CRAN Package Check Results for Package ackwards

Last updated on 2026-09-25 00:50:05 CEST.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	0.2.0	5.89	652.42	658.31	OK
r-devel-linux-x86_64-debian-gcc	0.2.0	6.18	400.88	407.06	OK
r-devel-linux-x86_64-fedora-clang	0.2.0			420.05	OK
r-devel-linux-x86_64-fedora-gcc	0.2.0			419.21	OK
r-devel-windows-x86_64	0.2.0	11.00	362.00	373.00	OK
r-patched-linux-x86_64	0.2.0	9.08	622.85	631.93	OK
r-release-linux-x86_64	0.2.0	7.43	643.19	650.62	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	13.00	497.00	510.00	OK

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