CRAN Package Check Results for Package ackwards

Last updated on 2026-09-29 01:51:16 CEST.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	0.2.0	8.14	652.72	660.86	OK
r-devel-linux-x86_64-debian-gcc	0.2.0	6.35	407.30	413.65	OK
r-devel-linux-x86_64-fedora-clang	0.2.0			420.05	OK
r-devel-linux-x86_64-fedora-gcc	0.2.0			423.46	OK
r-devel-windows-x86_64	0.2.0	11.00	360.00	371.00	OK
r-patched-linux-x86_64	0.2.0	9.08	622.85	631.93	OK
r-release-linux-x86_64	0.2.0	7.48	625.58	633.06	OK
r-release-macos-arm64	0.2.0	2.00	83.00	85.00	OK
r-release-macos-x86_64	0.2.0	6.00	571.00	577.00	OK
r-release-windows-x86_64	0.2.0	9.00	266.00	275.00	ERROR
r-oldrel-macos-arm64	0.2.0	2.00	86.00	88.00	OK
r-oldrel-macos-x86_64	0.2.0	6.00	722.00	728.00	OK
r-oldrel-windows-x86_64	0.2.0	12.00	440.00	452.00	ERROR

Check Details

Version: 0.2.0
Check: tests
Result: ERROR Running 'testthat.R' [142s] Running the tests in 'tests/testthat.R' failed. Complete output: > library(testthat) > library(ackwards) > > test_check("ackwards") Starting 2 test processes. > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)... > test-suggest_k.R: i PA-PC suggested 6 components -- above the evaluated ceiling (`k_max` = 3); reporting k <= 3. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: i PA-FA suggested 6 factors -- above the evaluated ceiling (`k_max` = 3); reporting k <= 3. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [363ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 364 rows with missing values removed (2436 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [189ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [11.4s] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [160ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [99ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [2.4s] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)... > test-suggest_k.R: i PA-PC suggested 3 components -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: i PA-FA suggested 5 factors -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [247ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 86 rows with missing values removed (914 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [87ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [2.7s] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [99ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [79ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [1.6s] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [93ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [121ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [1.7s] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [77ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [84ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [108ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)... > test-suggest_k.R: i PA-FA suggested 4 factors -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [244ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [88ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 41 rows with missing values removed (959 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [105ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [738ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [100ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: i CD requires EFAtools (install to enable). > test-suggest_k.R: v Running MAP and VSS... [81ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [54ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: x Running MAP and VSS... [44ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [98ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 64 rows with missing values removed (936 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [88ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [2.4s] > test-suggest_k.R: > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 v > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 v > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 v* > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 3 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 v > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 v > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 v* > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 3 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v - 0.0427* 0.6224 0.0000 v > test-suggest_k.R: 2 v - 0.0522 0.7305* 0.7981 v > test-suggest_k.R: 3 - - 0.0971 0.6415 0.8407 v* > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: undetermined (no FA factor exceeded random threshold) > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 3 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v - 0.0427* 0.6224 0.0000 v > test-suggest_k.R: 2 v - 0.0522 0.7305* 0.7981 v > test-suggest_k.R: 3 - - 0.0971 0.6415 0.8407 v* > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: undetermined (no FA factor exceeded random threshold) > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 3 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: i Running parallel analysis (20 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (20 iterations, PC + FA)... [715ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 125 rows with missing values removed (875 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [171ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-esem.R: > test-esem.R: -- Bass-Ackwards Analysis (ackwards) ------------------------------------------- > test-esem.R: Engine: esem > test-esem.R: Rotation: varimax > test-esem.R: Basis: pearson > test-esem.R: n: 200 > test-esem.R: k (max): 3 > test-esem.R: > test-esem.R: -- Levels -- > test-esem.R: > test-esem.R: v k = 1: 1 factor, 43.0% variance > test-esem.R: v k = 2: 2 factors, 85.2% variance > test-esem.R: v k = 3: 3 factors, 87.8% variance > test-esem.R: > test-esem.R: -- Edges -- > test-esem.R: > test-esem.R: 3 of 8 edges have |r| >= 0.3 > test-esem.R: -------------------------------------------------------------------------------- > test-esem.R: Note: This is a series of linked solutions, not a fitted hierarchical model. > test-esem.R: Cross-level edges are descriptive score correlations. Per-level fit indices > test-esem.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level -- > test-esem.R: they do not validate the edges or the hierarchy itself. > test-suggest_k.R: v Running Comparison Data (CD)... [20.3s] > test-suggest_k.R: > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: + CD requires EFAtools (install to enable). > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: + CD requires EFAtools (install to enable). > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: i PA-FA suggested 3 factors -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [143ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 41 rows with missing values removed (959 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [107ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [685ms] > test-suggest_k.R: > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 v* > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 - > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 - > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 1 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 v* > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 - > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 - > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 1 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 > test-suggest_k.R: 1 v v 0.0427 0.6224 0.0000 > test-suggest_k.R: 2 v v 0.0522* 0.7305* 0.7981* > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510 > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: + CD requires EFAtools (install to enable). > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 2 > test-suggest_k.R: * MAP: k = 2 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 2 > test-suggest_k.R: Consensus: k = 2 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 > test-suggest_k.R: 1 v v 0.0427 0.6224 0.0000 > test-suggest_k.R: 2 v v 0.0522* 0.7305* 0.7981* > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510 > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: + CD requires EFAtools (install to enable). > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 2 > test-suggest_k.R: * MAP: k = 2 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 2 > test-suggest_k.R: Consensus: k = 2 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [35ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [130ms] > test-suggest_k.R: > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used). > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [2.4s] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [42ms] > test-suggest_k.R: > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k VSS-1 VSS-2 > test-suggest_k.R: 1 0.6224 0.0000 > test-suggest_k.R: 2 0.7305* 0.7981 > test-suggest_k.R: 3 0.6415 0.8407 > test-suggest_k.R: 4 0.6451 0.8510* > test-suggest_k.R: * optimal k > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: Consensus range: k = 2-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [41ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [90ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [133ms] > test-suggest_k.R: > test-layout.R: i Redundancy pruning (direct criterion, |r| >= 0.9) flagged 3 nodes. > test-layout.R: i Nodes are retained in the object; inspect with `x$prune$nodes` and `x$prune$chains`. > test-check-items.R: > test-check-items.R: -- Item quality check (ackwards) ----------------------------------------------- > test-check-items.R: Basis: pearson > test-check-items.R: Items: 5 > test-check-items.R: Flagged: 0 > test-check-items.R: v No item problems detected. > test-check-items.R: -------------------------------------------------------------------------------- > test-check-items.R: Constant items must be dropped (no variance). A near-constant item (one > test-check-items.R: response dominates) can yield a meaningless factor; a sparse category can make > test-check-items.R: `cor = "polychoric"` fail -- collapse rare categories, try `correct = 0`, or > test-check-items.R: drop the item. Full per-item table: treat this object as a data frame. > test-check-items.R: > test-check-items.R: -- Item quality check (ackwards) ----------------------------------------------- > test-check-items.R: Basis: polychoric > test-check-items.R: Items: 7 > test-check-items.R: Flagged: 2 > test-check-items.R: > test-check-items.R: -- Flagged items -- > test-check-items.R: > test-check-items.R: x constant: "const" > test-check-items.R: ! near-constant: "nc" > test-check-items.R: -------------------------------------------------------------------------------- > test-check-items.R: Constant items must be dropped (no variance). A near-constant item (one > test-check-items.R: response dominates) can yield a meaningless factor; a sparse category can make > test-check-items.R: `cor = "polychoric"` fail -- collapse rare categories, try `correct = 0`, or > test-check-items.R: drop the item. Full per-item table: treat this object as a data frame. > test-comparability.R: i Fitting 2 split-half replicates (pca, k = 1-3)... > test-comparability.R: v Fitting 2 split-half replicates (pca, k = 1-3)... [156ms] > test-comparability.R: > test-comparability.R: > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ---------------------------------- > test-comparability.R: Engine: pca > test-comparability.R: Basis: pearson > test-comparability.R: n: 1,000 (500 per half) > test-comparability.R: Splits: 2 > test-comparability.R: Levels: 1-3 > test-comparability.R: > test-comparability.R: -- Comparability by level (median across splits) -- > test-comparability.R: > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1) > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2) > test-comparability.R: k = 3: median r .73, min r .28 (m3f2) [1/2 splits usable] > test-comparability.R: -------------------------------------------------------------------------------- > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in > test-comparability.R: `$coefficients`. > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) -- > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate. > test-comparability.R: i Fitting 2 split-half replicates (pca, k = 1-3)... > test-comparability.R: v Fitting 2 split-half replicates (pca, k = 1-3)... [171ms] > test-comparability.R: > test-comparability.R: > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ---------------------------------- > test-comparability.R: Engine: pca > test-comparability.R: Basis: pearson > test-comparability.R: n: 1,000 (500 per half) > test-comparability.R: Splits: 2 > test-comparability.R: Levels: 1-3 > test-comparability.R: > test-comparability.R: -- Comparability by level (median across splits) -- > test-comparability.R: > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1) > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2) > test-comparability.R: k = 3: no usable splits (half-solutions did not converge) > test-comparability.R: -------------------------------------------------------------------------------- > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in > test-comparability.R: `$coefficients`. > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) -- > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate. > test-comparability.R: > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ---------------------------------- > test-comparability.R: Engine: pca > test-comparability.R: Basis: pearson > test-comparability.R: n: 1,000 (500 per half) > test-comparability.R: Splits: 4 > test-comparability.R: Levels: 1-5 > test-comparability.R: > test-comparability.R: -- Comparability by level (median across splits) -- > test-comparability.R: > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1) > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f1) > test-comparability.R: k = 3: median r .80, min r .58 (m3f2) > test-comparability.R: k = 4: median r 1.00, min r 1.00 (m4f2) > test-comparability.R: k = 5: median r .99, min r .14 (m5f5) > test-comparability.R: -------------------------------------------------------------------------------- > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in > test-comparability.R: `$coefficients`. > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) -- > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate. > test-comparability.R: > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ---------------------------------- > test-comparability.R: Engine: pca > test-comparability.R: Basis: pearson > test-comparability.R: n: 1,000 (500 per half) > test-comparability.R: Splits: 4 > test-comparability.R: Levels: 1-5 > test-comparability.R: > test-comparability.R: -- Comparability by level (median across splits) -- > test-comparability.R: > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1) > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f1) > test-comparability.R: k = 3: median r .80, min r .58 (m3f2) > test-comparability.R: k = 4: median r 1.00, min r 1.00 (m4f2) > test-comparability.R: k = 5: median r .99, min r .14 (m5f5) > test-comparability.R: -------------------------------------------------------------------------------- > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in > test-comparability.R: `$coefficients`. > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) -- > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate. > test-comparability.R: > test-comparability.R: Likely variables with missing values are > test-comparability.R: i6 > test-comparability.R: > test-comparability.R: Likely variables with missing values are > test-comparability.R: i6 > test-comparability.R: > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ---------------------------------- > test-comparability.R: Engine: pca > test-comparability.R: Basis: pearson > test-comparability.R: n: 1,000 (500 per half) > test-comparability.R: Splits: 2 > test-comparability.R: Levels: 1-2 (requested 1-3; full-sample fit truncated) > test-comparability.R: > test-comparability.R: -- Comparability by level (median across splits) -- > test-comparability.R: > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1) > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2) > test-comparability.R: -------------------------------------------------------------------------------- > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in > test-comparability.R: `$coefficients`. > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) -- > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate. > test-cor-input.R: > test-cor-input.R: -- Bass-Ackwards Analysis (ackwards) ------------------------------------------- > test-cor-input.R: Engine: pca > test-cor-input.R: Rotation: varimax > test-cor-input.R: Basis: (user-supplied matrix) > test-cor-input.R: n: NA > test-cor-input.R: k (max): 3 > test-cor-input.R: > test-cor-input.R: -- Levels -- > test-cor-input.R: > test-cor-input.R: v k = 1: 1 factor, 41.8% variance > test-cor-input.R: v k = 2: 2 factors, 58.5% variance > test-cor-input.R: v k = 3: 3 factors, 72.2% variance > test-cor-input.R: > test-cor-input.R: -- Edges -- > test-cor-input.R: > test-cor-input.R: 5 of 8 edges have |r| >= 0.3 > test-cor-input.R: -------------------------------------------------------------------------------- > test-cor-input.R: Note: This is a series of linked solutions, not a fitted hierarchical model. > test-cor-input.R: Cross-level edges are descriptive score correlations. Per-level fit indices > test-cor-input.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level -- > test-cor-input.R: they do not validate the edges or the hierarchy itself. > test-cor-input.R: > test-cor-input.R: -- Bass-Ackwards Analysis (ackwards) ------------------------------------------- > test-cor-input.R: Engine: pca > test-cor-input.R: Rotation: varimax > test-cor-input.R: Basis: (user-supplied matrix) > test-cor-input.R: n: NA > test-cor-input.R: k (max): 3 > test-cor-input.R: > test-cor-input.R: -- Levels -- > test-cor-input.R: > test-cor-input.R: v k = 1: 1 factor, 41.8% variance > test-cor-input.R: v k = 2: 2 factors, 58.5% variance > test-cor-input.R: v k = 3: 3 factors, 72.2% variance > test-cor-input.R: > test-cor-input.R: -- Edges -- > test-cor-input.R: > test-cor-input.R: 5 of 8 edges have |r| >= 0.3 > test-cor-input.R: -------------------------------------------------------------------------------- > test-cor-input.R: Note: This is a series of linked solutions, not a fitted hierarchical model. > test-cor-input.R: Cross-level edges are descriptive score correlations. Per-level fit indices > test-cor-input.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level -- > test-cor-input.R: they do not validate the edges or the hierarchy itself. > test-cor-input.R: Error: ! testthat subprocess exited in file 'test-cor-input.R'. Caused by error: ! R session crashed with exit code -1073741819 Backtrace: ▆ 1. └─testthat::test_check("ackwards") 2. └─testthat::test_dir(...) 3. └─testthat:::test_files(...) 4. └─testthat:::test_files_parallel(...) 5. ├─withr::with_dir(...) 6. │ └─base::force(code) 7. ├─testthat::with_reporter(...) 8. │ └─base::tryCatch(...) 9. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers) 10. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]]) 11. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler) 12. └─testthat:::parallel_event_loop_chunky(queue, reporters, ".") 13. └─queue$poll(Inf) 14. └─base::lapply(...) 15. └─testthat (local) FUN(X[[i]], ...) 16. └─private$handle_error(msg, i) 17. └─cli::cli_abort(...) 18. └─rlang::abort(...) Execution halted Flavor: r-release-windows-x86_64

Version: 0.2.0
Check: tests
Result: ERROR Running 'testthat.R' [283s] Running the tests in 'tests/testthat.R' failed. Complete output: > library(testthat) > library(ackwards) > > test_check("ackwards") Starting 2 test processes. > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)... > test-suggest_k.R: i PA-PC suggested 6 components -- above the evaluated ceiling (`k_max` = 3); reporting k <= 3. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: i PA-FA suggested 6 factors -- above the evaluated ceiling (`k_max` = 3); reporting k <= 3. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [501ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 364 rows with missing values removed (2436 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [354ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [14.8s] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [213ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [145ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [3s] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)... > test-suggest_k.R: i PA-PC suggested 3 components -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: i PA-FA suggested 5 factors -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [370ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 86 rows with missing values removed (914 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [130ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [3.5s] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [209ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [163ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [2.2s] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [142ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [135ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [2s] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [132ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [174ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [179ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)... > test-suggest_k.R: i PA-FA suggested 4 factors -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [384ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [162ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 41 rows with missing values removed (959 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [166ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [1s] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [166ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: i CD requires EFAtools (install to enable). > test-suggest_k.R: v Running MAP and VSS... [169ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In smc, smcs < 0 were set to .0 > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [99ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: x Running MAP and VSS... [62ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [169ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 64 rows with missing values removed (936 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [135ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [2.7s] > test-suggest_k.R: > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 v > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 v > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 v* > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 3 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 v > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 v > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 v* > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 3 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v - 0.0427* 0.6224 0.0000 v > test-suggest_k.R: 2 v - 0.0522 0.7305* 0.7981 v > test-suggest_k.R: 3 - - 0.0971 0.6415 0.8407 v* > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: undetermined (no FA factor exceeded random threshold) > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 3 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v - 0.0427* 0.6224 0.0000 v > test-suggest_k.R: 2 v - 0.0522 0.7305* 0.7981 v > test-suggest_k.R: 3 - - 0.0971 0.6415 0.8407 v* > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: undetermined (no FA factor exceeded random threshold) > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 3 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: i Running parallel analysis (20 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (20 iterations, PC + FA)... [859ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 125 rows with missing values removed (875 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [258ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [28.5s] > test-suggest_k.R: > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: + CD requires EFAtools (install to enable). > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: + CD requires EFAtools (install to enable). > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: i PA-FA suggested 3 factors -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2. > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion. > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [231ms] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: CD: 41 rows with missing values removed (959 complete cases used). > test-suggest_k.R: v Running MAP and VSS... [168ms] > test-suggest_k.R: > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [941ms] > test-suggest_k.R: > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 v* > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 - > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 - > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 1 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 CD > test-suggest_k.R: 1 v v 0.0427* 0.6224 0.0000 v* > test-suggest_k.R: 2 v v 0.0522 0.7305* 0.7981 - > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 - > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510* - > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 3 > test-suggest_k.R: * MAP: k = 1 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: * CD: k = 1 > test-suggest_k.R: Consensus range: k = 1-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 > test-suggest_k.R: 1 v v 0.0427 0.6224 0.0000 > test-suggest_k.R: 2 v v 0.0522* 0.7305* 0.7981* > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510 > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: + CD requires EFAtools (install to enable). > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 2 > test-suggest_k.R: * MAP: k = 2 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 2 > test-suggest_k.R: Consensus: k = 2 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k PA-PC PA-FA MAP VSS-1 VSS-2 > test-suggest_k.R: 1 v v 0.0427 0.6224 0.0000 > test-suggest_k.R: 2 v v 0.0522* 0.7305* 0.7981* > test-suggest_k.R: 3 - v 0.0971 0.6415 0.8407 > test-suggest_k.R: 4 - - 0.1577 0.6451 0.8510 > test-suggest_k.R: v retained * optimal k - not retained > test-suggest_k.R: + CD requires EFAtools (install to enable). > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * PA-PC: k <= 2 > test-suggest_k.R: * PA-FA: k <= 2 > test-suggest_k.R: * MAP: k = 2 > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 2 > test-suggest_k.R: Consensus: k = 2 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [61ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [216ms] > test-suggest_k.R: > test-esem.R: > test-esem.R: -- Bass-Ackwards Analysis (ackwards) ------------------------------------------- > test-esem.R: Engine: esem > test-esem.R: Rotation: varimax > test-esem.R: Basis: pearson > test-esem.R: n: 200 > test-esem.R: k (max): 3 > test-esem.R: > test-esem.R: -- Levels -- > test-esem.R: > test-esem.R: v k = 1: 1 factor, 43.0% variance > test-esem.R: v k = 2: 2 factors, 85.2% variance > test-esem.R: v k = 3: 3 factors, 87.8% variance > test-esem.R: > test-esem.R: -- Edges -- > test-esem.R: > test-esem.R: 3 of 8 edges have |r| >= 0.3 > test-esem.R: -------------------------------------------------------------------------------- > test-esem.R: Note: This is a series of linked solutions, not a fitted hierarchical model. > test-esem.R: Cross-level edges are descriptive score correlations. Per-level fit indices > test-esem.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level -- > test-esem.R: they do not validate the edges or the hierarchy itself. > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used). > test-suggest_k.R: i Running Comparison Data (CD)... > test-suggest_k.R: v Running Comparison Data (CD)... [2.9s] > test-suggest_k.R: > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [72ms] > test-suggest_k.R: > test-suggest_k.R: > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-suggest_k.R: Variables: 8 > test-suggest_k.R: n: 1,000 > test-suggest_k.R: Basis: pearson > test-suggest_k.R: Tested k: 1-4 > test-suggest_k.R: > test-suggest_k.R: -- Criteria (k = 1-4) -- > test-suggest_k.R: > test-suggest_k.R: k VSS-1 VSS-2 > test-suggest_k.R: 1 0.6224 0.0000 > test-suggest_k.R: 2 0.7305* 0.7981 > test-suggest_k.R: 3 0.6415 0.8407 > test-suggest_k.R: 4 0.6451 0.8510* > test-suggest_k.R: * optimal k > test-suggest_k.R: > test-suggest_k.R: -- Recommendations -- > test-suggest_k.R: > test-suggest_k.R: * VSS-1: k = 2 > test-suggest_k.R: * VSS-2: k = 4 > test-suggest_k.R: Consensus range: k = 2-4 > test-suggest_k.R: -------------------------------------------------------------------------------- > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional. > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range. > test-suggest_k.R: i Running MAP and VSS... > test-suggest_k.R: v Running MAP and VSS... [74ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [156ms] > test-suggest_k.R: > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)... > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [205ms] > test-suggest_k.R: > test-layout.R: i Redundancy pruning (direct criterion, |r| >= 0.9) flagged 3 nodes. > test-layout.R: i Nodes are retained in the object; inspect with `x$prune$nodes` and `x$prune$chains`. > test-check-items.R: > test-check-items.R: -- Item quality check (ackwards) ----------------------------------------------- > test-check-items.R: Basis: pearson > test-check-items.R: Items: 5 > test-check-items.R: Flagged: 0 > test-check-items.R: v No item problems detected. > test-check-items.R: -------------------------------------------------------------------------------- > test-check-items.R: Constant items must be dropped (no variance). A near-constant item (one > test-check-items.R: response dominates) can yield a meaningless factor; a sparse category can make > test-check-items.R: `cor = "polychoric"` fail -- collapse rare categories, try `correct = 0`, or > test-check-items.R: drop the item. Full per-item table: treat this object as a data frame. > test-check-items.R: > test-check-items.R: -- Item quality check (ackwards) ----------------------------------------------- > test-check-items.R: Basis: polychoric > test-check-items.R: Items: 7 > test-check-items.R: Flagged: 2 > test-check-items.R: > test-check-items.R: -- Flagged items -- > test-check-items.R: > test-check-items.R: x constant: "const" > test-check-items.R: ! near-constant: "nc" > test-check-items.R: -------------------------------------------------------------------------------- > test-check-items.R: Constant items must be dropped (no variance). A near-constant item (one > test-check-items.R: response dominates) can yield a meaningless factor; a sparse category can make > test-check-items.R: `cor = "polychoric"` fail -- collapse rare categories, try `correct = 0`, or > test-check-items.R: drop the item. Full per-item table: treat this object as a data frame. > test-comparability.R: i Fitting 2 split-half replicates (pca, k = 1-3)... > test-comparability.R: v Fitting 2 split-half replicates (pca, k = 1-3)... [218ms] > test-comparability.R: > test-comparability.R: > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ---------------------------------- > test-comparability.R: Engine: pca > test-comparability.R: Basis: pearson > test-comparability.R: n: 1,000 (500 per half) > test-comparability.R: Splits: 2 > test-comparability.R: Levels: 1-3 > test-comparability.R: > test-comparability.R: -- Comparability by level (median across splits) -- > test-comparability.R: > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1) > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2) > test-comparability.R: k = 3: median r .73, min r .28 (m3f2) [1/2 splits usable] > test-comparability.R: -------------------------------------------------------------------------------- > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in > test-comparability.R: `$coefficients`. > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) -- > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate. > test-comparability.R: i Fitting 2 split-half replicates (pca, k = 1-3)... > test-comparability.R: v Fitting 2 split-half replicates (pca, k = 1-3)... [239ms] > test-comparability.R: > test-comparability.R: > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ---------------------------------- > test-comparability.R: Engine: pca > test-comparability.R: Basis: pearson > test-comparability.R: n: 1,000 (500 per half) > test-comparability.R: Splits: 2 > test-comparability.R: Levels: 1-3 > test-comparability.R: > test-comparability.R: -- Comparability by level (median across splits) -- > test-comparability.R: > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1) > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2) > test-comparability.R: k = 3: no usable splits (half-solutions did not converge) > test-comparability.R: -------------------------------------------------------------------------------- > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in > test-comparability.R: `$coefficients`. > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) -- > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate. > test-comparability.R: > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ---------------------------------- > test-comparability.R: Engine: pca > test-comparability.R: Basis: pearson > test-comparability.R: n: 1,000 (500 per half) > test-comparability.R: Splits: 4 > test-comparability.R: Levels: 1-5 > test-comparability.R: > test-comparability.R: -- Comparability by level (median across splits) -- > test-comparability.R: > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1) > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f1) > test-comparability.R: k = 3: median r .80, min r .58 (m3f2) > test-comparability.R: k = 4: median r 1.00, min r 1.00 (m4f2) > test-comparability.R: k = 5: median r .99, min r .14 (m5f5) > test-comparability.R: -------------------------------------------------------------------------------- > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in > test-comparability.R: `$coefficients`. > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) -- > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate. > test-comparability.R: > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ---------------------------------- > test-comparability.R: Engine: pca > test-comparability.R: Basis: pearson > test-comparability.R: n: 1,000 (500 per half) > test-comparability.R: Splits: 4 > test-comparability.R: Levels: 1-5 > test-comparability.R: > test-comparability.R: -- Comparability by level (median across splits) -- > test-comparability.R: > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1) > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f1) > test-comparability.R: k = 3: median r .80, min r .58 (m3f2) > test-comparability.R: k = 4: median r 1.00, min r 1.00 (m4f2) > test-comparability.R: k = 5: median r .99, min r .14 (m5f5) > test-comparability.R: -------------------------------------------------------------------------------- > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in > test-comparability.R: `$coefficients`. > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) -- > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate. > test-comparability.R: > test-comparability.R: Likely variables with missing values are > test-comparability.R: i6 > test-comparability.R: > test-comparability.R: Likely variables with missing values are > test-comparability.R: i6 > test-comparability.R: > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ---------------------------------- > test-comparability.R: Engine: pca > test-comparability.R: Basis: pearson > test-comparability.R: n: 1,000 (500 per half) > test-comparability.R: Splits: 2 > test-comparability.R: Levels: 1-2 (requested 1-3; full-sample fit truncated) > test-comparability.R: > test-comparability.R: -- Comparability by level (median across splits) -- > test-comparability.R: > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1) > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2) > test-comparability.R: -------------------------------------------------------------------------------- > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in > test-comparability.R: `$coefficients`. > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) -- > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate. > test-cor-input.R: > test-cor-input.R: -- Bass-Ackwards Analysis (ackwards) ------------------------------------------- > test-cor-input.R: Engine: pca > test-cor-input.R: Rotation: varimax > test-cor-input.R: Basis: (user-supplied matrix) > test-cor-input.R: n: NA > test-cor-input.R: k (max): 3 > test-cor-input.R: > test-cor-input.R: -- Levels -- > test-cor-input.R: > test-cor-input.R: v k = 1: 1 factor, 41.8% variance > test-cor-input.R: v k = 2: 2 factors, 58.5% variance > test-cor-input.R: v k = 3: 3 factors, 72.2% variance > test-cor-input.R: > test-cor-input.R: -- Edges -- > test-cor-input.R: > test-cor-input.R: 5 of 8 edges have |r| >= 0.3 > test-cor-input.R: -------------------------------------------------------------------------------- > test-cor-input.R: Note: This is a series of linked solutions, not a fitted hierarchical model. > test-cor-input.R: Cross-level edges are descriptive score correlations. Per-level fit indices > test-cor-input.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level -- > test-cor-input.R: they do not validate the edges or the hierarchy itself. > test-cor-input.R: > test-cor-input.R: -- Bass-Ackwards Analysis (ackwards) ------------------------------------------- > test-cor-input.R: Engine: pca > test-cor-input.R: Rotation: varimax > test-cor-input.R: Basis: (user-supplied matrix) > test-cor-input.R: n: NA > test-cor-input.R: k (max): 3 > test-cor-input.R: > test-cor-input.R: -- Levels -- > test-cor-input.R: > test-cor-input.R: v k = 1: 1 factor, 41.8% variance > test-cor-input.R: v k = 2: 2 factors, 58.5% variance > test-cor-input.R: v k = 3: 3 factors, 72.2% variance > test-cor-input.R: > test-cor-input.R: -- Edges -- > test-cor-input.R: > test-cor-input.R: 5 of 8 edges have |r| >= 0.3 > test-cor-input.R: -------------------------------------------------------------------------------- > test-cor-input.R: Note: This is a series of linked solutions, not a fitted hierarchical model. > test-cor-input.R: Cross-level edges are descriptive score correlations. Per-level fit indices > test-cor-input.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level -- > test-cor-input.R: they do not validate the edges or the hierarchy itself. > test-cor-input.R: > test-cor-input.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-cor-input.R: Variables: 6 > test-cor-input.R: n: 875 > test-cor-input.R: Basis: (user-supplied matrix) > test-cor-input.R: Tested k: 1-5 > test-cor-input.R: > test-cor-input.R: -- Criteria (k = 1-5) -- > test-cor-input.R: > test-cor-input.R: k PA-PC PA-FA MAP VSS-1 VSS-2 > test-cor-input.R: 1 v v 0.0461* 0.7025 0.0000 > test-cor-input.R: 2 - v 0.1279 0.7919* 0.8159 > test-cor-input.R: 3 - v 0.2614 0.7666 0.8879 > test-cor-input.R: 4 - - 0.4712 0.6796 0.9054* > test-cor-input.R: 5 - - 1.0000 0.7009 0.8935 > test-cor-input.R: v retained * optimal k - not retained > test-cor-input.R: + CD skipped (requires raw data; not available for matrix input). > test-cor-input.R: > test-cor-input.R: -- Recommendations -- > test-cor-input.R: > test-cor-input.R: * PA-PC: k <= 1 > test-cor-input.R: * PA-FA: k <= 3 > test-cor-input.R: * MAP: k = 1 > test-cor-input.R: * VSS-1: k = 2 > test-cor-input.R: * VSS-2: k = 4 > test-cor-input.R: Consensus range: k = 1-4 > test-cor-input.R: -------------------------------------------------------------------------------- > test-cor-input.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-cor-input.R: above the consensus to observe factor fragmentation is intentional. > test-cor-input.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-cor-input.R: 2023). PA-FA and CD are more conservative. Use the range. > test-cor-input.R: > test-cor-input.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------ > test-cor-input.R: Variables: 6 > test-data.R: i Running parallel analysis (5 iterations, PC + FA)... > test-cor-input.R: n: 875 > test-cor-input.R: Basis: (user-supplied matrix) > test-cor-input.R: Tested k: 1-5 > test-cor-input.R: > test-cor-input.R: -- Criteria (k = 1-5) -- > test-cor-input.R: > test-cor-input.R: k PA-PC PA-FA MAP VSS-1 VSS-2 > test-cor-input.R: 1 v v 0.0461* 0.7025 0.0000 > test-cor-input.R: 2 - v 0.1279 0.7919* 0.8159 > test-cor-input.R: 3 - v 0.2614 0.7666 0.8879 > test-cor-input.R: 4 - - 0.4712 0.6796 0.9054* > test-cor-input.R: 5 - - 1.0000 0.7009 0.8935 > test-cor-input.R: v retained * optimal k - not retained > test-cor-input.R: + CD skipped (requires raw data; not available for matrix input). > test-cor-input.R: > test-cor-input.R: -- Recommendations -- > test-cor-input.R: > test-cor-input.R: * PA-PC: k <= 1 > test-cor-input.R: * PA-FA: k <= 3 > test-cor-input.R: * MAP: k = 1 > test-cor-input.R: * VSS-1: k = 2 > test-cor-input.R: * VSS-2: k = 4 > test-cor-input.R: Consensus range: k = 1-4 > test-cor-input.R: -------------------------------------------------------------------------------- > test-cor-input.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels > test-cor-input.R: above the consensus to observe factor fragmentation is intentional. > test-cor-input.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes, > test-cor-input.R: 2023). PA-FA and CD are more conservative. Use the range. > test-data.R: v Running parallel analysis (5 iterations, PC + FA)... [223ms] > test-data.R: > test-data.R: i Running MAP and VSS... > test-cor-input.R: i Running parallel analysis (5 iterations, PC + FA)... > test-data.R: v Running MAP and VSS... [228ms] > test-data.R: > test-data.R: i Running Comparison Data (CD)... > test-cor-input.R: v Running parallel analysis (5 iterations, PC + FA)... [216ms] > test-cor-input.R: > test-cor-input.R: i Running MAP and VSS... > test-cor-input.R: v Running MAP and VSS... [224ms] > test-cor-input.R: > test-cor-input.R: i Running Comparison Data (CD)... > test-cor-input.R: v Running Comparison Data (CD)... [1.1s] > test-cor-input.R: > test-data.R: v Running Comparison Data (CD)... [9.4s] > test-data.R: > test-factor-labels.R: i Redundancy pruning (direct criterion, |r| >= 0.9) flagged 3 nodes. > test-factor-labels.R: i Nodes are retained in the object; inspect with `x$prune$nodes` and `x$prune$chains`. > test-efa.R: > test-efa.R: -- Bass-Ackwards Analysis (ackwards) ------------------------------------------- > test-efa.R: Engine: efa > test-efa.R: Rotation: varimax > test-efa.R: Basis: pearson > test-efa.R: n: 2,800 > test-efa.R: k (max): 3 > test-efa.R: > test-efa.R: -- Levels -- > test-efa.R: > test-efa.R: v k = 1: 1 factor, 17.0% variance > test-efa.R: v k = 2: 2 factors, 26.1% variance > test-efa.R: v k = 3: 3 factors, 32.1% variance > test-efa.R: > test-efa.R: -- Edges -- > test-efa.R: > test-efa.R: 5 of 8 edges have |r| >= 0.3 > test-efa.R: -------------------------------------------------------------------------------- > test-efa.R: Note: This is a series of linked solutions, not a fitted hierarchical model. > test-efa.R: Cross-level edges are descriptive score correlations. Per-level fit indices > test-efa.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level -- > test-efa.R: they do not validate the edges or the hierarchy itself. > test-efa.R: Error in solve.default(r) : > test-efa.R: system is computationally singular: reciprocal condition number = 2.22747e-17 > test-factor-labels.R: i Redundancy pruning (direct criterion, |r| >= 0.9) flagged 3 nodes. > test-factor-labels.R: i Nodes are retained in the object; inspect with `x$prune$nodes` and `x$prune$chains`. > test-factorability.R: > test-factorability.R: -- Factorability screen (ackwards) --------------------------------------------- > test-factorability.R: Basis: pearson > test-factorability.R: Observations: 1,000 > test-factorability.R: Variables: 16 > test-factorability.R: N:p ratio: 62.5:1 > test-factorability.R: > test-factorability.R: -- Sampling adequacy -- > test-factorability.R: > test-factorability.R: Overall KMO: 0.86 (meritorious) > test-factorability.R: > test-factorability.R: Bartlett's test of sphericity: chi-square(120) = 6476.3, p < .001 > test-factorability.R: > test-factorability.R: -- Identifiability -- > test-factorability.R: > test-factorability.R: Ledermann bound: at most 10 common factors are identifiable from 16 variables > test-factorability.R: (EFA/ESEM; PCA is unbounded). > test-factorability.R: -------------------------------------------------------------------------------- > test-factorability.R: KMO bands (Kaiser 1974), the N:p >= 5/10 rules, and Bartlett at .05 are widely > test-factorability.R: used *rules of thumb*, not settled thresholds -- required N depends on > test-factorability.R: communalities and factor overdetermination (MacCallum et al. 1999). Read the > test-factorability.R: numbers, not a pass/fail. > test-factorability.R: > test-factorability.R: -- Factorability screen (ackwards) --------------------------------------------- > test-factorability.R: Basis: pearson > test-factorability.R: Observations: 1,000 > test-factorability.R: Variables: 16 > test-factorability.R: N:p ratio: 62.5:1 > test-factorability.R: > test-factorability.R: -- Sampling adequacy -- > test-factorability.R: > test-factorability.R: Overall KMO: 0.86 (meritorious) > test-factorability.R: > test-factorability.R: Bartlett's test of sphericity: chi-square(120) = 6476.3, p < .001 > test-factorability.R: > test-factorability.R: -- Identifiability -- > test-factorability.R: > test-factorability.R: Ledermann bound: at most 10 common factors are identifiable from 16 variables > test-factorability.R: (EFA/ESEM; PCA is unbounded). > test-factorability.R: -------------------------------------------------------------------------------- > test-factorability.R: KMO bands (Kaiser 1974), the N:p >= 5/10 rules, and Bartlett at .05 are widely > test-factorability.R: used *rules of thumb*, not settled thresholds -- required N depends on > test-factorability.R: communalities and factor overdetermination (MacCallum et al. 1999). Read the > test-factorability.R: numbers, not a pass/fail. > test-label_template.R: c( > test-label_template.R: "m1f1" = "m1f1", > test-label_template.R: "m2f1" = "m2f1", > test-label_template.R: "m2f2" = "m2f2" > test-label_template.R: ) > test-label_template.R: `label_template()` scaffold (id style): > test-interpret.R: > test-interpret.R: -- Salient factors by item (ackwards) ------------------------------------------ > test-interpret.R: Engine: pca > test-interpret.R: Cut: |loading| >= 0.25 > test-interpret.R: Top-n: all > test-interpret.R: > test-interpret.R: -- Level 3 (3 factors) -- > test-interpret.R: > test-interpret.R: x1: First indicator > test-interpret.R: m3f1 [-0.939] > test-interpret.R: m3f3 [0.279] > test-interpret.R: > test-interpret.R: x2 > test-interpret.R: m3f1 [-0.955] > test-interpret.R: > test-interpret.R: x3 > test-interpret.R: m3f1 [-0.950] > test-interpret.R: > test-interpret.R: x4 > test-interpret.R: m3f2 [0.937] > test-interpret.R: > test-interpret.R: x5 > test-interpret.R: m3f2 [0.952] > test-interpret.R: > test-interpret.R: x6 > test-interpret.R: m3f2 [0.956] > test-interpret.R: -------------------------------------------------------------------------------- > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings") > test-interpret.R: for the full matrix. > test-interpret.R: > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------ > test-interpret.R: Engine: pca > test-interpret.R: Cut: |loading| >= 0.25 > test-interpret.R: Top-n: all > test-interpret.R: > test-interpret.R: -- Level 3 (3 factors) -- > test-interpret.R: > test-interpret.R: m3f1 > test-interpret.R: x2 [-0.955] > test-interpret.R: x3 [-0.950] > test-interpret.R: x1: First indicator [-0.939] > test-interpret.R: > test-interpret.R: m3f2 > test-interpret.R: x6 [0.956] > test-interpret.R: x5 [0.952] > test-interpret.R: x4 [0.937] > test-interpret.R: > test-interpret.R: m3f3 > test-interpret.R: x1: First indicator [0.279] > test-interpret.R: -------------------------------------------------------------------------------- > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings") > test-interpret.R: for the full matrix. > test-interpret.R: > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------ > test-interpret.R: Engine: pca > test-interpret.R: Cut: |loading| >= 0.3 > test-interpret.R: Top-n: all > test-interpret.R: > test-interpret.R: -- Level 1 (1 factor) -- > test-interpret.R: > test-interpret.R: m1f1 > test-interpret.R: x4 [0.709] > test-interpret.R: x5 [0.706] > test-interpret.R: x6 [0.706] > test-interpret.R: x3 [-0.701] > test-interpret.R: x2 [-0.676] > test-interpret.R: x1 [-0.662] > test-interpret.R: > test-interpret.R: -- Level 2 (2 factors) -- > test-interpret.R: > test-interpret.R: m2f1 > test-interpret.R: x6 [0.954] > test-interpret.R: x5 [0.951] > test-interpret.R: x4 [0.946] > test-interpret.R: > test-interpret.R: m2f2 > test-interpret.R: x2 [-0.952] > test-interpret.R: x1 [-0.944] > test-interpret.R: x3 [-0.941] > test-interpret.R: > test-interpret.R: -- Level 3 (3 factors) -- > test-interpret.R: > test-interpret.R: m3f1 > test-interpret.R: x2 [-0.955] > test-interpret.R: x3 [-0.950] > test-interpret.R: x1 [-0.939] > test-interpret.R: > test-interpret.R: m3f2 > test-interpret.R: x6 [0.956] > test-interpret.R: x5 [0.952] > test-interpret.R: x4 [0.937] > test-interpret.R: -------------------------------------------------------------------------------- > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings") > test-interpret.R: for the full matrix. > test-interpret.R: > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------ > test-interpret.R: Engine: pca > test-interpret.R: Cut: |loading| >= 0.3 > test-interpret.R: Top-n: all > test-interpret.R: > test-interpret.R: -- Level 1 (1 factor) -- > test-interpret.R: > test-interpret.R: m1f1 > test-interpret.R: x4 [0.709] > test-interpret.R: x5 [0.706] > test-interpret.R: x6 [0.706] > test-interpret.R: x3 [-0.701] > test-interpret.R: x2 [-0.676] > test-interpret.R: x1 [-0.662] > test-interpret.R: > test-interpret.R: -- Level 2 (2 factors) -- > test-interpret.R: > test-interpret.R: m2f1 > test-interpret.R: x6 [0.954] > test-interpret.R: x5 [0.951] > test-interpret.R: x4 [0.946] > test-interpret.R: > test-interpret.R: m2f2 > test-interpret.R: x2 [-0.952] > test-interpret.R: x1 [-0.944] > test-interpret.R: x3 [-0.941] > test-interpret.R: > test-interpret.R: -- Level 3 (3 factors) -- > test-interpret.R: > test-interpret.R: m3f1 > test-interpret.R: x2 [-0.955] > test-interpret.R: x3 [-0.950] > test-interpret.R: x1 [-0.939] > test-interpret.R: > test-interpret.R: m3f2 > test-interpret.R: x6 [0.956] > test-interpret.R: x5 [0.952] > test-interpret.R: x4 [0.937] > test-interpret.R: -------------------------------------------------------------------------------- > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings") > test-interpret.R: for the full matrix. > test-interpret.R: > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------ > test-interpret.R: Engine: pca > test-interpret.R: Cut: |loading| >= 0.99 > test-interpret.R: Top-n: all > test-interpret.R: No items met the |loading| >= 0.99 threshold. > test-interpret.R: > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------ > test-interpret.R: Engine: pca > test-interpret.R: Cut: |loading| >= 0.3 > test-interpret.R: Top-n: all > test-interpret.R: > test-interpret.R: -- Level 3 (3 factors) -- > test-interpret.R: > test-interpret.R: m3f1 > test-interpret.R: A4: Love children [0.778] > test-interpret.R: A5: Make people feel at ease [0.735] > test-interpret.R: A3: Know how to comfort others [0.728] > test-interpret.R: A2: Inquire about others' well-being [0.562] > test-interpret.R: > test-interpret.R: m3f2 > test-interpret.R: A1: Am indifferent to the feelings of others [-0.919] > test-interpret.R: A2: Inquire about others' well-being [0.529] > test-interpret.R: A3: Know how to comfort others [0.387] > test-interpret.R: > test-interpret.R: m3f3 > test-interpret.R: C1: Am exacting in my work [0.995] > test-interpret.R: -------------------------------------------------------------------------------- > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings") > test-interpret.R: for the full matrix. > test-interpret.R: > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------ > test-interpret.R: Engine: pca > test-interpret.R: Cut: |loading| >= 0.3 > test-interpret.R: Top-n: all > test-interpret.R: > test-interpret.R: -- Level 3 (3 factors) -- > test-interpret.R: > test-interpret.R: m3f1 > test-interpret.R: A4: Love children [0.778] > test-interpret.R: A5: Make people feel at ease [0.735] > test-interpret.R: A3: Know how to comfort others [0.728] > test-interpret.R: A2: Inquire about others' well-being [0.562] > test-interpret.R: > test-interpret.R: m3f2 > test-interpret.R: A1: Am indifferent to the feelings of others [-0.919] > test-interpret.R: A2: Inquire about others' well-being [0.529] > test-interpret.R: A3: Know how to comfort others [0.387] > test-interpret.R: > test-interpret.R: m3f3 > test-interpret.R: C1: Am exacting in my work [0.995] > test-interpret.R: -------------------------------------------------------------------------------- > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings") > test-interpret.R: for the full matrix. > test-ledger-anchors.R: Error: ! testthat subprocess exited in file 'test-ledger-anchors.R'. Caused by error: ! R session crashed with exit code -1073741819 Backtrace: ▆ 1. └─testthat::test_check("ackwards") 2. └─testthat::test_dir(...) 3. └─testthat:::test_files(...) 4. └─testthat:::test_files_parallel(...) 5. ├─withr::with_dir(...) 6. │ └─base::force(code) 7. ├─testthat::with_reporter(...) 8. │ └─base::tryCatch(...) 9. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers) 10. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]]) 11. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler) 12. └─testthat:::parallel_event_loop_chunky(queue, reporters, ".") 13. └─queue$poll(Inf) 14. └─base::lapply(...) 15. └─testthat (local) FUN(X[[i]], ...) 16. └─private$handle_error(msg, i) 17. └─cli::cli_abort(...) 18. └─rlang::abort(...) Execution halted Flavor: r-oldrel-windows-x86_64