31
## Proportion Var 0.59
##
## Mean item complexity = 1
## Test of the hypothesis that 1 component is sufficient.
##
## The root mean square of the residuals (RMSR) is 0.19
## with the empirical chi square 355.01 with prob < 6.7e-59
##
## Fit based upon off diagonal values = 0.88
summary(means.pca.oblique.2)
##
## Factor analysis with Call: principal(r = indiv_means_noslug, nfactors = 1, rotate = "oblimin")
##
## Test of the hypothesis that 1 factor is sufficient.
## The degrees of freedom for the model is 27 and the objective function was 5.74
## The number of observations was 130 with Chi Square = 714.71 with prob < 3.2e-133
##
## The root mean square of the residuals (RMSA) is 0.19
means.pca.oblique2.2 <- principal(indiv_means_noslug, nfactors = 2, rotate = "oblimin")
means.pca.oblique2.2
## Principal Components Analysis
## Call: principal(r = indiv_means_noslug, nfactors = 2, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC2 h2 u2 com
## anxious_mean 0.95 0.04 0.87 0.13 1.0
## nervous_mean 0.97 0.05 0.91 0.09 1.0
## upset_mean 0.89 -0.08 0.85 0.15 1.0
## irritable_mean 0.86 -0.10 0.82 0.18 1.0
## content_mean -0.27 0.79 0.86 0.14 1.2
## relaxed_mean -0.40 0.57 0.65 0.35 1.8
## excited_mean 0.25 0.98 0.83 0.17 1.1
## happy_mean -0.19 0.85 0.88 0.12 1.1
## attentive_mean 0.05 0.76 0.55 0.45 1.0
##
## TC1 TC2
## SS loadings 3.86 3.37
## Proportion Var 0.43 0.37
## Cumulative Var 0.43 0.80
## Proportion Explained 0.53 0.47
## Cumulative Proportion 0.53 1.00
##
## With component correlations of
## TC1 TC2
## TC1 1.00 -0.37
## TC2 -0.37 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.07
## with the empirical chi square 39.88 with prob < 0.0034
##
## Fit based upon off diagonal values = 0.99
summary(means.pca.oblique2.2)
##
## Factor analysis with Call: principal(r = indiv_means_noslug, nfactors = 2, rotate = "oblimin")
##
## Test of the hypothesis that 2 factors are sufficient.
## The degrees of freedom for the model is 19 and the objective function was 2.49
## The number of observations was 130 with Chi Square = 307.8 with prob < 5e-54
##
## The root mean square of the residuals (RMSA) is 0.07
##
## With component correlations of
## TC1 TC2
## TC1 1.00 -0.37
## TC2 -0.37 1.00
means.pca.oblique3.2 <- principal(indiv_means_noslug, nfactors = 3, rotate = "oblimin")
means.pca.oblique3.2
## Principal Components Analysis
## Call: principal(r = indiv_means_noslug, nfactors = 3, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC2 TC3 h2 u2 com
## anxious_mean 0.86 -0.14 0.23 0.89 0.106 1.2
## nervous_mean 0.95 0.00 0.06 0.91 0.089 1.0
## upset_mean 0.93 -0.01 -0.12 0.88 0.120 1.0
## irritable_mean 0.92 0.03 -0.21 0.87 0.127 1.1
## content_mean -0.22 0.76 0.15 0.87 0.134 1.3
## relaxed_mean -0.19 0.87 -0.33 0.87 0.134 1.4
## excited_mean 0.28 0.89 0.23 0.84 0.160 1.3
## happy_mean -0.15 0.80 0.17 0.88 0.117 1.2
## attentive_mean -0.15 0.27 0.77 0.85 0.155 1.3
##
## TC1 TC2 TC3
## SS loadings 3.72 3.12 1.02
## Proportion Var 0.41 0.35 0.11
## Cumulative Var 0.41 0.76 0.87
## Proportion Explained 0.47 0.40 0.13
## Cumulative Proportion 0.47 0.87 1.00
##
## With component correlations of
## TC1 TC2 TC3
## TC1 1.00 -0.43 0.00
## TC2 -0.43 1.00 0.31
## TC3 0.00 0.31 1.00
##
## Mean item complexity = 1.2
## Test of the hypothesis that 3 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.06
## with the empirical chi square 29.06 with prob < 0.0039
##
## Fit based upon off diagonal values = 0.99
summary(means.pca.oblique3.2)
##
## Factor analysis with Call: principal(r = indiv_means_noslug, nfactors = 3, rotate = "oblimin")
##
## Test of the hypothesis that 3 factors are sufficient.
## The degrees of freedom for the model is 12 and the objective function was 2.85
## The number of observations was 130 with Chi Square = 350.97 with prob < 8.7e-68
##
## The root mean square of the residuals (RMSA) is 0.06
##
## With component correlations of
## TC1 TC2 TC3
## TC1 1.00 -0.43 0.00
## TC2 -0.43 1.00 0.31
## TC3 0.00 0.31 1.00
means.pca.oblique4.2 <- principal(indiv_means_noslug, nfactors = 4, rotate = "oblimin")
means.pca.oblique4.2
## Principal Components Analysis
## Call: principal(r = indiv_means_noslug, nfactors = 4, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC2 TC3 TC4 h2 u2 com
## anxious_mean 0.88 0.02 0.01 -0.33 0.95 0.053 1.3
## nervous_mean 0.95 0.11 -0.12 -0.20 0.95 0.047 1.1
## upset_mean 0.88 -0.23 0.06 0.26 0.96 0.045 1.3
## irritable_mean 0.88 -0.13 -0.09 0.23 0.90 0.099 1.2
## content_mean -0.19 0.86 0.03 -0.06 0.92 0.080 1.1
## relaxed_mean -0.23 0.61 -0.04 0.56 0.93 0.072 2.3
## excited_mean 0.31 0.81 0.26 0.11 0.84 0.160 1.6
## happy_mean -0.11 0.90 0.05 -0.06 0.93 0.066 1.0
## attentive_mean -0.07 0.03 0.96 -0.03 0.98 0.015 1.0
##
## TC1 TC2 TC3 TC4
## SS loadings 3.59 2.95 1.18 0.64
## Proportion Var 0.40 0.33 0.13 0.07
## Cumulative Var 0.40 0.73 0.86 0.93
## Proportion Explained 0.43 0.35 0.14 0.08
## Cumulative Proportion 0.43 0.78 0.92 1.00
##
## With component correlations of
## TC1 TC2 TC3 TC4
## TC1 1.00 -0.38 -0.15 -0.14
## TC2 -0.38 1.00 0.50 0.11
## TC3 -0.15 0.50 1.00 0.05
## TC4 -0.14 0.11 0.05 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 4 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.03
## with the empirical chi square 7.63 with prob < 0.27
##
## Fit based upon off diagonal values = 1
summary(means.pca.oblique4.2)
##
## Factor analysis with Call: principal(r = indiv_means_noslug, nfactors = 4, rotate = "oblimin")
##
## Test of the hypothesis that 4 factors are sufficient.
## The degrees of freedom for the model is 6 and the objective function was 1.53
## The number of observations was 130 with Chi Square = 187.09 with prob < 1.1e-37
##
## The root mean square of the residuals (RMSA) is 0.03
##
## With component correlations of
## TC1 TC2 TC3 TC4
## TC1 1.00 -0.38 -0.15 -0.14
## TC2 -0.38 1.00 0.50 0.11
## TC3 -0.15 0.50 1.00 0.05
## TC4 -0.14 0.11 0.05 1.00
means.pca.oblique5.2 <- principal(indiv_means_noslug, nfactors = 5, rotate = "oblimin")
means.pca.oblique5.2
## Principal Components Analysis
## Call: principal(r = indiv_means_noslug, nfactors = 5, rotate = "oblimin")
##
## Warning: A Heywood case was detected.
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC4 TC2 TC3 TC5 h2 u2 com
## anxious_mean 0.84 -0.23 -0.07 0.10 0.31 0.97 0.03064 1.5
## nervous_mean 0.86 -0.18 0.12 -0.10 0.20 0.95 0.04571 1.3
## upset_mean 0.91 0.01 0.04 0.00 -0.28 0.96 0.04348 1.2
## irritable_mean 0.96 0.16 -0.09 -0.07 -0.13 0.91 0.09070 1.1
## content_mean -0.17 0.46 0.22 0.14 0.47 0.94 0.05553 2.9
## relaxed_mean -0.01 0.95 0.03 0.04 0.00 0.96 0.03685 1.0
## excited_mean 0.03 -0.01 1.00 0.03 -0.05 1.00 0.00480 1.0
## happy_mean -0.15 0.38 0.36 0.11 0.42 0.94 0.06136 3.4
## attentive_mean 0.01 -0.01 0.00 1.01 -0.04 1.00 0.00078 1.0
##
## TC1 TC4 TC2 TC3 TC5
## SS loadings 3.49 1.70 1.47 1.22 0.74
## Proportion Var 0.39 0.19 0.16 0.14 0.08
## Cumulative Var 0.39 0.58 0.74 0.88 0.96
## Proportion Explained 0.40 0.20 0.17 0.14 0.09
## Cumulative Proportion 0.40 0.60 0.77 0.91 1.00
##
## With component correlations of
## TC1 TC4 TC2 TC3 TC5
## TC1 1.00 -0.52 -0.16 -0.26 -0.13
## TC4 -0.52 1.00 0.51 0.34 0.04
## TC2 -0.16 0.51 1.00 0.58 0.32
## TC3 -0.26 0.34 0.58 1.00 0.22
## TC5 -0.13 0.04 0.32 0.22 1.00
##
## Mean item complexity = 1.6
## Test of the hypothesis that 5 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.02
## with the empirical chi square 3.22 with prob < 0.073
##
## Fit based upon off diagonal values = 1
summary(means.pca.oblique5.2)
##
## Factor analysis with Call: principal(r = indiv_means_noslug, nfactors = 5, rotate = "oblimin")
##
## Test of the hypothesis that 5 factors are sufficient.
## The degrees of freedom for the model is 1 and the objective function was 1.45
## The number of observations was 130 with Chi Square = 176.73 with prob < 2.5e-40
##
## The root mean square of the residuals (RMSA) is 0.02
##
## With component correlations of
## TC1 TC4 TC2 TC3 TC5
## TC1 1.00 -0.52 -0.16 -0.26 -0.13
## TC4 -0.52 1.00 0.51 0.34 0.04
## TC2 -0.16 0.51 1.00 0.58 0.32
## TC3 -0.26 0.34 0.58 1.00 0.22
## TC5 -0.13 0.04 0.32 0.22 1.00
EFA for the item means using fa()
mean.pca.oblimin.2 <- fa(r = indiv_means_noslug, nfactors = 1, rotate = "oblimin")
mean.pca.oblimin.2
## Factor Analysis using method = minres
## Call: fa(r = indiv_means_noslug, nfactors = 1, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## anxious_mean -0.75 0.57 0.43 1
## nervous_mean -0.77 0.59 0.41 1
## upset_mean -0.82 0.67 0.33 1
## irritable_mean -0.80 0.65 0.35 1
## content_mean 0.84 0.71 0.29 1
## relaxed_mean 0.75 0.57 0.43 1
## excited_mean 0.50 0.25 0.75 1
## happy_mean 0.81 0.65 0.35 1
## attentive_mean 0.49 0.24 0.76 1
##
## MR1
## SS loadings 4.90
## Proportion Var 0.54
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 27 and the objective function was 5.68
##
## The root mean square of the residuals (RMSR) is 0.19
## The df corrected root mean square of the residuals is 0.22
##
## The harmonic number of observations is 117 with the empirical chi square 296.94 with prob < 3e-47
## The total number of observations was 130 with Likelihood Chi Square = 706.98 with prob < 1.3e-131
##
## Tucker Lewis Index of factoring reliability = 0.295
## RMSEA index = 0.45 and the 90 % confidence intervals are 0.414 0.47
## BIC = 575.56
## Fit based upon off diagonal values = 0.89
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.96
## Multiple R square of scores with factors 0.93
## Minimum correlation of possible factor scores 0.86
print(mean.pca.oblimin.2$loadings, cutoff=0.3)
##
## Loadings:
## MR1
## anxious_mean -0.755
## nervous_mean -0.771
## upset_mean -0.816
## irritable_mean -0.804
## content_mean 0.841
## relaxed_mean 0.754
## excited_mean 0.496
## happy_mean 0.809
## attentive_mean 0.495
##
## MR1
## SS loadings 4.898
## Proportion Var 0.544
mean.pca.oblimin2.2 <- fa(r = indiv_means_noslug, nfactors = 2, rotate = "oblimin")
mean.pca.oblimin2.2
## Factor Analysis using method = minres
## Call: fa(r = indiv_means_noslug, nfactors = 2, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## anxious_mean 0.92 0.03 0.83 0.168 1.0
## nervous_mean 0.98 0.06 0.92 0.077 1.0
## upset_mean 0.85 -0.10 0.81 0.194 1.0
## irritable_mean 0.81 -0.12 0.76 0.244 1.0
## content_mean -0.22 0.82 0.87 0.135 1.1
## relaxed_mean -0.35 0.54 0.57 0.430 1.7
## excited_mean 0.25 0.94 0.75 0.250 1.1
## happy_mean -0.14 0.88 0.90 0.098 1.0
## attentive_mean 0.00 0.62 0.39 0.613 1.0
##
## MR1 MR2
## SS loadings 3.61 3.18
## Proportion Var 0.40 0.35
## Cumulative Var 0.40 0.75
## Proportion Explained 0.53 0.47
## Cumulative Proportion 0.53 1.00
##
## With factor correlations of
## MR1 MR2
## MR1 1.00 -0.41
## MR2 -0.41 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 19 and the objective function was 2.14
##
## The root mean square of the residuals (RMSR) is 0.05
## The df corrected root mean square of the residuals is 0.07
##
## The harmonic number of observations is 117 with the empirical chi square 20.26 with prob < 0.38
## The total number of observations was 130 with Likelihood Chi Square = 264.46 with prob < 3.6e-45
##
## Tucker Lewis Index of factoring reliability = 0.636
## RMSEA index = 0.323 and the 90 % confidence intervals are 0.283 0.351
## BIC = 171.98
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR2
## Correlation of (regression) scores with factors 0.99 0.98
## Multiple R square of scores with factors 0.97 0.95
## Minimum correlation of possible factor scores 0.94 0.91
print(mean.pca.oblimin2.2$loadings, cutoff=0.3)
##
## Loadings:
## MR1 MR2
## anxious_mean 0.924
## nervous_mean 0.984
## upset_mean 0.851
## irritable_mean 0.811
## content_mean 0.817
## relaxed_mean -0.354 0.537
## excited_mean 0.938
## happy_mean 0.884
## attentive_mean 0.623
##
## MR1 MR2
## SS loadings 3.462 3.037
## Proportion Var 0.385 0.337
## Cumulative Var 0.385 0.722
mean.pca.oblimin3.2 <- fa(r = indiv_means_noslug, nfactors = 3, rotate = "oblimin")
## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefully
mean.pca.oblimin3.2
## Factor Analysis using method = minres
## Call: fa(r = indiv_means_noslug, nfactors = 3, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR3 h2 u2 com
## anxious_mean 0.05 0.97 0.06 1.00 -0.00194 1.0
## nervous_mean 0.04 0.73 0.32 0.90 0.09917 1.4
## upset_mean -0.10 0.12 0.88 1.00 0.00083 1.1
## irritable_mean -0.13 0.25 0.65 0.80 0.20083 1.4
## content_mean 0.81 -0.04 -0.24 0.87 0.12959 1.2
## relaxed_mean 0.55 -0.57 0.18 0.69 0.31291 2.2
## excited_mean 0.92 0.05 0.19 0.75 0.24564 1.1
## happy_mean 0.88 0.02 -0.21 0.91 0.08996 1.1
## attentive_mean 0.61 0.04 -0.07 0.39 0.61305 1.0
##
## MR2 MR1 MR3
## SS loadings 3.15 2.28 1.88
## Proportion Var 0.35 0.25 0.21
## Cumulative Var 0.35 0.60 0.81
## Proportion Explained 0.43 0.31 0.26
## Cumulative Proportion 0.43 0.74 1.00
##
## With factor correlations of
## MR2 MR1 MR3
## MR2 1.00 -0.35 -0.31
## MR1 -0.35 1.00 0.65
## MR3 -0.31 0.65 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 12 and the objective function was 0.9
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 117 with the empirical chi square 5.37 with prob < 0.94
## The total number of observations was 130 with Likelihood Chi Square = 110.59 with prob < 4.6e-18
##
## Tucker Lewis Index of factoring reliability = 0.768
## RMSEA index = 0.259 and the 90 % confidence intervals are 0.211 0.296
## BIC = 52.18
## Fit based upon off diagonal values = 1
print(mean.pca.oblimin3.2$loadings, cutoff=0.3)
##
## Loadings:
## MR2 MR1 MR3
## anxious_mean 0.975
## nervous_mean 0.728 0.316
## upset_mean 0.880
## irritable_mean 0.652
## content_mean 0.809
## relaxed_mean 0.550 -0.572
## excited_mean 0.919
## happy_mean 0.875
## attentive_mean 0.614
##
## MR2 MR1 MR3
## SS loadings 2.974 1.893 1.483
## Proportion Var 0.330 0.210 0.165
## Cumulative Var 0.330 0.541 0.706
fa.diagram(mean.pca.oblimin3.2)
mean.pca.oblimin4.2 <- fa(r = indiv_means_noslug, nfactors = 4, rotate = "oblimin")
## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefully
mean.pca.oblimin4.2
## Factor Analysis using method = minres
## Call: fa(r = indiv_means_noslug, nfactors = 4, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR4 MR3 h2 u2 com
## anxious_mean 0.00 0.95 0.07 0.01 0.98 0.0183 1.0
## nervous_mean 0.10 0.78 0.27 -0.12 0.93 0.0685 1.3
## upset_mean -0.18 0.18 0.82 0.01 1.00 -0.0013 1.2
## irritable_mean -0.10 0.31 0.60 -0.12 0.80 0.1971 1.7
## content_mean 0.83 -0.01 -0.21 0.05 0.89 0.1080 1.1
## relaxed_mean 0.60 -0.55 0.22 -0.03 0.73 0.2666 2.3
## excited_mean 0.75 0.07 0.19 0.21 0.70 0.3004 1.3
## happy_mean 0.89 0.05 -0.18 0.05 0.93 0.0655 1.1
## attentive_mean 0.02 -0.01 0.01 0.99 1.00 0.0044 1.0
##
## MR2 MR1 MR4 MR3
## SS loadings 2.76 2.36 1.63 1.22
## Proportion Var 0.31 0.26 0.18 0.14
## Cumulative Var 0.31 0.57 0.75 0.89
## Proportion Explained 0.35 0.30 0.21 0.15
## Cumulative Proportion 0.35 0.64 0.85 1.00
##
## With factor correlations of
## MR2 MR1 MR4 MR3
## MR2 1.00 -0.37 -0.27 0.54
## MR1 -0.37 1.00 0.59 -0.19
## MR4 -0.27 0.59 1.00 -0.17
## MR3 0.54 -0.19 -0.17 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 6 and the objective function was 0.59
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 117 with the empirical chi square 0.93 with prob < 0.99
## The total number of observations was 130 with Likelihood Chi Square = 72.38 with prob < 1.3e-13
##
## Tucker Lewis Index of factoring reliability = 0.685
## RMSEA index = 0.301 and the 90 % confidence intervals are 0.235 0.355
## BIC = 43.17
## Fit based upon off diagonal values = 1
print(mean.pca.oblimin4.2$loadings, cutoff=0.3)
##
## Loadings:
## MR2 MR1 MR4 MR3
## anxious_mean 0.951
## nervous_mean 0.784
## upset_mean 0.816
## irritable_mean 0.311 0.596
## content_mean 0.831
## relaxed_mean 0.604 -0.553
## excited_mean 0.750
## happy_mean 0.892
## attentive_mean 0.988
##
## MR2 MR1 MR4 MR3
## SS loadings 2.468 1.962 1.261 1.056
## Proportion Var 0.274 0.218 0.140 0.117
## Cumulative Var 0.274 0.492 0.632 0.750
fa.diagram(mean.pca.oblimin4.2)
mean.pca.oblimin5.2 <- fa(r = indiv_means_noslug, nfactors = 5, rotate = "oblimin")
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefully
mean.pca.oblimin5.2
## Factor Analysis using method = minres
## Call: fa(r = indiv_means_noslug, nfactors = 5, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR4 MR3 MR5 h2 u2 com
## anxious_mean 0.05 0.82 0.18 0.02 -0.22 0.98 0.01573 1.3
## nervous_mean -0.01 0.90 0.13 -0.08 0.15 1.00 -0.00085 1.1
## upset_mean -0.14 0.12 0.86 0.04 0.08 1.00 0.00456 1.1
## irritable_mean 0.01 0.15 0.77 -0.12 -0.10 0.83 0.16813 1.2
## content_mean 0.70 0.07 -0.32 0.08 0.16 0.89 0.10907 1.6
## relaxed_mean 0.56 -0.51 0.17 -0.01 0.20 0.73 0.27294 2.4
## excited_mean 0.66 0.11 0.12 0.25 0.17 0.70 0.30211 1.6
## happy_mean 0.95 -0.03 -0.10 0.03 -0.10 1.00 0.00408 1.0
## attentive_mean 0.00 -0.02 0.00 0.99 -0.01 1.00 0.00486 1.0
##
## MR2 MR1 MR4 MR3 MR5
## SS loadings 2.53 2.15 1.91 1.24 0.29
## Proportion Var 0.28 0.24 0.21 0.14 0.03
## Cumulative Var 0.28 0.52 0.73 0.87 0.90
## Proportion Explained 0.31 0.27 0.23 0.15 0.04
## Cumulative Proportion 0.31 0.58 0.81 0.96 1.00
##
## With factor correlations of
## MR2 MR1 MR4 MR3 MR5
## MR2 1.00 -0.30 -0.39 0.54 0.34
## MR1 -0.30 1.00 0.70 -0.17 -0.22
## MR4 -0.39 0.70 1.00 -0.22 0.01
## MR3 0.54 -0.17 -0.22 1.00 0.15
## MR5 0.34 -0.22 0.01 0.15 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 1 and the objective function was 0.15
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 117 with the empirical chi square 0.38 with prob < 0.54
## The total number of observations was 130 with Likelihood Chi Square = 17.85 with prob < 2.4e-05
##
## Tucker Lewis Index of factoring reliability = 0.518
## RMSEA index = 0.372 and the 90 % confidence intervals are 0.227 0.517
## BIC = 12.98
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR2 MR1 MR4 MR3 MR5
## Correlation of (regression) scores with factors 1 1 1.00 1.00 0.97
## Multiple R square of scores with factors 1 1 0.99 1.00 0.94
## Minimum correlation of possible factor scores 1 1 0.99 0.99 0.87
print(mean.pca.oblimin5.2$loadings, cutoff=0.3)
##
## Loadings:
## MR2 MR1 MR4 MR3 MR5
## anxious_mean 0.815
## nervous_mean 0.903
## upset_mean 0.857
## irritable_mean 0.766
## content_mean 0.704 -0.317
## relaxed_mean 0.560 -0.507
## excited_mean 0.656
## happy_mean 0.953
## attentive_mean 0.993
##
## MR2 MR1 MR4 MR3 MR5
## SS loadings 2.170 1.791 1.525 1.078 0.190
## Proportion Var 0.241 0.199 0.169 0.120 0.021
## Cumulative Var 0.241 0.440 0.610 0.729 0.751
EFA for the item means using fa() and fm=pa
mean.pca.oblimin.2 <- fa(r = indiv_means_noslug, nfactors = 1, rotate = "oblimin", fm ="pa")
mean.pca.oblimin.2
## Factor Analysis using method = pa
## Call: fa(r = indiv_means_noslug, nfactors = 1, rotate = "oblimin",
## fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 h2 u2 com
## anxious_mean -0.75 0.57 0.43 1
## nervous_mean -0.77 0.59 0.41 1
## upset_mean -0.82 0.67 0.33 1
## irritable_mean -0.80 0.65 0.35 1
## content_mean 0.84 0.71 0.29 1
## relaxed_mean 0.75 0.57 0.43 1
## excited_mean 0.50 0.25 0.75 1
## happy_mean 0.81 0.65 0.35 1
## attentive_mean 0.49 0.24 0.76 1
##
## PA1
## SS loadings 4.90
## Proportion Var 0.54
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 27 and the objective function was 5.68
##
## The root mean square of the residuals (RMSR) is 0.19
## The df corrected root mean square of the residuals is 0.22
##
## The harmonic number of observations is 117 with the empirical chi square 296.95 with prob < 2.9e-47
## The total number of observations was 130 with Likelihood Chi Square = 706.96 with prob < 1.3e-131
##
## Tucker Lewis Index of factoring reliability = 0.295
## RMSEA index = 0.45 and the 90 % confidence intervals are 0.414 0.47
## BIC = 575.54
## Fit based upon off diagonal values = 0.89
## Measures of factor score adequacy
## PA1
## Correlation of (regression) scores with factors 0.96
## Multiple R square of scores with factors 0.93
## Minimum correlation of possible factor scores 0.86
print(mean.pca.oblimin.2$loadings, cutoff=0.3)
##
## Loadings:
## PA1
## anxious_mean -0.755
## nervous_mean -0.771
## upset_mean -0.816
## irritable_mean -0.804
## content_mean 0.841
## relaxed_mean 0.754
## excited_mean 0.496
## happy_mean 0.809
## attentive_mean 0.495
##
## PA1
## SS loadings 4.898
## Proportion Var 0.544
mean.pca.oblimin2.2 <- fa(r = indiv_means_noslug, nfactors = 2, rotate = "oblimin", fm ="pa")
mean.pca.oblimin2.2
## Factor Analysis using method = pa
## Call: fa(r = indiv_means_noslug, nfactors = 2, rotate = "oblimin",
## fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA2 h2 u2 com
## anxious_mean 0.93 0.03 0.83 0.166 1.0
## nervous_mean 0.98 0.06 0.92 0.079 1.0
## upset_mean 0.85 -0.10 0.81 0.194 1.0
## irritable_mean 0.81 -0.12 0.76 0.244 1.0
## content_mean -0.22 0.82 0.87 0.134 1.1
## relaxed_mean -0.35 0.54 0.57 0.430 1.7
## excited_mean 0.25 0.94 0.75 0.251 1.1
## happy_mean -0.14 0.88 0.90 0.099 1.0
## attentive_mean 0.00 0.62 0.39 0.612 1.0
##
## PA1 PA2
## SS loadings 3.61 3.18
## Proportion Var 0.40 0.35
## Cumulative Var 0.40 0.75
## Proportion Explained 0.53 0.47
## Cumulative Proportion 0.53 1.00
##
## With factor correlations of
## PA1 PA2
## PA1 1.00 -0.41
## PA2 -0.41 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 19 and the objective function was 2.13
##
## The root mean square of the residuals (RMSR) is 0.05
## The df corrected root mean square of the residuals is 0.07
##
## The harmonic number of observations is 117 with the empirical chi square 20.27 with prob < 0.38
## The total number of observations was 130 with Likelihood Chi Square = 264.3 with prob < 3.9e-45
##
## Tucker Lewis Index of factoring reliability = 0.637
## RMSEA index = 0.323 and the 90 % confidence intervals are 0.283 0.351
## BIC = 171.82
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## PA1 PA2
## Correlation of (regression) scores with factors 0.98 0.98
## Multiple R square of scores with factors 0.97 0.95
## Minimum correlation of possible factor scores 0.94 0.91
print(mean.pca.oblimin2.2$loadings, cutoff=0.3)
##
## Loadings:
## PA1 PA2
## anxious_mean 0.925
## nervous_mean 0.983
## upset_mean 0.851
## irritable_mean 0.811
## content_mean 0.818
## relaxed_mean -0.354 0.538
## excited_mean 0.937
## happy_mean 0.884
## attentive_mean 0.624
##
## PA1 PA2
## SS loadings 3.462 3.037
## Proportion Var 0.385 0.337
## Cumulative Var 0.385 0.722
mean.pca.oblimin3.2 <- fa(r = indiv_means_noslug, nfactors = 3, rotate = "oblimin", fm ="pa")
## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefully
mean.pca.oblimin3.2
## Factor Analysis using method = pa
## Call: fa(r = indiv_means_noslug, nfactors = 3, rotate = "oblimin",
## fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA2 PA1 PA3 h2 u2 com
## anxious_mean 0.04 0.99 0.05 1.03 -0.029 1.0
## nervous_mean 0.04 0.71 0.33 0.89 0.106 1.4
## upset_mean -0.08 0.10 0.91 1.02 -0.015 1.0
## irritable_mean -0.13 0.25 0.65 0.79 0.206 1.4
## content_mean 0.81 -0.03 -0.25 0.87 0.130 1.2
## relaxed_mean 0.56 -0.56 0.17 0.68 0.320 2.2
## excited_mean 0.92 0.04 0.20 0.75 0.245 1.1
## happy_mean 0.87 0.02 -0.22 0.91 0.090 1.1
## attentive_mean 0.61 0.04 -0.07 0.39 0.613 1.0
##
## PA2 PA1 PA3
## SS loadings 3.15 2.25 1.93
## Proportion Var 0.35 0.25 0.21
## Cumulative Var 0.35 0.60 0.81
## Proportion Explained 0.43 0.31 0.26
## Cumulative Proportion 0.43 0.74 1.00
##
## With factor correlations of
## PA2 PA1 PA3
## PA2 1.00 -0.34 -0.33
## PA1 -0.34 1.00 0.65
## PA3 -0.33 0.65 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 12 and the objective function was 0.88
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 117 with the empirical chi square 5.33 with prob < 0.95
## The total number of observations was 130 with Likelihood Chi Square = 107.92 with prob < 1.5e-17
##
## Tucker Lewis Index of factoring reliability = 0.774
## RMSEA index = 0.255 and the 90 % confidence intervals are 0.207 0.293
## BIC = 49.51
## Fit based upon off diagonal values = 1
print(mean.pca.oblimin3.2$loadings, cutoff=0.3)
##
## Loadings:
## PA2 PA1 PA3
## anxious_mean 0.993
## nervous_mean 0.711 0.329
## upset_mean 0.907
## irritable_mean 0.652
## content_mean 0.806
## relaxed_mean 0.559 -0.556
## excited_mean 0.921
## happy_mean 0.872
## attentive_mean 0.613
##
## PA2 PA1 PA3
## SS loadings 2.973 1.879 1.537
## Proportion Var 0.330 0.209 0.171
## Cumulative Var 0.330 0.539 0.710
fa.diagram(mean.pca.oblimin3.2)
mean.pca.oblimin4.2 <- fa(r = indiv_means_noslug, nfactors = 4, rotate = "oblimin", fm ="pa")
## maximum iteration exceeded
## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefully
mean.pca.oblimin4.2
## Factor Analysis using method = pa
## Call: fa(r = indiv_means_noslug, nfactors = 4, rotate = "oblimin",
## fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA2 PA1 PA3 PA4 h2 u2 com
## anxious_mean -0.02 0.94 0.07 0.02 0.98 0.021 1.0
## nervous_mean 0.11 0.79 0.26 -0.14 0.94 0.063 1.3
## upset_mean -0.16 0.15 0.86 0.02 1.04 -0.036 1.1
## irritable_mean -0.06 0.32 0.58 -0.15 0.80 0.204 1.7
## content_mean 0.81 0.00 -0.23 0.07 0.89 0.107 1.2
## relaxed_mean 0.63 -0.54 0.20 -0.04 0.74 0.263 2.2
## excited_mean 0.70 0.07 0.19 0.28 0.72 0.285 1.5
## happy_mean 0.85 0.05 -0.20 0.09 0.93 0.073 1.1
## attentive_mean 0.06 -0.02 0.03 0.84 0.76 0.240 1.0
##
## PA2 PA1 PA3 PA4
## SS loadings 2.67 2.34 1.70 1.07
## Proportion Var 0.30 0.26 0.19 0.12
## Cumulative Var 0.30 0.56 0.75 0.86
## Proportion Explained 0.34 0.30 0.22 0.14
## Cumulative Proportion 0.34 0.64 0.86 1.00
##
## With factor correlations of
## PA2 PA1 PA3 PA4
## PA2 1.00 -0.35 -0.28 0.58
## PA1 -0.35 1.00 0.60 -0.19
## PA3 -0.28 0.60 1.00 -0.21
## PA4 0.58 -0.19 -0.21 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 6 and the objective function was 0.61
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 117 with the empirical chi square 0.96 with prob < 0.99
## The total number of observations was 130 with Likelihood Chi Square = 74.85 with prob < 4.1e-14
##
## Tucker Lewis Index of factoring reliability = 0.674
## RMSEA index = 0.307 and the 90 % confidence intervals are 0.24 0.36
## BIC = 45.64
## Fit based upon off diagonal values = 1
print(mean.pca.oblimin4.2$loadings, cutoff=0.3)
##
## Loadings:
## PA2 PA1 PA3 PA4
## anxious_mean 0.942
## nervous_mean 0.793
## upset_mean 0.861
## irritable_mean 0.324 0.582
## content_mean 0.809
## relaxed_mean 0.632 -0.536
## excited_mean 0.701
## happy_mean 0.854
## attentive_mean 0.837
##
## PA2 PA1 PA3 PA4
## SS loadings 2.324 1.942 1.320 0.836
## Proportion Var 0.258 0.216 0.147 0.093
## Cumulative Var 0.258 0.474 0.621 0.713
fa.diagram(mean.pca.oblimin4.2)
mean.pca.oblimin5.2 <- fa(r = indiv_means_noslug, nfactors = 5, rotate = "oblimin", fm ="pa")
## maximum iteration exceeded
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : A loading greater than abs(1) was detected. Examine the loadings
## carefully.
## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefully
mean.pca.oblimin5.2
## Factor Analysis using method = pa
## Call: fa(r = indiv_means_noslug, nfactors = 5, rotate = "oblimin",
## fm = "pa")
##
## Warning: A Heywood case was detected.
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA2 PA3 PA1 PA4 PA5 h2 u2 com
## anxious_mean -0.02 0.36 0.31 0.05 0.55 0.99 0.0102 2.4
## nervous_mean 0.00 0.03 1.04 -0.04 0.01 1.16 -0.1575 1.0
## upset_mean -0.15 0.86 0.12 0.09 -0.07 0.99 0.0089 1.1
## irritable_mean 0.03 0.84 0.03 -0.15 0.14 0.85 0.1519 1.1
## content_mean 0.77 -0.27 0.09 0.07 -0.05 0.89 0.1135 1.3
## relaxed_mean 0.67 0.11 -0.20 -0.05 -0.34 0.73 0.2696 1.8
## excited_mean 0.64 0.13 0.13 0.35 -0.09 0.73 0.2706 1.8
## happy_mean 0.92 -0.08 -0.09 0.04 0.13 0.97 0.0344 1.1
## attentive_mean 0.08 -0.06 -0.13 0.73 0.04 0.67 0.3335 1.1
##
## PA2 PA3 PA1 PA4 PA5
## SS loadings 2.72 2.05 1.62 0.90 0.69
## Proportion Var 0.30 0.23 0.18 0.10 0.08
## Cumulative Var 0.30 0.53 0.71 0.81 0.88
## Proportion Explained 0.34 0.26 0.20 0.11 0.09
## Cumulative Proportion 0.34 0.60 0.80 0.91 1.00
##
## With factor correlations of
## PA2 PA3 PA1 PA4 PA5
## PA2 1.00 -0.44 -0.31 0.57 -0.25
## PA3 -0.44 1.00 0.72 -0.11 0.29
## PA1 -0.31 0.72 1.00 -0.06 0.52
## PA4 0.57 -0.11 -0.06 1.00 -0.04
## PA5 -0.25 0.29 0.52 -0.04 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 1 and the objective function was 0.1
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 117 with the empirical chi square 0.29 with prob < 0.59
## The total number of observations was 130 with Likelihood Chi Square = 12.04 with prob < 0.00052
##
## Tucker Lewis Index of factoring reliability = 0.684
## RMSEA index = 0.302 and the 90 % confidence intervals are 0.161 0.45
## BIC = 7.17
## Fit based upon off diagonal values = 1
print(mean.pca.oblimin5.2$loadings, cutoff=0.3)
##
## Loadings:
## PA2 PA3 PA1 PA4 PA5
## anxious_mean 0.357 0.311 0.548
## nervous_mean 1.041
## upset_mean 0.861
## irritable_mean 0.841
## content_mean 0.774
## relaxed_mean 0.671 -0.340
## excited_mean 0.642 0.350
## happy_mean 0.920
## attentive_mean 0.734
##
## PA2 PA3 PA1 PA4 PA5
## SS loadings 2.341 1.687 1.285 0.704 0.469
## Proportion Var 0.260 0.187 0.143 0.078 0.052
## Cumulative Var 0.260 0.448 0.590 0.668 0.721
EFA for the item means using fa() and fm=minres
mean.pca.oblimin.2 <- fa(r = indiv_means_noslug, nfactors = 1, rotate = "oblimin", fm ="minres")
mean.pca.oblimin.2
## Factor Analysis using method = minres
## Call: fa(r = indiv_means_noslug, nfactors = 1, rotate = "oblimin",
## fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## anxious_mean -0.75 0.57 0.43 1
## nervous_mean -0.77 0.59 0.41 1
## upset_mean -0.82 0.67 0.33 1
## irritable_mean -0.80 0.65 0.35 1
## content_mean 0.84 0.71 0.29 1
## relaxed_mean 0.75 0.57 0.43 1
## excited_mean 0.50 0.25 0.75 1
## happy_mean 0.81 0.65 0.35 1
## attentive_mean 0.49 0.24 0.76 1
##
## MR1
## SS loadings 4.90
## Proportion Var 0.54
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 27 and the objective function was 5.68
##
## The root mean square of the residuals (RMSR) is 0.19
## The df corrected root mean square of the residuals is 0.22
##
## The harmonic number of observations is 117 with the empirical chi square 296.94 with prob < 3e-47
## The total number of observations was 130 with Likelihood Chi Square = 706.98 with prob < 1.3e-131
##
## Tucker Lewis Index of factoring reliability = 0.295
## RMSEA index = 0.45 and the 90 % confidence intervals are 0.414 0.47
## BIC = 575.56
## Fit based upon off diagonal values = 0.89
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.96
## Multiple R square of scores with factors 0.93
## Minimum correlation of possible factor scores 0.86
print(mean.pca.oblimin.2$loadings, cutoff=0.3)
##
## Loadings:
## MR1
## anxious_mean -0.755
## nervous_mean -0.771
## upset_mean -0.816
## irritable_mean -0.804
## content_mean 0.841
## relaxed_mean 0.754
## excited_mean 0.496
## happy_mean 0.809
## attentive_mean 0.495
##
## MR1
## SS loadings 4.898
## Proportion Var 0.544
mean.pca.oblimin2.2 <- fa(r = indiv_means_noslug, nfactors = 2, rotate = "oblimin", fm ="minres")
mean.pca.oblimin2.2
## Factor Analysis using method = minres
## Call: fa(r = indiv_means_noslug, nfactors = 2, rotate = "oblimin",
## fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 MR2 h2 u2 com
## anxious_mean 0.92 0.03 0.83 0.168 1.0
## nervous_mean 0.98 0.06 0.92 0.077 1.0
## upset_mean 0.85 -0.10 0.81 0.194 1.0
## irritable_mean 0.81 -0.12 0.76 0.244 1.0
## content_mean -0.22 0.82 0.87 0.135 1.1
## relaxed_mean -0.35 0.54 0.57 0.430 1.7
## excited_mean 0.25 0.94 0.75 0.250 1.1
## happy_mean -0.14 0.88 0.90 0.098 1.0
## attentive_mean 0.00 0.62 0.39 0.613 1.0
##
## MR1 MR2
## SS loadings 3.61 3.18
## Proportion Var 0.40 0.35
## Cumulative Var 0.40 0.75
## Proportion Explained 0.53 0.47
## Cumulative Proportion 0.53 1.00
##
## With factor correlations of
## MR1 MR2
## MR1 1.00 -0.41
## MR2 -0.41 1.00
##
## Mean item complexity = 1.1
## Test of the hypothesis that 2 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 19 and the objective function was 2.14
##
## The root mean square of the residuals (RMSR) is 0.05
## The df corrected root mean square of the residuals is 0.07
##
## The harmonic number of observations is 117 with the empirical chi square 20.26 with prob < 0.38
## The total number of observations was 130 with Likelihood Chi Square = 264.46 with prob < 3.6e-45
##
## Tucker Lewis Index of factoring reliability = 0.636
## RMSEA index = 0.323 and the 90 % confidence intervals are 0.283 0.351
## BIC = 171.98
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## MR1 MR2
## Correlation of (regression) scores with factors 0.99 0.98
## Multiple R square of scores with factors 0.97 0.95
## Minimum correlation of possible factor scores 0.94 0.91
print(mean.pca.oblimin2.2$loadings, cutoff=0.3)
##
## Loadings:
## MR1 MR2
## anxious_mean 0.924
## nervous_mean 0.984
## upset_mean 0.851
## irritable_mean 0.811
## content_mean 0.817
## relaxed_mean -0.354 0.537
## excited_mean 0.938
## happy_mean 0.884
## attentive_mean 0.623
##
## MR1 MR2
## SS loadings 3.462 3.037
## Proportion Var 0.385 0.337
## Cumulative Var 0.385 0.722
mean.pca.oblimin3.2 <- fa(r = indiv_means_noslug, nfactors = 3, rotate = "oblimin", fm ="minres")
## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefully
mean.pca.oblimin3.2
## Factor Analysis using method = minres
## Call: fa(r = indiv_means_noslug, nfactors = 3, rotate = "oblimin",
## fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR3 h2 u2 com
## anxious_mean 0.05 0.97 0.06 1.00 -0.00194 1.0
## nervous_mean 0.04 0.73 0.32 0.90 0.09917 1.4
## upset_mean -0.10 0.12 0.88 1.00 0.00083 1.1
## irritable_mean -0.13 0.25 0.65 0.80 0.20083 1.4
## content_mean 0.81 -0.04 -0.24 0.87 0.12959 1.2
## relaxed_mean 0.55 -0.57 0.18 0.69 0.31291 2.2
## excited_mean 0.92 0.05 0.19 0.75 0.24564 1.1
## happy_mean 0.88 0.02 -0.21 0.91 0.08996 1.1
## attentive_mean 0.61 0.04 -0.07 0.39 0.61305 1.0
##
## MR2 MR1 MR3
## SS loadings 3.15 2.28 1.88
## Proportion Var 0.35 0.25 0.21
## Cumulative Var 0.35 0.60 0.81
## Proportion Explained 0.43 0.31 0.26
## Cumulative Proportion 0.43 0.74 1.00
##
## With factor correlations of
## MR2 MR1 MR3
## MR2 1.00 -0.35 -0.31
## MR1 -0.35 1.00 0.65
## MR3 -0.31 0.65 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 3 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 12 and the objective function was 0.9
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 117 with the empirical chi square 5.37 with prob < 0.94
## The total number of observations was 130 with Likelihood Chi Square = 110.59 with prob < 4.6e-18
##
## Tucker Lewis Index of factoring reliability = 0.768
## RMSEA index = 0.259 and the 90 % confidence intervals are 0.211 0.296
## BIC = 52.18
## Fit based upon off diagonal values = 1
print(mean.pca.oblimin3.2$loadings, cutoff=0.3)
##
## Loadings:
## MR2 MR1 MR3
## anxious_mean 0.975
## nervous_mean 0.728 0.316
## upset_mean 0.880
## irritable_mean 0.652
## content_mean 0.809
## relaxed_mean 0.550 -0.572
## excited_mean 0.919
## happy_mean 0.875
## attentive_mean 0.614
##
## MR2 MR1 MR3
## SS loadings 2.974 1.893 1.483
## Proportion Var 0.330 0.210 0.165
## Cumulative Var 0.330 0.541 0.706
fa.diagram(mean.pca.oblimin3.2)
mean.pca.oblimin4.2 <- fa(r = indiv_means_noslug, nfactors = 4, rotate = "oblimin", fm ="minres")
## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefully
mean.pca.oblimin4.2
## Factor Analysis using method = minres
## Call: fa(r = indiv_means_noslug, nfactors = 4, rotate = "oblimin",
## fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR4 MR3 h2 u2 com
## anxious_mean 0.00 0.95 0.07 0.01 0.98 0.0183 1.0
## nervous_mean 0.10 0.78 0.27 -0.12 0.93 0.0685 1.3
## upset_mean -0.18 0.18 0.82 0.01 1.00 -0.0013 1.2
## irritable_mean -0.10 0.31 0.60 -0.12 0.80 0.1971 1.7
## content_mean 0.83 -0.01 -0.21 0.05 0.89 0.1080 1.1
## relaxed_mean 0.60 -0.55 0.22 -0.03 0.73 0.2666 2.3
## excited_mean 0.75 0.07 0.19 0.21 0.70 0.3004 1.3
## happy_mean 0.89 0.05 -0.18 0.05 0.93 0.0655 1.1
## attentive_mean 0.02 -0.01 0.01 0.99 1.00 0.0044 1.0
##
## MR2 MR1 MR4 MR3
## SS loadings 2.76 2.36 1.63 1.22
## Proportion Var 0.31 0.26 0.18 0.14
## Cumulative Var 0.31 0.57 0.75 0.89
## Proportion Explained 0.35 0.30 0.21 0.15
## Cumulative Proportion 0.35 0.64 0.85 1.00
##
## With factor correlations of
## MR2 MR1 MR4 MR3
## MR2 1.00 -0.37 -0.27 0.54
## MR1 -0.37 1.00 0.59 -0.19
## MR4 -0.27 0.59 1.00 -0.17
## MR3 0.54 -0.19 -0.17 1.00
##
## Mean item complexity = 1.3
## Test of the hypothesis that 4 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 6 and the objective function was 0.59
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 117 with the empirical chi square 0.93 with prob < 0.99
## The total number of observations was 130 with Likelihood Chi Square = 72.38 with prob < 1.3e-13
##
## Tucker Lewis Index of factoring reliability = 0.685
## RMSEA index = 0.301 and the 90 % confidence intervals are 0.235 0.355
## BIC = 43.17
## Fit based upon off diagonal values = 1
print(mean.pca.oblimin4.2$loadings, cutoff=0.3)
##
## Loadings:
## MR2 MR1 MR4 MR3
## anxious_mean 0.951
## nervous_mean 0.784
## upset_mean 0.816
## irritable_mean 0.311 0.596
## content_mean 0.831
## relaxed_mean 0.604 -0.553
## excited_mean 0.750
## happy_mean 0.892
## attentive_mean 0.988
##
## MR2 MR1 MR4 MR3
## SS loadings 2.468 1.962 1.261 1.056
## Proportion Var 0.274 0.218 0.140 0.117
## Cumulative Var 0.274 0.492 0.632 0.750
fa.diagram(mean.pca.oblimin4.2)
mean.pca.oblimin5.2 <- fa(r = indiv_means_noslug, nfactors = 5, rotate = "oblimin", fm ="minres")
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate =
## rotate, : An ultra-Heywood case was detected. Examine the results carefully
mean.pca.oblimin5.2
## Factor Analysis using method = minres
## Call: fa(r = indiv_means_noslug, nfactors = 5, rotate = "oblimin",
## fm = "minres")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR2 MR1 MR4 MR3 MR5 h2 u2 com
## anxious_mean 0.05 0.82 0.18 0.02 -0.22 0.98 0.01573 1.3
## nervous_mean -0.01 0.90 0.13 -0.08 0.15 1.00 -0.00085 1.1
## upset_mean -0.14 0.12 0.86 0.04 0.08 1.00 0.00456 1.1
## irritable_mean 0.01 0.15 0.77 -0.12 -0.10 0.83 0.16813 1.2
## content_mean 0.70 0.07 -0.32 0.08 0.16 0.89 0.10907 1.6
## relaxed_mean 0.56 -0.51 0.17 -0.01 0.20 0.73 0.27294 2.4
## excited_mean 0.66 0.11 0.12 0.25 0.17 0.70 0.30211 1.6
## happy_mean 0.95 -0.03 -0.10 0.03 -0.10 1.00 0.00408 1.0
## attentive_mean 0.00 -0.02 0.00 0.99 -0.01 1.00 0.00486 1.0
##
## MR2 MR1 MR4 MR3 MR5
## SS loadings 2.53 2.15 1.91 1.24 0.29
## Proportion Var 0.28 0.24 0.21 0.14 0.03
## Cumulative Var 0.28 0.52 0.73 0.87 0.90
## Proportion Explained 0.31 0.27 0.23 0.15 0.04
## Cumulative Proportion 0.31 0.58 0.81 0.96 1.00
##
## With factor correlations of
## MR2 MR1 MR4 MR3 MR5
## MR2 1.00 -0.30 -0.39 0.54 0.34
## MR1 -0.30 1.00 0.70 -0.17 -0.22
## MR4 -0.39 0.70 1.00 -0.22 0.01
## MR3 0.54 -0.17 -0.22 1.00 0.15
## MR5 0.34 -0.22 0.01 0.15 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 36 and the objective function was 10.62 with Chi Square of 1329.6
## The degrees of freedom for the model are 1 and the objective function was 0.15
##
## The root mean square of the residuals (RMSR) is 0.01
## The df corrected root mean square of the residuals is 0.04
##
## The harmonic number of observations is 117 with the empirical chi square 0.38 with prob < 0.54
## The total number of observations was 130 with Likelihood Chi Square = 17.85 with prob < 2.4e-05
##
## Tucker Lewis Index of factoring reliability = 0.518
## RMSEA index = 0.372 and the 90 % confidence intervals are 0.227 0.517
## BIC = 12.98
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR2 MR1 MR4 MR3 MR5
## Correlation of (regression) scores with factors 1 1 1.00 1.00 0.97
## Multiple R square of scores with factors 1 1 0.99 1.00 0.94
## Minimum correlation of possible factor scores 1 1 0.99 0.99 0.87
print(mean.pca.oblimin5.2$loadings, cutoff=0.3)
##
## Loadings:
## MR2 MR1 MR4 MR3 MR5
## anxious_mean 0.815
## nervous_mean 0.903
## upset_mean 0.857
## irritable_mean 0.766
## content_mean 0.704 -0.317
## relaxed_mean 0.560 -0.507
## excited_mean 0.656
## happy_mean 0.953
## attentive_mean 0.993
##
## MR2 MR1 MR4 MR3 MR5
## SS loadings 2.170 1.791 1.525 1.078 0.190
## Proportion Var 0.241 0.199 0.169 0.120 0.021
## Cumulative Var 0.241 0.440 0.610 0.729 0.751