Exploratory factor analysis on multiple data imputations

Fits efa_fit() to each of several imputed datasets, aligns the factor solutions to a common factor space, and pools the resulting estimates and selected fit quantities across imputations.

Usage

efa_mi(
  data_list,
  p = 0.05,
  target_method = c("first_target", "consensus"),
  align_unrotated = c("signed_tucker_congruence", "none", "procrustes"),
  fit_pool_method = c("D2"),
  consensus_args = list(),
  procrustes_args = list(),
  rmsea_ci_level = 0.9,
  rmsr_upper = lifecycle::deprecated(),
  ...
)

Arguments

data_list: A list of length $m$, where $m$ is the number of imputations. Each list element is a data frame or matrix of raw data, or a correlation matrix. See argument x in efa_fit(). A mids object from mice must be converted first, with mice::complete(x, "all").
p: Numeric in $(0, 1)$. One minus the confidence level for the pooled confidence intervals, whichever se method produced them ("information", "np-boot", or "sandwich"). For example, p = .05 gives 95% intervals.
target_method: Character. How rotated solutions are aligned across imputations before pooling: "first_target" (the default) aligns every imputation to the first imputation's rotated solution, while "consensus" refines a centroid target by Generalized Procrustes Analysis, started from the medoid imputation so that the pooled rotated solution does not depend on the order of data_list (orthogonal rotations only). See Aligning solutions across imputations in Details.
align_unrotated: Character. How unrotated loadings are aligned before pooling: "signed_tucker_congruence" (the default; sign/permutation via Tucker congruence, anchored on the medoid imputation and returned in the extraction's canonical gauge), "procrustes" (orthogonal Procrustes to the first imputation), or "none". See Aligning solutions across imputations in Details.
fit_pool_method: Character. Only "D2" is implemented for pooling chi-square-type fit. If no chi-square is available, only residual-based fit and descriptive quantities are returned. See Pooling the model chi-square and fit indices in Details.
consensus_args: List of additional arguments controlling the GPA-consensus iteration when target_method = "consensus". Recognised tuning parameters include the convergence tolerances tol and loss_tol, the iteration bounds min_iter and max_iter, the target-update damping alpha, the multi-start controls multi_start and starts, and start, which overrides the medoid imputation the iteration is otherwise started from.
procrustes_args: List of efa_procrustes() algorithm controls for fixed-target alignment, for example oblique_maxit or oblique_random_starts. The loadings A, the alignment Target, the rotation family, and the cross-product S are derived from the imputations and cannot be set here.
rmsea_ci_level: Numeric. Confidence level for the RMSEA CI.
rmsr_upper: Deprecated and ignored. efa_mi() now always computes RMSR the same way, from the unique off-diagonal residuals; SRMR is reported alongside it. Supplying it to efa_mi() signals a deprecation warning; the superseded EFA_POOLED() accepts it silently.
...: Additional arguments passed to efa_fit() (e.g. estimator, rotation, se, n_factors, N). These select the estimator, rotation, standard-error method, and fit indices used for every imputation; see efa_fit() for the available options, their properties, and which combinations are valid. Two of them shape the pooled object rather than a single fit: seed sets the random state once for the whole efa_mi() call – every component bootstrap and every random-start rotation draws from it, so a seeded call is reproducible as a whole, and the caller's random stream is restored afterwards – and b_boot sets the number of bootstrap replicates drawn per imputation under se = "np-boot", which is what the pooled within-imputation variances are estimated from and is recorded in settings$b_boot. The estimate_control() and rotate_control() objects are accepted through ... as well, although they are not declared formals: pass them as estimate_control = / rotate_control = exactly as you would to efa_fit().

Value

A list of class c("efa_mi", "EFA_POOLED", "efa", "EFA") containing pooled estimates, residuals, fit indices, the individual fits, and MI diagnostics. The trailing legacy classes keep inherits(x, "EFA_POOLED") and the single-fit EFA accessors and S3 dispatch working. In addition to the slots inherited from efa_fit() (including SE, CI, and, on the bootstrap path, replicates), the object carries:

SE, CI: Pooled standard errors and confidence intervals, named as efa_fit() names them: where a pooled communality standard error and interval are produced, they are SE$communalities and CI$communalities on every route. The Rubin routes (se = "information", se = "np-boot") additionally return them under the compatibility alias h2, which holds the same values. The analytic route builds the communality family only when a rotation was requested; an unrotated analytic pool reports uniquenesses instead.
fit_indices: The pooled fit indices. Every route reports chi, df, p_chi, CAF, CFI, TLI, RMSEA, RMSEA_LB, RMSEA_UB, AIC, BIC, ECVI, RMSR, SRMR, chi_null, df_null, p_null, and pool_method under those names and in that order. pool_method records the rule the model chi-square was pooled with ("D2"); it is NA on the se = "sandwich" (MI2S) path, which fits once on the pooled inputs and reports that fit's own scaled statistic rather than pooling several. On that path a few extra scaled-statistic fields are appended after the common block, for advanced diagnostics.
standardized_residuals: The pooled residuals divided by their pooled bootstrap standard errors, with a zero diagonal. Returned on the se = "np-boot" path only, the one route that pools a residual standard error.
MI: Multiple-imputation diagnostics for each pooled parameter family. On the bootstrap path: unrot_loadings, communalities, residuals, optionally rot_loadings, Phi, Structure, and fit_indices_descriptive, plus integer vectors bootstrap_source_failures (replicates the component efa_fit() could not fit), bootstrap_rotation_failures (replicates whose Procrustes alignment to the target was invalid), and bootstrap_rotation_valid (those that entered the pool, B - source - rotation failures). Both paths use the plain Rubin (1987) df. On the analytic path (se = "information"): unrot_loadings and uniquenesses, plus, when a rotation was requested, rot_loadings, communalities, and (oblique) Phi and Structure. The communality family is keyed by its canonical name here, without the SE/CI alias, so each family is counted once in the printed FMI/RIV summary. Each per-family entry is a list with RIV (relative increase in variance), FMI (the fraction of missing information, reported as Rubin's asymptotic $\lambda = RIV / (1 + RIV)$), and df; the rotated families on the analytic path additionally carry a method string recording the gauge alignment used ("gauge_invariant" for communalities and "signed_permutation_approx" for rotated loadings and, for oblique rotations, factor correlations and structure coefficients). fit_indices_descriptive, on the bootstrap path, pools every fit index the bootstrap replicates carry, so the structural constants among them (df, df_null) appear with a standard error of 0. The RMSEA confidence bounds are not among them: the replicate fits run without confidence intervals, so no per-replicate value exists to pool.
mi_fit: On the se = "sandwich" (MI2S) path only: the single efa_fit() fit on the pooled correlation matrix $\bar r$ and pooled asymptotic covariance $\tilde\Gamma$. Its orig_R is $\bar r$ and its Gamma is $\tilde\Gamma$; the pooled SE and CI are taken from it, as are the pooled fit_indices (put into the common order above and extended with pool_method, while mi_fit keeps efa_fit()'s own layout). MI is NULL on this path because the imputation uncertainty is carried by $\tilde\Gamma$ rather than by per-parameter Rubin pooling.
mi_diagnostics: Diagnostics for the pooled model fit, NULL on the se = "sandwich" (MI2S) path, where there is one fit and no D2 pool. m is the number of imputations that entered the pool. D2_F, D2_df1, D2_df2, D2_chi_asymptotic, ARIV and FMI describe the D2 pool of the model chi-square (the average relative increase in variance and the fraction of missing information it implies), and chi_bar_naive is the plain mean of the per-imputation statistics for comparison; the *_null entries are the same quantities for the independence baseline. D2_F is the rule's raw statistic and is reported unfloored, so it is negative whenever the between-imputation variability of the component statistics exceeds the pooled discrepancy – a diagnostic of the pool rather than a fit statistic. The reported fit is not affected: the pooled chi-square is floored at zero and its p-value is 1 in that case. chi_cfi and chi_null_cfi are the pooled model and baseline chi-squares on the common $N - 1$ noncentrality scale. chi_cfi is the statistic the reported RMSEA is formed from; the pair also gives a reference CFI formed the conventional way, 1 - (chi_cfi - df) / (chi_null_cfi - df_null) (and analogously for TLI) – a different quantity from the reported CFI/TLI, which average the per-imputation indices.
mi_admissibility: Admissibility and convergence of the component fits, kept on the pooled object so a saved solution carries the record independently of fits: m (the number of imputations that entered the pool), heywood_imputations (the indices of the fits with at least one Heywood case – an improper solution where a variable's communality is at or above 1, or its uniqueness is fixed at the estimation boundary), n_heywood_items (the number of flagged variables per imputation), nonconverged (the indices whose extraction reported a non-zero convergence code), and iter (the iterations each extraction used). Averaging aligned solutions pulls boundary communalities back inside the admissible range, so a pooled matrix with no Heywood case can still rest on component fits that had them; summary() reports the pooled count together with these.
fits: The list of $m$ component efa_fit() fits, in the order of data_list, kept for per-imputation diagnostics. On the MI2S path these are the per-imputation fits whose inputs were pooled, not the pooled fit itself (which is mi_fit).
alignment: Metadata from aligning the rotated solutions, NULL when no rotation was requested or on the MI2S path (one fit, one gauge). Under target_method = "first_target": the method used, the target it aligned to, the per-imputation target_rotations, the indices of any point_rotation_failures, and whether every inner alignment converged. Under target_method = "consensus" it is the full efa_procrustes()-based GPA record: the converged target, the aligned_loadings and aligned_phi, the iteration history, convergence flags, and the multi-start summary.
settings: The component fits' efa_fit() settings with the pooling settings added: pooled (always TRUE), pooled_N and N (the mean N across imputations), n_imputations, component_se (the se the component fits used), target_method, align_unrotated, fit_pool_method, p, ci and rmsea_ci_level. se records what was actually pooled, so it is "none" when pooled standard errors could not be produced although the component fits computed them (component_se keeps the request).

Details

efa_mi() is the multiple-imputation route to handling missing data: several imputed datasets are each fitted with efa_fit() and the solutions pooled. A single-fit alternative is full-information maximum likelihood, available directly in efa_fit() as cor_method = "fiml", which EM-estimates a two-stage correlation from one raw dataset with missing values. Both feed the same correlation-scale EFA core and differ only in how the missingness is handled; FIML is intentionally not routed through efa_mi(), which is a multi-fit pooler by construction.

Both routes assume the values are missing at random (MAR). Which one to prefer is largely practical: FIML is a single, efficient fit and is the simpler default when the analysis model is the whole story, whereas multiple imputation is more flexible when the imputation model should draw on auxiliary variables not in the factor model, or when the same imputations feed several downstream analyses.

Standard-error pooling routes

The pooling pathway is selected automatically from the se method recorded on the component efa_fit() fits, which must be identical across imputations:

se = "none": no standard errors are pooled.
se = "information": the per-imputation expected-information standard errors are pooled with Rubin's (1987) rules (Wald intervals).
se = "sandwich": the two-stage pooled-inputs (MI2S) approach fits a single model on the Rubin-pooled correlation matrix and asymptotic covariance.
se = "np-boot": the non-parametric bootstrap replicates are re-aligned to the multiple-imputation target and Rubin-pooled.

On the information and np-boot routes, if pooled standard errors cannot be produced (for example an unreliable analytic covariance or too few bootstrap replicates) the pool falls back to point-estimate-only pooling and downgrades settings$se to "none". The MI2S route is the exception: its single fit fuses the point estimates and standard errors through the pooled asymptotic covariance, so a structural failure aborts directly rather than falling back.

Aligning solutions across imputations

The same efa_fit() model is fitted to each imputed dataset and the solutions are put into a common factor space before averaging. For oblique solutions the factor intercorrelations are aligned together with the loadings so the model stays internally consistent.

target_method controls how rotated solutions are aligned. "first_target" (the default) aligns every imputation to the first imputation's rotated solution by one Procrustes rotation each. "consensus" instead refines a centroid target by Generalized Procrustes Analysis (Gower 1975; van Ginkel & Kroonenberg 2014; Lorenzo-Seva & Van Ginkel 2016), starting from the medoid imputation's rotated solution – the one closest in aligned squared distance to all the others. "consensus" is supported for orthogonal rotations only.

Factor loadings are only unique up to rotation; "gauge" here means which particular rotation, or orientation, a solution is expressed in. The two methods differ in the rotational gauge the pooled solution ends up in, and so in how it responds to the order of data_list. The GPA iteration moves its target toward the centroid but keeps the gauge of the solution it started from, so starting it at the medoid – a property of the set, not of the list order – makes the pooled rotated solution invariant to that order, as the pooled unrotated solution already is. "first_target" anchors on the first imputation by construction, so an atypical first imputation fixes the orientation for every other one. Permuting data_list therefore moves the pooled pattern – by a few hundredths of a loading unit when imputations are similar, more when they disagree. For oblique rotations, the factor correlations move with it. Where the two anchors coincide – and, more generally, where the imputations agree – the two methods give effectively the same pooled estimate and "consensus" is simply the more expensive; where they do not, the pooled patterns differ by the rotation between the two gauges. Passing start through consensus_args overrides the medoid anchor and makes the consensus order dependent again.

align_unrotated controls how unrotated loadings are aligned before pooling: "signed_tucker_congruence" (the default) matches them up to factor reordering and sign changes, "procrustes" aligns them to the first imputation by orthogonal Procrustes rotation, and "none" averages them as returned by efa_fit().

The default anchors the matching on the medoid imputation (defined above) rather than on whichever imputation happens to come first, so the pooled unrotated solution does not depend on the order of data_list. The rotated solution is aligned separately, against a reference chosen by target_method, and still depends on that reference.

The pooled unrotated matrix is then returned in the same gauge as a single efa_fit() fit. Its identifying constraint differs by extraction method – a principal-axis extraction and a maximum-likelihood extraction fix the rotation differently (Anderson & Rubin 1956; Lawley & Maxwell 1971) – and is read off each component fit automatically, so the pooled matrix can be compared element-by-element with an efa_fit() solution. A solution that meets neither constraint – an improper one, say – is left as aligned. The correction is a common orthogonal rotation, so communalities, the total variance accounted for, the model-implied correlation matrix, the residuals, and RMSR are unchanged; only the split of variance across factors moves. "procrustes" and "none" keep their first-imputation anchor and are returned as aligned.

Pooling point estimates

Point estimates are pooled by arithmetic averaging after alignment. For oblique rotations the structure matrix is recomputed from the pooled pattern matrix and pooled factor correlations, $Structure = \Lambda \Phi$, and communalities are the diagonal of the reproduced correlation matrix, $diag(\Lambda \Phi \Lambda')$ for oblique rotations and $diag(\Lambda \Lambda')$ otherwise. Residuals are not averaged across imputations; they are the pooled observed correlation matrix minus the model-implied correlation of the pooled solution, so RMSR/SRMR are based on these pooled residuals. Both are returned, though the print and summary methods show SRMR only.

Pooling the model chi-square and fit indices

The model chi-square and the indices derived from it (ECVI and the descriptive AIC/BIC) are pooled with the D2 rule (Li, Meng, Raghunathan & Rubin, 1991), not arithmetically averaged. RMSEA is pooled by the same rule but from a second D2 pool of the per-imputation discrepancies taken on the uncorrected $N - 1$ scale. The printed RMSEA therefore does not reconcile by hand with the printed chi-square; the statistic it is formed from is chi_cfi in mi_diagnostics. Because D2 shrinks the pooled chi-square in proportion to the between-imputation variability, the pooled RMSEA can fall below the mean of the per-imputation RMSEAs; read it together with the per-imputation fit. The incremental indices CFI (Bentler, 1990) and TLI (Tucker & Lewis, 1973) are instead the average of the per-imputation indices, which keeps them consistent with the component fits and avoids out-of-range values; the separately pooled model and baseline chi-squares those indices would be formed from remain available in mi_diagnostics. AIC and BIC, if returned, are chi-square-derived descriptive quantities and are not likelihood-based MI information criteria. They are reported only where the component fits report them: whenever a component withholds them – any cor_method = "fiml" fit, and any fit whose chi-square is a scaled statistic, such as se = "sandwich" – the pooled AIC, BIC, and ECVI are NA too, matching what efa_fit() returns for a single such fit. On the sandwich/MI2S route the chi-square is the single fit's scaled statistic rather than a D2 pool.

Bootstrap pooling (np-boot)

If each component efa_fit() call was run with se = "np-boot", pooled bootstrap SEs and Wald-type MI confidence intervals are computed for loadings, communalities, residuals, and, when applicable, factor correlations and structure coefficients. The unrotated bootstrap replicates are re-aligned to the final MI target before the within-imputation covariance is estimated, and Rubin pooling is applied with $T = Ubar + (1 + 1/m) B$. The confidence level of the pooled intervals is set by p, not by the component efa_fit() calls' ci.

Analytic pooling (information)

With se = "information", the analytic unrotated-loading and uniqueness SEs returned by each fit are pooled element-wise with Rubin's rules ($T = Ubar + (1 + 1/m) B$), with Wald intervals on the plain Rubin (1987) degrees of freedom (the analytic loadings are asymptotically normal, so the Barnard-Rubin (1999) adjustment reduces to this form). NA propagation is fail-closed: if any imputation is NA at an element, all pooled outputs for that element are NA. When a rotation was requested, the rotated loadings, communalities, and (for oblique rotations) factor correlations and structure coefficients are pooled as well; residual SE pooling is available only on the bootstrap path. Under align_unrotated = "procrustes" the full unrotated covariance vcov_unrot_loadings (populated by se = "information") is propagated through the alignment, so it must be present and reliable on every fit. The default alignment also mixes loading columns, through the common canonical-gauge rotation, and so propagates the same covariance; where a fit does not carry it, the unrotated standard errors are returned as NA rather than aborting, and the remaining families still pool.

A rotated-loading standard error is conditional on the rotation criterion used (see References). For both orthogonal and oblique rotations the within-imputation variance is therefore each fit's own criterion-aware delta-method rotated SE (the quantity efa_fit() returns), reused after a signed-permutation alignment to the MI target, and the between-imputation variance is the sample variance of the aligned rotated loadings. This is a deliberate approximation – each SE is conditional on its own fit's rotation optimum rather than on a common gauge – and is flagged by MI$<param>$method = "signed_permutation_approx". Communalities are rotation-invariant and pool element-wise. For a fully gauge-consistent rotated uncertainty, cross-check with se = "np-boot".

Two-stage pooling (sandwich / MI2S)

With se = "sandwich" (robust SEs from a polychoric/tetrachoric or continuous-Pearson asymptotic covariance), pooling follows the two-stage, pooled-inputs approach (Chung & Cai 2019; Sriutaisuk, Liu, Chung, Kim & Gu 2025): the correlation matrix and the asymptotic covariance of its off-diagonal entries are Rubin-pooled across imputations, and a single EFA model is fitted to the pooled correlation with the pooled covariance, $\tilde\Gamma$, as the robust meat (its diagonal as the weights for estimator = "DWLS"). Because there is only one fit and one rotational gauge, this route bypasses the per-imputation alignment: target_method and align_unrotated do not apply. The fitted object carries native scaled-shifted chi-square statistics and sandwich SEs that already reflect the multiple-imputation uncertainty, so the chi-square is not D2-pooled and the likelihood-ratio-based AIC/BIC/ECVI are NA; it is returned in the mi_fit slot, with the per-imputation fits retained for diagnostics. The pooled fit uses the same estimate_control() and rotate_control() tuning (including any rotation-engine extras) as the per-imputation fits. At least 20 imputations are recommended for the scaled-shifted statistic, and more (around 100) at higher rates of missingness (Sriutaisuk et al. 2025). The polychoric/tetrachoric (ordinal) case is the primary, best-evaluated target; the continuous-Pearson case uses the same recipe but is less benchmarked.

Conditions

Errors and warnings raised by efa_mi() are classed, with an efa_pooled_ prefix (efa_consensus_ for the consensus target) – except the dots validation shared with efa_fit(), which signals efa_flat_knob_in_dots or efa_renamed_arg – so they can be caught programmatically. The message shown explains what went wrong and, where relevant, how to fix it.

References

Anderson, T. W., & Rubin, H. (1956). Statistical inference in factor analysis. In Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability (Vol. 5, pp. 111-150). University of California Press.

Archer, C. O., & Jennrich, R. I. (1973). Standard errors for rotated factor loadings. Psychometrika, 38(4), 581-592.

Barnard, J., & Rubin, D. B. (1999). Small-sample degrees of freedom with multiple imputation. Biometrika, 86(4), 948-955.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238-246.

Chung, S., & Cai, L. (2019). Alternative multiple imputation inference for categorical structural equation modeling. Multivariate Behavioral Research, 54(3), 323-337.

Gower, J. C. (1975). Generalized Procrustes analysis. Psychometrika, 40(1), 33-51.

Jennrich, R. I. (1973). Standard errors for obliquely rotated factor loadings. Psychometrika, 38(4), 593-604.

Jennrich, R. I. (1974). Simplified formulae for standard errors in maximum-likelihood factor analysis. British Journal of Mathematical and Statistical Psychology, 27(1), 122-131.

Lawley, D. N., & Maxwell, A. E. (1971). Factor analysis as a statistical method (2nd ed.). Butterworths.

Li, K. H., Meng, X.-L., Raghunathan, T. E., & Rubin, D. B. (1991). Significance levels from repeated p-values with multiply-imputed data. Statistica Sinica, 1(1), 65-92.

Lorenzo-Seva, U., & Van Ginkel, J. R. (2016). Multiple imputation of missing values in exploratory factor analysis of multidimensional scales. Anales de Psicologia, 32(2), 596-608.

Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. Wiley.

Schoenemann, P. H. (1966). A generalized solution of the orthogonal Procrustes problem. Psychometrika, 31(1), 1-10.

Sriutaisuk, S., Liu, Y., Chung, S., Kim, H., & Gu, F. (2025). Evaluating imputation-based fit statistics in structural equation modeling with ordinal data: The MI2S approach. Educational and Psychological Measurement, 85(1), 82-113.

Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1-10.

van Ginkel, J. R., & Kroonenberg, P. M. (2014). Using generalized Procrustes analysis for multiple imputation in principal component analysis. Journal of Classification, 31(2), 242-269.

Zhang, G., & Preacher, K. J. (2015). Factor rotation and standard errors in exploratory factor analysis. Journal of Educational and Behavioral Statistics, 40(6), 579-603.

Zhang, G., Preacher, K. J., & Jennrich, R. I. (2012). The infinitesimal jackknife with exploratory factor analysis. Psychometrika, 77(4), 634-648.

Author

Andreas Soteriades, Markus Steiner

Examples


# create a list of three datasets, mimicking a list you would obtain from
# e.g. mice.
dat_list <- lapply(1:3, function(x) GRiPS_raw[sample(1:nrow(GRiPS_raw), replace = TRUE),])
mod <- efa_mi(dat_list, n_factors = 1, estimator = "ML")
#> ℹ `x` is not a correlation matrix; computing correlations from the raw data.
mod
#> 
#> Pooled EFA across 3 imputations performed with estimator = 'ML' and
#>   rotation = 'none'.
#> Pooling settings: align_unrotated = 'signed_tucker_congruence',
#>   fit_pool_method = 'D2'.
#> 
#> ── Unrotated Loadings ──────────────────────────────────────────────────────────
#> 
#>             F1    h2    u2
#> fun        .808  .652  .348
#> friends    .837  .701  .299
#> enjoy      .871  .759  .241
#> hurt       .757  .573  .427
#> part       .814  .662  .338
#> commonly   .818  .669  .331
#> chances    .783  .613  .387
#> attracted  .844  .713  .287
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                 F1
#> SS loadings   5.343
#> Prop Tot Var   .668
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> D2-pooled χ²(20) = 0.00, p = 1.000
#> CFI (avg. over imputations): .99
#> TLI (avg. over imputations): .98
#> RMSEA [90% CI]: .00 [.00; .00]
#> AIC: -40.00
#> BIC: -133.94
#> ECVI: 0.04
#> CAF: .50
#> SRMR: .02
#> Note: the pooled χ² is the D2 statistic; its p uses the D2 reference F(20, .1),
#> not the χ²(20) tail.
#> Note: CFI and TLI are averaged over the imputations, not formed from the
#> separately pooled model and baseline statistics in `mi_diagnostics`.

# \donttest{
# add computation of standard errors and CIs
mod <- efa_mi(dat_list, n_factors = 1, estimator = "ML", se = "np-boot")
#> ℹ `x` is not a correlation matrix; computing correlations from the raw data.
mod
#> 
#> Pooled EFA across 3 imputations performed with estimator = 'ML' and
#>   rotation = 'none'.
#> Pooling settings: align_unrotated = 'signed_tucker_congruence',
#>   fit_pool_method = 'D2'.
#> 
#> ── Unrotated Loadings ──────────────────────────────────────────────────────────
#> 
#>             F1    h2    u2
#> fun        .808  .652  .348
#> friends    .837  .701  .299
#> enjoy      .871  .759  .241
#> hurt       .757  .573  .427
#> part       .814  .662  .338
#> commonly   .818  .669  .331
#> chances    .783  .613  .387
#> attracted  .844  .713  .287
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                 F1
#> SS loadings   5.343
#> Prop Tot Var   .668
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> D2-pooled χ²(20) = 0.00, p = 1.000
#> CFI (avg. over imputations) [95% bootstrap/MI-CI]: .99 [.96, 1.02]
#> TLI (avg. over imputations) [95% bootstrap/MI-CI]: .98 [.94, 1.02]
#> RMSEA [90% CI] [95% bootstrap/MI-CI]: .00 [.00; .00] [-.01, .14]
#> AIC [95% bootstrap/MI-CI]: -40.00 [-93.87, 197.52]
#> BIC [95% bootstrap/MI-CI]: -133.94 [-187.82, 103.58]
#> ECVI [95% bootstrap/MI-CI]: 0.04 [-.03, .33]
#> CAF [95% bootstrap/MI-CI]: .50 [.48, .52]
#> SRMR [95% bootstrap/MI-CI]: .02 [.00, .03]
#> Note: the pooled χ² is the D2 statistic; its p uses the D2 reference F(20, .1),
#> not the χ²(20) tail.
#> Note: CFI and TLI are averaged over the imputations, not formed from the
#> separately pooled model and baseline statistics in `mi_diagnostics`.
#> 
#> Note: Bootstrap/MI CIs based on 1000 bootstrap samples per imputation.
# }