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Exploratory factor analysis (EFA)

Source: R/efa_fit.R
efa_fit.Rd

This function does an EFA with either PAF, ML, ULS/MINRES, or DWLS with or without subsequent rotation. Estimation and rotation are controlled through the control objects built by estimate_control() and rotate_control(); each accepts a type ("EFAtools", "SPSS", "psych", or "none") that fills in its remaining settings.

Usage

efa_fit(
  x,
  n_factors,
  N = NA,
  estimator = c("PAF", "ML", "ULS", "MINRES", "DWLS"),
  rotation = c("none", "varimax", "equamax", "quartimax", "geominT", "bentlerT",
    "bifactorT", "promax", "oblimin", "quartimin", "simplimax", "bentlerQ", "geominQ",
    "bifactorQ"),
  se = c("none", "information", "sandwich", "np-boot"),
  cor_method = c("pearson", "spearman", "kendall", "poly", "tetra", "fiml"),
  use = c("pairwise.complete.obs", "all.obs", "complete.obs", "everything",
    "na.or.complete"),
  estimate_control = NULL,
  rotate_control = NULL,
  b_boot = 1000,
  ci = 0.95,
  seed = NULL,
  ...
)

Source

Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods & Research, 21, 205–229. doi: 10.1177/0049124192021002004

Grieder, S., & Steiner, M. D. (2022). Algorithmic jingle jungle: A comparison of implementations of principal axis factoring and promax rotation in R and SPSS. Behavior Research Methods, 54, 54–74. doi: 10.3758/s13428-021-01581-x

Hendrickson, A. E., & White, P. O. (1964). Promax: A quick method for rotation to oblique simple structure. British Journal of Statistical Psychology, 17 , 65–70. doi: 10.1111/j.2044-8317.1964.tb00244.x

Lorenzo-Seva, U., Timmerman, M. E., & Kiers, H. A. L. (2011). The Hull Method for Selecting the Number of Common Factors, Multivariate Behavioral Research, 46, 340-364, doi: 10.1080/00273171.2011.564527

Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187–200. doi: 10.1007/BF02289233

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

Cudeck, R. (1989). Analysis of correlation matrices using covariance structure models. Psychological Bulletin, 105, 317–327. doi: 10.1037/0033-2909.105.2.317

Olkin, I., & Siotani, M. (1976). Asymptotic distribution of functions of a correlation matrix. In S. Ikeda (Ed.), Essays in probability and statistics (pp. 235–251). Shinko Tsusho.

Jennrich, R. I. (1973). Standard errors for obliquely rotated factor loadings. Psychometrika, 38, 593–604. doi: 10.1007/BF02291497

Zhang, G., & Preacher, K. J. (2015). Factor rotation and standard errors in exploratory factor analysis. Journal of Educational and Behavioral Statistics, 40, 579–603. doi: 10.3102/1076998615606098

Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 62–83. doi: 10.1111/j.2044-8317.1984.tb00789.x

Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Sage.

Asparouhov, T., & Muthén, B. (2010). Simple second order chi-square correction. Mplus Technical Appendix.

Muthén, B., du Toit, S. H. C., & Spisic, D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. Unpublished manuscript.

Yuan, K.-H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological Methodology, 30, 165–200. doi: 10.1111/0081-1750.00078

Yuan, K.-H., Marshall, L. L., & Bentler, P. M. (2002). A unified approach to exploratory factor analysis with missing data, nonnormal data, and in the presence of outliers. Psychometrika, 67, 95–121. doi: 10.1007/BF02294711

Savalei, V., & Bentler, P. M. (2009). A two-stage approach to missing data: Theory and application to auxiliary variables. Structural Equation Modeling, 16, 477–497. doi: 10.1080/10705510903008238

Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). Wiley.

Bartlett, M. S. (1951). The effect of standardization on a Chi-square approximation in factor analysis. Biometrika, 38, 337–344.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. doi: 10.1037/0033-2909.107.2.238

Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10. doi: 10.1007/BF02291170

Browne, M. W., & Cudeck, R. (1989). Single sample cross-validation indices for covariance structures. Multivariate Behavioral Research, 24, 445–455. doi: 10.1207/s15327906mbr2404_4

Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21, 230–258. doi: 10.1177/0049124192021002005

Bentler, P. M. (1995). EQS structural equations program manual. Multivariate Software.

Arguments

x

data.frame or matrix. Dataframe or matrix of raw data or matrix with correlations. If raw data is entered, the correlation matrix is found from the data.

n_factors

numeric. Number of factors to extract. Must be at least 1 and smaller than the number of variables (the common factor model is not identified otherwise). Use efa_retain() to decide on a value.

N

numeric. The number of observations. Needs only be specified if a correlation matrix is used; with raw data, N is found from the data instead.

  • With N = NA, not all fit indices can be computed; a positive N that is very small relative to the number of variables leaves the chi-square-derived indices unavailable as well, with a warning.

  • With raw data, N is the number of cases the correlation matrix was actually computed from – see use for the general rule and how missing values change it. Under cor_method = "fiml", use is ignored and N is instead the number of cases carrying at least one observed value.

estimator

character. The estimator used to fit the EFA: "PAF" (principal axis factoring), "ML" (maximum likelihood), "ULS" (unweighted least squares; "MINRES" is an accepted alias returning identical results), or "DWLS" (diagonally weighted least squares, for ordinal data). See the Estimators section in Details for their properties and data requirements. Lower-case versions (e.g., "paf") are also accepted.

rotation

character. Either perform no rotation ("none"; default), an orthogonal rotation ("varimax", "equamax", "quartimax", "geominT", "bentlerT", or "bifactorT"), or an oblique rotation ("promax", "oblimin", "quartimin", "simplimax", "bentlerQ", "geominQ", or "bifactorQ"). See the Rotations section in Details for their properties and known issues.

se

character. Whether and how to compute standard errors (and matching confidence intervals): "none" (default), "information" (analytic standard errors from the expected Fisher information of the ML solution), "sandwich" (robust "sandwich" standard errors from raw data, which stay reliable under non-normality or a misspecified estimator weight), or "np-boot" (non-parametric bootstrap). The methods differ in their assumptions, their data requirements, and which estimator, rotation, and cor_method combinations they support; see the Standard errors section in Details.

cor_method

character. How the correlation is computed from raw data: "pearson", "spearman", or "kendall" (passed to stats::cor()); "poly" / "tetra" for polychoric / tetrachoric correlations of ordinal / binary data; or "fiml" for a two-stage full-information maximum-likelihood correlation from raw data with missing values. See the Correlation methods section in Details for their properties and the combinations they support. Default is "pearson".

use

character. Passed to stats::cor() if raw data is given as input. Default is "pairwise.complete.obs". It is ignored when cor_method = "fiml" (which handles the missingness itself, so every case contributes), and it is overridden to listwise deletion whenever an asymptotic covariance is required (the "DWLS" estimator, or se = "sandwich"), because the covariance must describe the same cases as the correlation matrix.

estimate_control

a control object from estimate_control() bundling the estimation control arguments: the type preset; the principal-axis-factoring iteration settings init_comm, criterion, criterion_type, max_iter, and abs_eigen; and the maximum-likelihood start_method. Defaults to estimate_control(), which uses the "EFAtools" type. See estimate_control().

rotate_control

a control object from rotate_control() bundling the rotation control arguments: the type preset; Kaiser normalize; the convergence precision; the factor order_type; the varimax/promax settings varimax_type and p_type; the simplimax/promax k; and random_starts. Defaults to rotate_control(), which uses the "EFAtools" type. The estimation and rotation presets are independent. See rotate_control() for details.

b_boot

numeric. The number of bootstrap samples to draw. Default is 1000. Must be at least 2, the smallest number from which a standard error is defined. Under cor_method = "fiml" each bootstrap sample re-runs the EM moment estimation, so a smaller value may be advisable.

ci

numeric. The level of the confidence intervals: the percentile intervals from the bootstrap samples under se = "np-boot", and the analytic Wald intervals under se = "information" and se = "sandwich", the corrected two-stage intervals of cor_method = "fiml" included. Must be greater than 0 and smaller than 1. Default is .95 for 95% CIs.

seed

numeric. An optional seed for the random-number generator.

...

Additional arguments forwarded to the rotation engine (usually not needed); an unrecognized one is an error, not a setting that is silently ignored.

Value

A list of class c("efa", "EFA") containing (a subset of) the following:

orig_R

Original correlation matrix.

h2_init

Initial communality estimates from PAF.

h2

Final communality estimates from the unrotated solution.

orig_eigen

Eigen values of the original correlation matrix.

init_eigen

Initial eigenvalues, obtained from the correlation matrix with the initial communality estimates as diagonal in PAF.

final_eigen

Eigenvalues obtained from the correlation matrix with the final communality estimates as diagonal.

iter

For PAF, the number of iterations until convergence. For ML, ULS, and DWLS, the number of objective-function evaluations used by the optimiser (not the number of optimiser iterations).

convergence

Integer convergence code (0 = converged), using the codes of stats::optim(). For ML and ULS it is the code from the bounded optimiser; for DWLS the fit runs a bounded warm start followed by an unconstrained polish, and the reported code is from the final polish. For PAF it is 1 if the maximum number of iterations was reached without meeting the convergence criterion and 0 otherwise. A non-zero code is also reported with a warning.

heywood

A named integer vector indicating which variables have a Heywood (improper) case in the unrotated solution; empty if there are none.

unrot_loadings

Loading matrix containing the final unrotated loadings.

vars_accounted

Matrix of explained variances and sums of squared loadings. Based on the unrotated loadings. Its rows are "SS loadings" and "Prop Tot Var", followed by "Cum Prop Tot Var", "Prop Comm Var", and "Cum Prop Comm Var"; those last three are omitted for a single-factor solution, where they would only repeat the two above them. So code that indexes a row by name (for example ["Prop Comm Var", ]) must allow for the two-row form at n_factors = 1.

fit_indices

A named list of fit indices computed from the unrotated loadings. ML and ULS report the full set: the model Chi Square (with its p-value and df), CFI, TLI, RMSEA with its 90% confidence interval, AIC, BIC, ECVI, RMSR, SRMR, and CAF. PAF and DWLS report only RMSR, SRMR, CAF, and df; the Chi-Square-based indices are NA there, unless DWLS uses se = "sandwich", which fills the full block from a scaled Chi Square instead. RMSR and SRMR are both included, but the print and summary methods display only SRMR (see Fit indices in Details). Whenever the Chi Square is scaled (se = "sandwich", or a cor_method = "fiml" fit whose correction could be formed), AIC, BIC, and ECVI are NA, and a few extra scaled-statistic fields are appended for advanced diagnostics (see Fit indices in Details). The list also carries Fm, the estimator's own objective value at the solution – on its own scale, not the discrepancy the Chi Square is built from – and the independence-model baseline chi_null, df_null, and p_null that CFI and TLI are computed from.

model_implied_R

The model implied correlation matrix.

residuals

Residual correlations, i.e., orig_R - model_implied_R

standardized_residuals

Residual correlations standardized by their bootstrap standard errors. Only returned, if se = "np-boot".

rot_loadings

Loading matrix containing the final rotated loadings. For an oblique rotation this is the pattern matrix – each variable's unique contribution from each factor, with the other factors partialled out – and is the matrix normally interpreted (see Rotations).

Phi

The factor intercorrelations (only for oblique rotations).

Structure

The structure matrix rot_loadings %*% Phi, holding the plain variable-factor correlations, which are inflated by the factor intercorrelations (only for oblique rotations).

rotmat

The rotation matrix. The rotated loadings are recovered from the unrotated loadings as unrot_loadings %*% rotmat for orthogonal rotations and for promax, and as unrot_loadings %*% t(solve(rotmat)) for the other oblique rotations.

vars_accounted_rot

Matrix of explained variances and sums of squared loadings. Based on rotated loadings and, for oblique rotations, the factor intercorrelations. Same rows as vars_accounted; it is returned only when a rotation was actually applied, which never happens for a single-factor solution, so it always has the five-row form.

settings

A list of the settings used, including seed (NULL when none was supplied), input_type ("raw" or "correlation", what x was), and cor_method_used (the correlation method that actually ran, NA_character_ for a correlation-matrix input, which consumes none). cor_method keeps the requested value whether or not it was used. For the criterion rotations fitted by gradient projection it additionally carries rotation_diagnostics, a list summarising the multi-start run:

  • converged: whether the start whose solution is returned reached the convergence tolerance (reported separately from the counts below because the returned solution is the one with the lowest criterion value, which need not be a converged one).

  • n_starts_total: the random_starts random starts plus the rational start.

  • n_optimized: how many of those starts were actually optimized – fewer than n_starts_total whenever the solver screens the random starts and optimizes only the most promising ones.

  • n_converged: how many optimized starts reached the convergence tolerance.

  • n_distinct_minima: how many distinct local optima those converged starts found; more than one means the criterion is multimodal on these data.

  • criterion_spread: the range of the criterion values they attained.

  • criterion_best: the criterion value of the returned solution.

When normalize = TRUE, criterion_best and criterion_spread are evaluated on the Kaiser-normalized loadings the criterion is optimized on, not on the returned rot_loadings, so they are not directly comparable to a criterion recomputed from the returned loadings.

fiml

Diagnostics of the FIML correlation's EM estimation, present only for cor_method = "fiml": whether it converged before fiml_max_iter, how many iterations it took (iter), the number of distinct missingness patterns (n_patterns), and the number of cases used (n, the reported N). A converged of FALSE means the analysed correlation is the EM's last iterate, not the true FIML estimate; this is flagged in the printed output. Set fiml_max_iter and fiml_tol through estimate_control().

SE

A named list of standard error matrices, returned only when se is not "none" (see Standard errors in Details for what each method assumes and when a component comes back NA). For se = "np-boot": bootstrap SDs of the loadings (rotated too, if a rotation was applied), the residuals, and the fit indices, plus – for oblique rotations – Phi and the structure coefficients; valid_replicates (and, when rotated, valid_target_rotations) records how many bootstrap replicates each of those is based on. For se = "information" and se = "sandwich": Wald-type SEs for the loadings, the uniquenesses, and the communalities – identical to each other, since communality is just 1 - uniqueness – plus Phi and the structure coefficients for oblique rotations. "sandwich" stays valid under non-normality; "information" assumes the model is correctly specified.

CI

A named list of confidence intervals of width ci, matching the components of SE: percentile intervals for se = "np-boot", Wald intervals for se = "information" and se = "sandwich". Only returned if se is not "none".

replicates

A named list of raw bootstrap replicate arrays – the aligned loadings, Phi, structure coefficients, residuals, and fit indices behind SE and CI – with one replicate per array's last dimension (first dimension for fit_indices). Failed replicates are left NA. Populated only for se = "np-boot"; NULL otherwise.

vcov_unrot_loadings

The full unrotated-loading covariance matrix behind SE$unrot_loadings: a p * n_factors by p * n_factors matrix in column-major vec(Lambda) order, with rows and columns labelled "<variable>_<factor>". Populated for se = "information" and se = "sandwich" – always the unrotated block, even when a rotation is applied – and NA-filled if the analytic covariance is unreliable (a Heywood case or a singular information matrix); NULL for se = "np-boot" and se = "none". It can be populated even when SE$unrot_loadings is NA: a weakly determined rotational orientation invalidates only the marginal SEs, not the underlying covariance.

Gamma

The asymptotic covariance of the off-diagonal sample correlations – the meat of the robust sandwich SEs. A p (p - 1) / 2 by p (p - 1) / 2 matrix, rows and columns ordered by utils::combn() over the column pairs and labelled "<var_i>-<var_j>". Populated for se = "sandwich" on the polychoric/tetrachoric and Pearson paths; NULL otherwise (including under cor_method = "fiml", whose meat is not returned). It typically dominates a sandwich fit's size (about 11 MB at p = 50) and is kept because efa_mi() needs it from each per-imputation fit to build the pooled covariance.

Details

Estimators

The estimator is chosen with estimator.

  • PAF (principal axis factoring) iteratively estimates the communalities and makes no distributional assumptions, which makes it robust and a good general-purpose default. Because it minimises no likelihood or weighted discrepancy it provides no model chi-square, and hence no chi-square-based fit indices (see Fit indices).

  • ML (maximum likelihood) maximises the normal-theory likelihood. It yields the full set of fit indices and is the only estimator with analytic expected-information standard errors (se = "information"), but it assumes multivariate normality and is the most prone to Heywood (improper) cases. Its starting values are set by start_method.

  • ULS (unweighted least squares) minimises the sum of squared correlation residuals. "MINRES" (minimum residual) is the same estimator under a different name and returns identical results. It makes no normality assumption, is robust to mild non-normality, and yields the full set of fit indices.

  • DWLS (diagonally weighted least squares) is the recommended estimator for ordinal data. It weights each off-diagonal correlation residual by the inverse asymptotic variance of the corresponding polychoric correlation (Muthén, du Toit, & Spisic, 1997), reproducing the loadings of a diagonally weighted least squares fit. It therefore requires raw ordinal data with cor_method = "poly" or "tetra". Because the weighting follows the polychoric asymptotic covariance, the matrix and the weights are estimated on the listwise-complete cases. Its fit-index behaviour is described under Fit indices.

Correlation methods

When raw data are supplied, cor_method selects how the correlation matrix is computed (it is ignored when a correlation matrix is entered directly).

  • "pearson" (default), "spearman", and "kendall" are passed to stats::cor(). The factor model assumes a Pearson correlation, but a rank correlation is analysed on its own scale, not converted to a Pearson-equivalent value; Kendall's tau in particular gives the most attenuated loadings of the three. For ordinal items prefer "poly" / "tetra" below, which estimate the correlation of the underlying continuous variables.

  • "poly" / "tetra" compute polychoric / tetrachoric correlations for ordinal / binary data, assuming an underlying bivariate-normal latent variable. They use a two-step estimator. The polychoric asymptotic covariance that underlies both the DWLS weights and the scaled (sandwich) statistic relies on large-sample theory that degrades for empty or near-empty response-category combinations; with very sparse cells the resulting weights and standard errors can be unreliable (a warning is issued when empty cells are present), so interpret them with caution and consider collapsing rare categories. Each of the p(p - 1)/2 variable pairs is a separate numerical optimisation, so a polychoric matrix takes much longer to compute than a Pearson one, and the difference grows quadratically in the number of variables; with se = "np-boot" the whole matrix is re-estimated for every bootstrap replicate.

  • "fiml" estimates a two-stage full-information maximum-likelihood correlation. The saturated multivariate-normal mean and covariance are estimated from raw data with missing values by an EM algorithm assuming the data are missing at random (Yuan, Marshall, & Bentler, 2002; Little & Rubin, 2002), and the standardized covariance is then analysed. The model fit indices are corrected two-stage statistics wherever the correction can be formed (see Fit indices). "fiml" uses every case and handles the missingness itself, so use is ignored; it supplies a continuous (Pearson-type) correlation only and is therefore not compatible with estimator = "DWLS". Standard errors are available analytically for estimator = "ML" or "ULS" and, for any estimator, by the non-parametric bootstrap (see Standard errors). For multiply imputed data, efa_mi() is the alternative route to handling missingness. Both routes assume the values are missing at random (MAR), and 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.

Rotations

A rotation transforms the unrotated loadings toward a simpler, more interpretable pattern; all rotations are performed by rotation engines built into the package. Orthogonal rotations keep the factors uncorrelated, whereas oblique rotations let them correlate (returning a pattern matrix, a structure matrix, and the factor intercorrelations Phi) and are usually more realistic for psychological constructs.

For an oblique solution the pattern matrix (rot_loadings) holds each variable's unique contribution from each factor, with the other factors partialled out; it is what is normally interpreted and reported. The structure matrix (Structure = rot_loadings %*% Phi) holds the plain variable-factor correlations, which are inflated by the factor intercorrelations. The two coincide only when Phi is the identity, which is why an orthogonal rotation returns rot_loadings alone.

Orthogonal rotations:

  • varimax maximises the variance of the squared loadings within each factor (column simplicity). It is the most widely used orthogonal rotation and spreads variance across factors rather than concentrating it in a general factor.

  • quartimax simplifies the variables (rows) so that each loads mainly on one factor; it tends to produce a strong general factor.

  • equamax is a Crawford-Ferguson compromise between varimax (column) and quartimax (row) simplicity.

  • geominT uses a geometric-mean criterion that rewards a sparse pattern and tolerates variables with cross-loadings; a smaller offset delta gives a sparser solution but sharper local minima.

  • bentlerT uses Bentler's invariant pattern simplicity criterion.

  • bifactorT is the Jennrich-Bentler orthogonal bifactor criterion: a general factor plus group factors (bifactor simple structure). It needs at least two group factors (n_factors >= 3): with two factors the criterion is identically zero, so no rotation is performed and the unrotated loadings are returned with a warning.

Oblique rotations:

  • promax is a fast two-step rotation: a varimax solution is raised to a power (controlled by k and p_type) to form a target that is then fitted obliquely.

  • quartimin simplifies the variables (rows), like quartimax, so each loads mainly on one factor, but factors are allowed to correlate.

  • oblimin is quartimin with a tunable argument, gam (default 0, i.e. quartimin itself); turning it up trades some of that row simplicity for more strongly correlated factors, and can drive the solution toward factor collapse, so inspect Phi before interpreting a fit with gam > 0.

  • simplimax drives the k smallest loadings toward zero. Its criterion is only piecewise smooth and strongly multimodal, which makes it by far the most start-dependent rotation offered here: different random seeds can reach noticeably different solutions. Because every start is fully optimised (there is no screening stage), raising random_starts to several hundred costs proportionally more time but buys a better optimum.

  • bentlerQ is the oblique Bentler invariant pattern simplicity criterion.

  • geominQ is the oblique geomin criterion; it handles complex (cross-loading) structure well but is multimodal, so it benefits from more random_starts (and uses a more thorough multi-start search internally).

  • bifactorQ is the oblique (correlated) Jennrich-Bentler bifactor criterion, with the same two-group-factor requirement as bifactorT.

Prefer an oblique rotation unless there is a substantive reason to force the factors to be uncorrelated: if Phi comes back near zero the oblique solution is essentially the orthogonal one anyway, whereas imposing orthogonality on genuinely correlated factors distorts the pattern.

The criterion-based rotations (all except varimax and promax) are fitted by gradient projection with random_starts random starts to guard against local minima; the complexity criteria (simplimax and geominQ in particular) are the most multimodal. The starts are drawn from the random-number generator, so different starts can reach genuinely different optima and such a fit is reproducible only when the generator is controlled: pass seed, or call base::set.seed() beforehand.

Standard errors

se selects whether and how standard errors (and matching confidence intervals) are computed. Which quantities they cover depends on the method. The analytic methods ("information" and "sandwich") cover the unrotated loadings, the uniquenesses and the communalities and, when a rotation is applied, the rotated loadings and – for oblique rotations – the factor correlations and the structure coefficients. The bootstrap ("np-boot") covers the unrotated loadings, the residuals, and the fit indices and, when a rotation is applied, the rotated loadings and – for oblique rotations – the factor correlations and the structure coefficients; it reports no uniqueness or communality standard errors.

  • "none" (default) computes no standard errors.

  • "information" returns analytic standard errors from the expected (Fisher) information matrix of the maximum-likelihood solution, and therefore requires estimator = "ML" and cor_method = "pearson" (or "fiml", see below). The rotated standard errors are obtained by propagating the unrotated-loading covariance through the rotation (Jennrich, 1973); because rotated quantities do not depend on how the unrotated solution happens to be oriented, they are directly comparable across programs. Unlike the bootstrap it also works from a correlation matrix as long as N is supplied. These standard errors assume multivariate normality and a correctly specified model; under heavy-tailed data or model misfit they can understate the sampling variability, where "sandwich" or "np-boot" are more robust.

    The rotated loadings, Phi, the structure coefficients, the uniquenesses, and the communalities do not depend on how the unrotated solution happens to be oriented, and so are comparable across programs. The unrotated loading standard errors are not: a program using a different orientation will report different unrotated loading standard errors for the same fit. That orientation can also fail to be well defined – for example when two factors are of near-equal strength – in which case the unrotated loadings have no well-defined sampling distribution and their standard errors diverge. efa_fit() detects this and returns NA for the unrotated loading standard errors with a warning, rather than reporting a number that looks like a standard error but is an artefact of the orientation; the rotated loadings, Phi, the structure coefficients, the uniquenesses, and the communalities are unaffected and still reported. Use those, or se = "np-boot", when the unrotated loadings themselves are the quantity of interest. This detection applies to every analytic route (the Pearson and polychoric paths and the two-stage cor_method = "fiml" sandwich alike). A Heywood case (a uniqueness at its lower boundary) is separate and more severe: there the whole SE/CI block comes back NA with a warning, because the standard-error approximation fails for every parameter. The scaled chi-square (from "sandwich", or from cor_method = "fiml") does not rely on that approximation and is still reported, so the fit indices are unaffected.

  • "sandwich" returns robust ("sandwich") standard errors estimated from raw data, combining the estimator weight with a distribution-free covariance of the correlations, so it stays valid under non-normality and weight misspecification (Browne, 1984; Satorra & Bentler, 1994). It is available either for ordinal data with cor_method = "poly" or "tetra" and estimator one of "ML", "ULS", or "DWLS", or for continuous data with cor_method = "pearson" and estimator = "ML" or "ULS". It reports the same coverage as "information" and additionally fills the model fit's chi-square block with a scaled chi-square (see Fit indices); the statistic reported as chi is always the scaled-and-shifted one (flagged by chi_scaled_type), and a mean-adjusted alternative is returned alongside it as chi_mean_adjusted. Because the covariance must describe the same cases as the correlation matrix, the sandwich (like estimator = "DWLS") is computed on the listwise-complete cases; on data with missing values the reported N, the correlation matrix, and the point estimate therefore reflect the complete cases regardless of use.

  • "np-boot" draws a non-parametric (case-resampling) bootstrap and needs raw data. A correlation matrix carries no cases to resample; alone among the unsupported combinations this one does not error but warns and downgrades se to "none", so the fit is returned without an SE slot. It is the most general method – available for any estimator, rotation, and cor_method – and the most robust to non-normality and misfit, at the cost of speed; its intervals are bootstrap percentile intervals. The replicate fits run across replicates with the future framework; by default they run sequentially, but registering a plan with future::plan() runs them in parallel instead, as the examples show. With a fixed seed the bootstrap is reproducible and yields the same result regardless of the number of workers. Under cor_method = "fiml" each resample also re-runs the EM moment estimation and is therefore slow, so a smaller b_boot may be advisable.

    The percentile intervals are centred on the point estimate for the loadings, the factor correlations, the structure coefficients, and the residuals, but not for the indices derived from the chi-square (RMSEA, AIC, BIC, and ECVI): a resample already carries the model's own misfit, so those intervals ride upward and can even place the point estimate below their own lower bound. Read them as a spread rather than a range for the point estimate; correcting that shift needs resampling from a population transformed to fit the model (Bollen & Stine, 1992), which is not what this bootstrap does. CFI and TLI are not affected, being ratios in which the baseline chi-square shifts along with the model one.

The analytic methods ("information" and "sandwich") are not available with the "promax" or "simplimax" rotations, which have no supported analytic route for the rotated standard errors; use "np-boot" there. Under cor_method = "fiml", "information" and "sandwich" instead return, for estimator = "ML" or "ULS", corrected two-stage sandwich standard errors (Yuan & Bentler, 2000; Savalei & Bentler, 2009). estimator = "PAF" carries no Stage-2 weight to build the sandwich from, so use se = "np-boot" there instead.

Fit indices

For ML and ULS, efa_fit() returns the model chi-square (with its p-value and degrees of freedom), the Comparative Fit Index (CFI; Bentler, 1990), the Tucker-Lewis Index (TLI, also called the non-normed fit index; Tucker & Lewis, 1973), the Root Mean Square Error of Approximation (RMSEA) with its 90% confidence interval (Browne & Cudeck, 1992), the Akaike and Bayesian Information Criteria (AIC, BIC), the Expected Cross-Validation Index (ECVI; Browne & Cudeck, 1989), the Root Mean Squared Residual (RMSR), the Standardized Root Mean Squared Residual (SRMR; Bentler, 1995), and the common-part-accounted-for (CAF) index (Lorenzo-Seva, Timmerman, & Kiers, 2011). They come with the independence-baseline statistics chi_null, df_null, and p_null. On the unscaled ML and ULS paths chi_null is Bartlett's test of sphericity; a scaled chi-square, and the two-stage statistic of a cor_method = "fiml" fit, each carry their own baseline instead.

The degrees of freedom depend on the number of variables and factors; the baseline degrees of freedom df_null are \(p(p - 1)/2\) for \(p\) variables.

RMSR is the root mean square of the off-diagonal residuals; SRMR rescales it by a fixed factor that depends only on the number of variables. The print and summary methods show SRMR, not RMSR; RMSR remains in the returned object for backward compatibility. Both are computed over the same residuals, so an unavailable residual leaves both NA. (psych's rms uses a different divisor and equals RMSR\(/\sqrt{2}\); the two are not directly comparable.)

The model chi-square is the Bartlett-corrected discrepancy (matching stats::factanal() for ML). For ULS it is the same maximum-likelihood discrepancy, evaluated at the ULS-fitted solution, as in psych::fa() – not the least-squares criterion lavaan reports as its standard ULS test statistic, so the two are not comparable. AIC and BIC are built on this chi-square and can therefore be negative; ECVI (built on the same chi-square plus a non-negative penalty) cannot. Because of the Bartlett correction, ECVI differs slightly from the uncorrected Browne-Cudeck form reported by lavaan and Mplus (their AIC/BIC use an unrelated log-likelihood-based formula, so they are not comparable). On the unscaled ML/ULS path, CFI and TLI are computed on a slightly different discrepancy scale than the reported chi-square, so you cannot recompute one from the other by hand there; on the scaled (sandwich) and FIML paths, the reported chi and chi_null are exactly the pair the indices use.

Which indices are reported depends on the estimator:

  • ML and ULS compute the full set above.

  • PAF returns only the descriptive residual indices (RMSR, SRMR, CAF) and df; the printed model-fit block shows CAF and SRMR. The chi-square-based indices are NA, because PAF minimises no discrepancy.

  • DWLS by default returns only RMSR, SRMR, CAF, and df, because the ordinary maximum-likelihood discrepancy is not its fit function. When se = "sandwich", a scaled chi-square and the CFI, TLI, and RMSEA derived from it are reported (AIC and BIC remain NA); that statistic is a two-stage correction applied to the polychoric correlation residuals (Browne, 1984), not identical to the full WLSMV test of lavaan or Mplus, which also corrects for the response thresholds.

  • cor_method = "fiml" (with ML or ULS) reports two-stage-corrected statistics (Yuan, Marshall, & Bentler, 2002); AIC, BIC, and ECVI are left NA, as for any scaled chi-square. The correction itself can be degenerate – typically with a small sample, a high proportion of missing values, or near-collinear variables – in which case an uncorrected likelihood-ratio statistic is reported in its place with a warning, and the print methods label that line uncorrected rather than scaled; read its p-value and the CFI, TLI, and RMSEA derived from it as indicative only.

Beyond the estimator, the chi-square and everything derived from it are NA whenever the statistic itself is undefined: when N is not supplied, when the model is underidentified (a negative df), and when a positive N is too small relative to the number of variables and factors for the small-sample correction to remain valid. Each case raises its own warning. The residual summaries (RMSR, SRMR, CAF) and the degrees of freedom are still returned there, but residual size does not establish that a model is identified: below zero degrees of freedom a near-zero residual is an artefact of over-parameterisation, not close fit.

Whenever the chi-square is a scaled one (se = "sandwich", or a cor_method = "fiml" fit whose correction could be formed), AIC, BIC, and ECVI are NA; see the fit_indices entry in Value for the additional components then returned. AIC, BIC, and ECVI are NA on every cor_method = "fiml" fit, including the uncorrected fallback above. Lorenzo-Seva, Timmerman, and Kiers (2011) describe CAF as ranging from 0 to 1, with values near 1 indicating close fit; that does not hold here, where a well-fitting model produces a CAF near 0.5, not near 1. Read it as a relative rather than an absolute measure.

Available combinations

Not every estimator, rotation, standard-error, and correlation method can be combined:

  • Estimator and correlation method. estimator = "DWLS" requires ordinal data with cor_method = "poly" or "tetra". cor_method = "fiml" works with PAF, ML, and ULS (not DWLS) and needs raw data with missing values.

  • Standard errors. se = "information" requires estimator = "ML" and cor_method = "pearson" or "fiml", and can be computed from a correlation matrix when N is supplied. se = "sandwich" requires raw data, with either a polychoric/tetrachoric cor_method (ML, ULS, or DWLS) or a Pearson cor_method (ML or ULS); it is not available for PAF. Under cor_method = "fiml", "information" and "sandwich" are available for ML and ULS only and both return the corrected two-stage sandwich. se = "np-boot" requires raw data and works with any estimator, rotation, and correlation method. Neither "information" nor "sandwich" is available with the "promax" or "simplimax" rotations.

  • Fit indices. The chi-square-based indices are available for ML and ULS (and, as scaled statistics, for cor_method = "fiml" and for DWLS with se = "sandwich"); PAF and DWLS otherwise report only the descriptive residual indices.

See also

estimate_control() and rotate_control() for the estimation and rotation tuning knobs. efa_retain() for choosing n_factors, and efa_scores(), efa_reliability(), efa_schmid_leiman(), and efa_compare() for working with the fitted solution.

Other factor analysis: efa_average(), efa_group(), efa_mi(), plot.efa_group(), print.efa_group()

Examples


# Principal axis factoring with oblimin rotation
mod_oblimin <- efa_fit(test_models$baseline$cormat, n_factors = 3, N = 500,
                       rotation = "oblimin")
mod_oblimin
#> 
#> EFA performed with estimator = 'PAF' and rotation = 'oblimin'.
#> 
#> ── Rotated Loadings ────────────────────────────────────────────────────────────
#> 
#>        F1     F2     F3    h2    u2
#> V1   -.035   .049   .597  .367  .633
#> V2    .010   .077   .470  .277  .723
#> V3    .069   .066   .442  .283  .717
#> V4    .110   .007   .536  .378  .622
#> V5    .164  -.005   .427  .293  .707
#> V6   -.058  -.032   .684  .399  .601
#> V7    .012   .525   .099  .357  .643
#> V8   -.005   .570   .039  .349  .651
#> V9    .047   .540   .008  .330  .670
#> V10  -.011   .659  -.058  .383  .617
#> V11   .025   .355   .232  .297  .703
#> V12   .031   .638   .001  .432  .568
#> V13   .606   .092  -.059  .394  .606
#> V14   .541  -.056   .088  .322  .678
#> V15   .554   .133  -.062  .363  .637
#> V16   .548  -.039   .091  .344  .656
#> V17   .654  -.027  -.023  .390  .610
#> V18   .549   .014   .052  .350  .650
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── Factor Intercorrelations ────────────────────────────────────────────────────
#> 
#>       F1     F2     F3
#> F1  1.000
#> F2   .591  1.000
#> F3   .623   .598  1.000
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                      F1     F2     F3
#> SS loadings        2.218  2.083  2.005
#> Prop Tot Var        .123   .116   .111
#> Cum Prop Tot Var    .123   .239   .350
#> Prop Comm Var       .352   .330   .318
#> Cum Prop Comm Var   .352   .682  1.000
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> CAF: .50
#> SRMR: .02
#> df: 102
summary(mod_oblimin)
#> 
#> EFA performed with estimator = 'PAF' and rotation = 'oblimin'.
#> 
#> ── Model Diagnostics ───────────────────────────────────────────────────────────
#> 
#> Factors: 3
#> Variables: 18
#> N: 500
#> Rotation local optima: 1 distinct from 6 of 101 starts
#> Heywood cases: 0
#> Cross-loading items (|loading| >= .300): 0
#> Items without salient loading (|loading| >= .300): 0
#> Factors with fewer than 3 salient indicators: 0
#> Items with primary-loading gap < .200: 1
#> Largest |residual|: .069
#> Factor intercorrelations > .85: none
#> 
#> ── Rotated Loadings ────────────────────────────────────────────────────────────
#> 
#>        F1     F2     F3    h2    u2
#> V1   -.035   .049   .597  .367  .633
#> V2    .010   .077   .470  .277  .723
#> V3    .069   .066   .442  .283  .717
#> V4    .110   .007   .536  .378  .622
#> V5    .164  -.005   .427  .293  .707
#> V6   -.058  -.032   .684  .399  .601
#> V7    .012   .525   .099  .357  .643
#> V8   -.005   .570   .039  .349  .651
#> V9    .047   .540   .008  .330  .670
#> V10  -.011   .659  -.058  .383  .617
#> V11   .025   .355   .232  .297  .703
#> V12   .031   .638   .001  .432  .568
#> V13   .606   .092  -.059  .394  .606
#> V14   .541  -.056   .088  .322  .678
#> V15   .554   .133  -.062  .363  .637
#> V16   .548  -.039   .091  .344  .656
#> V17   .654  -.027  -.023  .390  .610
#> V18   .549   .014   .052  .350  .650
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── Factor Intercorrelations ────────────────────────────────────────────────────
#> 
#>       F1     F2     F3
#> F1  1.000
#> F2   .591  1.000
#> F3   .623   .598  1.000
#> 
#> ── Structure Matrix ────────────────────────────────────────────────────────────
#> 
#>       F1    F2    F3
#> V1   .366  .385  .604
#> V2   .348  .364  .522
#> V3   .384  .371  .525
#> V4   .448  .392  .609
#> V5   .427  .347  .526
#> V6   .350  .343  .629
#> V7   .384  .591  .420
#> V8   .356  .590  .376
#> V9   .371  .573  .361
#> V10  .341  .617  .328
#> V11  .379  .508  .460
#> V12  .408  .657  .401
#> V13  .624  .415  .374
#> V14  .563  .317  .392
#> V15  .594  .423  .363
#> V16  .582  .340  .410
#> V17  .624  .346  .369
#> V18  .589  .369  .402
#> 
#> ── Simple Structure Diagnostics ────────────────────────────────────────────────
#> 
#> Items with primary-loading gap < .200:
#> • V11: F2 = .355, F3 = .232
#> 
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                      F1     F2     F3
#> SS loadings        2.218  2.083  2.005
#> Prop Tot Var        .123   .116   .111
#> Cum Prop Tot Var    .123   .239   .350
#> Prop Comm Var       .352   .330   .318
#> Cum Prop Comm Var   .352   .682  1.000
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> CAF: .50
#> SRMR: .02
#> df: 102
#> 
#> ── Residual Diagnostics ────────────────────────────────────────────────────────
#> 
#> Residual cutoff: |r| > .100
#> Number of large residuals: 0
#> Largest absolute residual: .069
#> 
#> No absolute residuals > .100 occurred.
#> 
#> Inspect the residual matrix for details (e.g., with residuals()).

# ML estimation with oblimin rotation
mod_oblimin <- efa_fit(test_models$baseline$cormat, n_factors = 3, N = 500,
                       estimator = "ML", rotation = "oblimin")
mod_oblimin
#> 
#> EFA performed with estimator = 'ML' and rotation = 'oblimin'.
#> 
#> ── Rotated Loadings ────────────────────────────────────────────────────────────
#> 
#>        F1     F2     F3    h2    u2
#> V1   -.036   .043   .607  .373  .627
#> V2    .013   .087   .458  .274  .726
#> V3    .074   .074   .430  .280  .720
#> V4    .111   .007   .536  .379  .621
#> V5    .164   .005   .418  .290  .710
#> V6   -.055  -.036   .687  .402  .598
#> V7    .017   .524   .095  .355  .645
#> V8   -.003   .562   .044  .345  .655
#> V9    .044   .535   .017  .328  .672
#> V10  -.019   .661  -.051  .385  .615
#> V11   .030   .352   .230  .296  .704
#> V12   .034   .649  -.015  .437  .563
#> V13   .612   .095  -.068  .397  .603
#> V14   .540  -.053   .086  .320  .680
#> V15   .552   .137  -.065  .363  .637
#> V16   .550  -.039   .092  .345  .655
#> V17   .652  -.035  -.013  .390  .610
#> V18   .549   .012   .052  .349  .651
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── Factor Intercorrelations ────────────────────────────────────────────────────
#> 
#>       F1     F2     F3
#> F1  1.000
#> F2   .591  1.000
#> F3   .621   .596  1.000
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                      F1     F2     F3
#> SS loadings        2.225  2.088  1.994
#> Prop Tot Var        .124   .116   .111
#> Cum Prop Tot Var    .124   .240   .350
#> Prop Comm Var       .353   .331   .316
#> Cum Prop Comm Var   .353   .684  1.000
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> χ²(102) = 123.75, p = .070
#> CFI: .99
#> TLI: .98
#> RMSEA [90% CI]: .02 [.00; .03]
#> AIC: -80.25
#> BIC: -510.14
#> ECVI: 0.52
#> CAF: .50
#> SRMR: .03
summary(mod_oblimin)
#> 
#> EFA performed with estimator = 'ML' and rotation = 'oblimin'.
#> 
#> ── Model Diagnostics ───────────────────────────────────────────────────────────
#> 
#> Factors: 3
#> Variables: 18
#> N: 500
#> Rotation local optima: 1 distinct from 6 of 101 starts
#> Heywood cases: 0
#> Cross-loading items (|loading| >= .300): 0
#> Items without salient loading (|loading| >= .300): 0
#> Factors with fewer than 3 salient indicators: 0
#> Items with primary-loading gap < .200: 1
#> Largest |residual|: .069
#> Factor intercorrelations > .85: none
#> 
#> ── Rotated Loadings ────────────────────────────────────────────────────────────
#> 
#>        F1     F2     F3    h2    u2
#> V1   -.036   .043   .607  .373  .627
#> V2    .013   .087   .458  .274  .726
#> V3    .074   .074   .430  .280  .720
#> V4    .111   .007   .536  .379  .621
#> V5    .164   .005   .418  .290  .710
#> V6   -.055  -.036   .687  .402  .598
#> V7    .017   .524   .095  .355  .645
#> V8   -.003   .562   .044  .345  .655
#> V9    .044   .535   .017  .328  .672
#> V10  -.019   .661  -.051  .385  .615
#> V11   .030   .352   .230  .296  .704
#> V12   .034   .649  -.015  .437  .563
#> V13   .612   .095  -.068  .397  .603
#> V14   .540  -.053   .086  .320  .680
#> V15   .552   .137  -.065  .363  .637
#> V16   .550  -.039   .092  .345  .655
#> V17   .652  -.035  -.013  .390  .610
#> V18   .549   .012   .052  .349  .651
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── Factor Intercorrelations ────────────────────────────────────────────────────
#> 
#>       F1     F2     F3
#> F1  1.000
#> F2   .591  1.000
#> F3   .621   .596  1.000
#> 
#> ── Structure Matrix ────────────────────────────────────────────────────────────
#> 
#>       F1    F2    F3
#> V1   .366  .384  .610
#> V2   .349  .367  .518
#> V3   .385  .374  .520
#> V4   .448  .392  .609
#> V5   .427  .351  .523
#> V6   .350  .341  .631
#> V7   .385  .590  .418
#> V8   .357  .587  .377
#> V9   .371  .571  .363
#> V10  .339  .619  .331
#> V11  .381  .507  .459
#> V12  .409  .661  .393
#> V13  .626  .416  .369
#> V14  .562  .317  .390
#> V15  .593  .425  .360
#> V16  .584  .341  .410
#> V17  .623  .343  .371
#> V18  .589  .368  .401
#> 
#> ── Simple Structure Diagnostics ────────────────────────────────────────────────
#> 
#> Items with primary-loading gap < .200:
#> • V11: F2 = .352, F3 = .230
#> 
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                      F1     F2     F3
#> SS loadings        2.225  2.088  1.994
#> Prop Tot Var        .124   .116   .111
#> Cum Prop Tot Var    .124   .240   .350
#> Prop Comm Var       .353   .331   .316
#> Cum Prop Comm Var   .353   .684  1.000
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> χ²(102) = 123.75, p = .070
#> CFI: .99
#> TLI: .98
#> RMSEA [90% CI]: .02 [.00; .03]
#> AIC: -80.25
#> BIC: -510.14
#> ECVI: 0.52
#> CAF: .50
#> SRMR: .03
#> 
#> ── Residual Diagnostics ────────────────────────────────────────────────────────
#> 
#> Residual cutoff: |r| > .100
#> Number of large residuals: 0
#> Largest absolute residual: .069
#> 
#> No absolute residuals > .100 occurred.
#> 
#> Inspect the residual matrix for details (e.g., with residuals()).

# Tuning knobs are supplied through the control objects. Here the SPSS preset is
# used for the estimation and rotation, with the maximum PAF iterations raised.
mod_spss <- efa_fit(test_models$baseline$cormat, n_factors = 3, N = 500,
                    rotation = "promax",
                    estimate_control = estimate_control(type = "SPSS", max_iter = 500),
                    rotate_control = rotate_control(type = "SPSS"))
#> Warning: An argument was set together with `type` = "SPSS"; the supplied value is used
#> and may differ from the "SPSS" preset:
#> • max_iter = 500
mod_spss
#> 
#> EFA performed with estimator = 'PAF' and rotation = 'promax'.
#> 
#> ── Rotated Loadings ────────────────────────────────────────────────────────────
#> 
#>        F1     F2     F3    h2    u2
#> V1   -.048   .035   .613  .367  .633
#> V2   -.001   .067   .482  .277  .723
#> V3    .060   .056   .453  .283  .717
#> V4    .101  -.009   .551  .378  .622
#> V5    .157  -.018   .438  .293  .707
#> V6   -.072  -.049   .704  .399  .601
#> V7    .001   .533   .093  .357  .643
#> V8   -.016   .581   .030  .349  .651
#> V9    .038   .550  -.001  .330  .670
#> V10  -.021   .674  -.071  .383  .617
#> V11   .015   .356   .232  .297  .703
#> V12   .020   .651  -.010  .432  .568
#> V13   .614   .086  -.067  .394  .606
#> V14   .548  -.068   .088  .322  .678
#> V15   .561   .128  -.070  .363  .637
#> V16   .555  -.050   .091  .344  .656
#> V17   .664  -.037  -.027  .390  .610
#> V18   .555   .004   .050  .350  .650
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── Factor Intercorrelations ────────────────────────────────────────────────────
#> 
#>       F1     F2     F3
#> F1  1.000
#> F2   .617  1.000
#> F3   .648   .632  1.000
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                      F1     F2     F3
#> SS loadings        2.199  2.074  2.034
#> Prop Tot Var        .122   .115   .113
#> Cum Prop Tot Var    .122   .237   .350
#> Prop Comm Var       .349   .329   .323
#> Cum Prop Comm Var   .349   .677  1.000
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> CAF: .50
#> SRMR: .02
#> df: 102

# Analytic (expected-information) standard errors for the above
ML_info <- efa_fit(test_models$baseline$cormat, n_factors = 3, N = 500,
                   estimator = "ML", rotation = "oblimin", se = "information")
ML_info
#> 
#> EFA performed with estimator = 'ML' and rotation = 'oblimin'.
#> 
#> ── Rotated Loadings ────────────────────────────────────────────────────────────
#> 
#>        F1     F2     F3    h2    u2
#> V1   -.036   .043   .607  .373  .627
#> V2    .013   .087   .458  .274  .726
#> V3    .074   .074   .430  .280  .720
#> V4    .111   .007   .536  .379  .621
#> V5    .164   .005   .418  .290  .710
#> V6   -.055  -.036   .687  .402  .598
#> V7    .017   .524   .095  .355  .645
#> V8   -.003   .562   .044  .345  .655
#> V9    .044   .535   .017  .328  .672
#> V10  -.019   .661  -.051  .385  .615
#> V11   .030   .352   .230  .296  .704
#> V12   .034   .649  -.015  .437  .563
#> V13   .612   .095  -.068  .397  .603
#> V14   .540  -.053   .086  .320  .680
#> V15   .552   .137  -.065  .363  .637
#> V16   .550  -.039   .092  .345  .655
#> V17   .652  -.035  -.013  .390  .610
#> V18   .549   .012   .052  .349  .651
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── Factor Intercorrelations ────────────────────────────────────────────────────
#> 
#>       F1     F2     F3
#> F1  1.000
#> F2   .591  1.000
#> F3   .621   .596  1.000
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                      F1     F2     F3
#> SS loadings        2.225  2.088  1.994
#> Prop Tot Var        .124   .116   .111
#> Cum Prop Tot Var    .124   .240   .350
#> Prop Comm Var       .353   .331   .316
#> Cum Prop Comm Var   .353   .684  1.000
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> χ²(102) = 123.75, p = .070
#> CFI: .99
#> TLI: .98
#> RMSEA [90% CI]: .02 [.00; .03]
#> AIC: -80.25
#> BIC: -510.14
#> ECVI: 0.52
#> CAF: .50
#> SRMR: .03
summary(ML_info)
#> 
#> EFA performed with estimator = 'ML' and rotation = 'oblimin'.
#> 
#> ── Model Diagnostics ───────────────────────────────────────────────────────────
#> 
#> Factors: 3
#> Variables: 18
#> N: 500
#> Rotation local optima: 1 distinct from 6 of 101 starts
#> Heywood cases: 0
#> Cross-loading items (|loading| >= .300): 0
#> Items without salient loading (|loading| >= .300): 0
#> Factors with fewer than 3 salient indicators: 0
#> Items with primary-loading gap < .200: 1
#> Largest |residual|: .069
#> Factor intercorrelations > .85: none
#> 
#> ── Rotated Loadings ────────────────────────────────────────────────────────────
#> 
#>        F1     F2     F3    h2    u2
#> V1   -.036   .043   .607  .373  .627
#> V2    .013   .087   .458  .274  .726
#> V3    .074   .074   .430  .280  .720
#> V4    .111   .007   .536  .379  .621
#> V5    .164   .005   .418  .290  .710
#> V6   -.055  -.036   .687  .402  .598
#> V7    .017   .524   .095  .355  .645
#> V8   -.003   .562   .044  .345  .655
#> V9    .044   .535   .017  .328  .672
#> V10  -.019   .661  -.051  .385  .615
#> V11   .030   .352   .230  .296  .704
#> V12   .034   .649  -.015  .437  .563
#> V13   .612   .095  -.068  .397  .603
#> V14   .540  -.053   .086  .320  .680
#> V15   .552   .137  -.065  .363  .637
#> V16   .550  -.039   .092  .345  .655
#> V17   .652  -.035  -.013  .390  .610
#> V18   .549   .012   .052  .349  .651
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── 95% Wald CIs for salient rotated loadings ───────────────────────────────────
#> 
#> Variable  Factor  est    lower  upper
#> V13       F1       .612   .488   .736
#> V14       F1       .540   .411   .669
#> V15       F1       .552   .423   .681
#> V16       F1       .550   .421   .679
#> V17       F1       .652   .535   .769
#> V18       F1       .549   .420   .679
#> V7        F2       .524   .394   .653
#> V8        F2       .562   .437   .687
#> V9        F2       .535   .407   .662
#> V10       F2       .661   .548   .773
#> V11       F2       .352   .211   .493
#> V12       F2       .649   .529   .770
#> V1        F3       .607   .474   .739
#> V2        F3       .458   .313   .604
#> V3        F3       .430   .282   .578
#> V4        F3       .536   .395   .677
#> V5        F3       .418   .269   .567
#> V6        F3       .687   .570   .805
#> 
#> ── Factor Intercorrelations ────────────────────────────────────────────────────
#> 
#>       F1     F2     F3
#> F1  1.000
#> F2   .591  1.000
#> F3   .621   .596  1.000
#> 
#> ── 95% Wald CIs for factor intercorrelations ───────────────────────────────────
#> 
#> Factors   est    lower  upper
#> F1 ~~ F2   .591   .499   .683
#> F1 ~~ F3   .621   .531   .712
#> F2 ~~ F3   .596   .503   .690
#> 
#> ── Structure Matrix ────────────────────────────────────────────────────────────
#> 
#>       F1    F2    F3
#> V1   .366  .384  .610
#> V2   .349  .367  .518
#> V3   .385  .374  .520
#> V4   .448  .392  .609
#> V5   .427  .351  .523
#> V6   .350  .341  .631
#> V7   .385  .590  .418
#> V8   .357  .587  .377
#> V9   .371  .571  .363
#> V10  .339  .619  .331
#> V11  .381  .507  .459
#> V12  .409  .661  .393
#> V13  .626  .416  .369
#> V14  .562  .317  .390
#> V15  .593  .425  .360
#> V16  .584  .341  .410
#> V17  .623  .343  .371
#> V18  .589  .368  .401
#> 
#> ── Simple Structure Diagnostics ────────────────────────────────────────────────
#> 
#> Items with primary-loading gap < .200:
#> • V11: F2 = .352, F3 = .230
#> 
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                      F1     F2     F3
#> SS loadings        2.225  2.088  1.994
#> Prop Tot Var        .124   .116   .111
#> Cum Prop Tot Var    .124   .240   .350
#> Prop Comm Var       .353   .331   .316
#> Cum Prop Comm Var   .353   .684  1.000
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> χ²(102) = 123.75, p = .070
#> CFI: .99
#> TLI: .98
#> RMSEA [90% CI]: .02 [.00; .03]
#> AIC: -80.25
#> BIC: -510.14
#> ECVI: 0.52
#> CAF: .50
#> SRMR: .03
#> 
#> ── Residual Diagnostics ────────────────────────────────────────────────────────
#> 
#> Residual cutoff: |r| > .100
#> Number of large residuals: 0
#> Largest absolute residual: .069
#> 
#> No absolute residuals > .100 occurred.
#> 
#> Inspect the residual matrix for details (e.g., with residuals()).

# \donttest{
# Robust (sandwich) standard errors and a scaled chi-square for ordinal raw data.
# These need a polychoric/tetrachoric correlation method and estimator ML, ULS, or DWLS.
DWLS_rob <- efa_fit(DOSPERT_raw, n_factors = 6, cor_method = "poly",
                    estimator = "DWLS", rotation = "oblimin", se = "sandwich")
#> ℹ `x` is not a correlation matrix; computing correlations from the raw data.
#> Warning: 18 variable pairs have an empty response-category combination despite a
#> non-negligible expected count.
#> ✖ Affected pairs: "ethR_3-recR_3", "ethR_5-heaR_2", "finR_3-finR_6",
#>   "finR_3-heaR_1", "finR_3-socR_3", and 13 more.
#> ℹ The polychoric asymptotic covariance (and any DWLS weights or robust standard
#>   errors derived from it) can be unreliable for such structurally sparse cells;
#>   interpret them with caution and consider collapsing rare response categories
#>   in these variables.
#> Warning: Analytic standard errors could not be computed for all parameters.
#> ℹ The factor solution's rotational orientation is only weakly determined (two
#>   canonical variances nearly coincide), so the unrotated loadings have no
#>   well-defined standard error. The rotated loadings and communalities are
#>   gauge-invariant and unaffected.
DWLS_rob
#> 
#> EFA performed with estimator = 'DWLS' and rotation = 'oblimin'.
#> 
#> ── Rotated Loadings ────────────────────────────────────────────────────────────
#> 
#>           F1     F2     F3     F4     F5     F6    h2    u2
#> ethR_1   .022   .508   .005   .076   .071   .121  .388  .612
#> ethR_2   .019   .565   .082   .131   .046   .014  .435  .565
#> ethR_3   .074   .739  -.227   .017   .026   .064  .697  .303
#> ethR_4  -.072   .617  -.052  -.062   .034   .041  .371  .629
#> ethR_5   .143   .544  -.118   .024   .039  -.006  .408  .592
#> ethR_6   .000   .685   .039   .037  -.016  -.066  .467  .533
#> finR_1   .043   .053  -.021   .804   .051   .134  .828  .172
#> finR_2   .011  -.025   .045  -.011  -.099   .699  .486  .514
#> finR_3   .055   .065   .019   .820   .033   .115  .840  .160
#> finR_4  -.017   .060  -.074   .122   .012   .779  .681  .319
#> finR_5   .038   .078   .015   .834   .013   .131  .866  .134
#> finR_6  -.004   .006   .055   .132   .054   .741  .663  .337
#> heaR_1   .091   .373   .118   .127   .179  -.034  .344  .656
#> heaR_2   .027   .337   .165   .087   .267  -.060  .336  .664
#> heaR_3  -.040   .082  -.076   .072   .676  -.001  .530  .470
#> heaR_4   .021   .053  -.119   .136   .705  -.026  .626  .374
#> heaR_5  -.030   .141   .075  -.040   .463  -.004  .281  .719
#> heaR_6   .122   .234   .122   .038   .397   .038  .427  .573
#> recR_1   .364  -.152   .198  -.094   .308   .069  .368  .632
#> recR_2   .489  -.054  -.178   .102   .354   .118  .596  .404
#> recR_3   .549  -.098  -.041   .019   .328   .113  .577  .423
#> recR_4   .997   .088   .016   .020  -.180  -.036  .904  .096
#> recR_5   .914   .133  -.030   .056  -.105  -.043  .827  .173
#> recR_6   .621  -.007   .052   .051   .074   .120  .534  .466
#> socR_1  -.067  -.058   .693  -.073  -.003  -.020  .507  .493
#> socR_2   .002   .023   .805   .173   .000  -.040  .623  .377
#> socR_3  -.042  -.110   .648  -.070  -.022   .046  .475  .525
#> socR_4   .040  -.078   .729   .159  -.009  -.068  .515  .485
#> socR_5   .121   .115   .436  -.185  -.001   .121  .299  .701
#> socR_6  -.006   .051   .592  -.147   .010   .145  .434  .566
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── Factor Intercorrelations ────────────────────────────────────────────────────
#> 
#>       F1     F2     F3     F4     F5     F6
#> F1  1.000
#> F2   .231  1.000
#> F3   .134  -.074  1.000
#> F4   .236   .397  -.147  1.000
#> F5   .404   .469   .112   .312  1.000
#> F6   .363   .151   .195   .290   .181  1.000
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                      F1     F2     F3     F4     F5     F6
#> SS loadings        3.280  3.153  2.887  2.708  2.250  2.053
#> Prop Tot Var        .109   .105   .096   .090   .075   .068
#> Cum Prop Tot Var    .109   .214   .311   .401   .476   .544
#> Prop Comm Var       .201   .193   .177   .166   .138   .126
#> Cum Prop Comm Var   .201   .394   .571   .737   .874  1.000
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> scaled χ²(270) = 3521.43, p < .001
#> CFI: .96
#> TLI: .93
#> RMSEA [90% CI]: .06 [.06; .06]
#> AIC: NA
#> BIC: NA
#> CAF: .41
#> SRMR: .03
summary(DWLS_rob)
#> 
#> EFA performed with estimator = 'DWLS' and rotation = 'oblimin'.
#> 
#> ── Model Diagnostics ───────────────────────────────────────────────────────────
#> 
#> Factors: 6
#> Variables: 30
#> N: 3123
#> Rotation local optima: 1 distinct from 6 of 101 starts
#> Heywood cases: 0
#> Cross-loading items (|loading| >= .300): 3
#> Items without salient loading (|loading| >= .300): 0
#> Factors with fewer than 3 salient indicators: 0
#> Items with primary-loading gap < .200: 5
#> Largest |residual|: .153
#> Factor intercorrelations > .85: none
#> 
#> ── Rotated Loadings ────────────────────────────────────────────────────────────
#> 
#>           F1     F2     F3     F4     F5     F6    h2    u2
#> ethR_1   .022   .508   .005   .076   .071   .121  .388  .612
#> ethR_2   .019   .565   .082   .131   .046   .014  .435  .565
#> ethR_3   .074   .739  -.227   .017   .026   .064  .697  .303
#> ethR_4  -.072   .617  -.052  -.062   .034   .041  .371  .629
#> ethR_5   .143   .544  -.118   .024   .039  -.006  .408  .592
#> ethR_6   .000   .685   .039   .037  -.016  -.066  .467  .533
#> finR_1   .043   .053  -.021   .804   .051   .134  .828  .172
#> finR_2   .011  -.025   .045  -.011  -.099   .699  .486  .514
#> finR_3   .055   .065   .019   .820   .033   .115  .840  .160
#> finR_4  -.017   .060  -.074   .122   .012   .779  .681  .319
#> finR_5   .038   .078   .015   .834   .013   .131  .866  .134
#> finR_6  -.004   .006   .055   .132   .054   .741  .663  .337
#> heaR_1   .091   .373   .118   .127   .179  -.034  .344  .656
#> heaR_2   .027   .337   .165   .087   .267  -.060  .336  .664
#> heaR_3  -.040   .082  -.076   .072   .676  -.001  .530  .470
#> heaR_4   .021   .053  -.119   .136   .705  -.026  .626  .374
#> heaR_5  -.030   .141   .075  -.040   .463  -.004  .281  .719
#> heaR_6   .122   .234   .122   .038   .397   .038  .427  .573
#> recR_1   .364  -.152   .198  -.094   .308   .069  .368  .632
#> recR_2   .489  -.054  -.178   .102   .354   .118  .596  .404
#> recR_3   .549  -.098  -.041   .019   .328   .113  .577  .423
#> recR_4   .997   .088   .016   .020  -.180  -.036  .904  .096
#> recR_5   .914   .133  -.030   .056  -.105  -.043  .827  .173
#> recR_6   .621  -.007   .052   .051   .074   .120  .534  .466
#> socR_1  -.067  -.058   .693  -.073  -.003  -.020  .507  .493
#> socR_2   .002   .023   .805   .173   .000  -.040  .623  .377
#> socR_3  -.042  -.110   .648  -.070  -.022   .046  .475  .525
#> socR_4   .040  -.078   .729   .159  -.009  -.068  .515  .485
#> socR_5   .121   .115   .436  -.185  -.001   .121  .299  .701
#> socR_6  -.006   .051   .592  -.147   .010   .145  .434  .566
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── 95% Wald CIs for salient rotated loadings ───────────────────────────────────
#> 
#> Variable  Factor  est    lower  upper
#> recR_1    F1       .364   .315   .414
#> recR_2    F1       .489   .445   .534
#> recR_3    F1       .549   .506   .592
#> recR_4    F1       .997   .977  1.017
#> recR_5    F1       .914   .894   .934
#> recR_6    F1       .621   .591   .650
#> ethR_1    F2       .508   .467   .550
#> ethR_2    F2       .565   .519   .610
#> ethR_3    F2       .739   .700   .778
#> ethR_4    F2       .617   .580   .654
#> ethR_5    F2       .544   .500   .588
#> ethR_6    F2       .685   .649   .721
#> heaR_1    F2       .373   .323   .423
#> heaR_2    F2       .337   .284   .389
#> socR_1    F3       .693   .667   .719
#> socR_2    F3       .805   .783   .827
#> socR_3    F3       .648   .621   .675
#> socR_4    F3       .729   .704   .754
#> socR_5    F3       .436   .400   .473
#> socR_6    F3       .592   .561   .622
#> finR_1    F4       .804   .773   .835
#> finR_3    F4       .820   .790   .850
#> finR_5    F4       .834   .804   .864
#> heaR_3    F5       .676   .633   .719
#> heaR_4    F5       .705   .658   .752
#> heaR_5    F5       .463   .414   .513
#> heaR_6    F5       .397   .347   .448
#> recR_1    F5       .308   .254   .362
#> recR_2    F5       .354   .306   .402
#> recR_3    F5       .328   .278   .378
#> finR_2    F6       .699   .671   .727
#> finR_4    F6       .779   .751   .806
#> finR_6    F6       .741   .714   .768
#> 
#> ── Factor Intercorrelations ────────────────────────────────────────────────────
#> 
#>       F1     F2     F3     F4     F5     F6
#> F1  1.000
#> F2   .231  1.000
#> F3   .134  -.074  1.000
#> F4   .236   .397  -.147  1.000
#> F5   .404   .469   .112   .312  1.000
#> F6   .363   .151   .195   .290   .181  1.000
#> 
#> ── 95% Wald CIs for factor intercorrelations ───────────────────────────────────
#> 
#> Factors   est    lower  upper
#> F1 ~~ F2   .231   .195   .267
#> F1 ~~ F3   .134   .106   .162
#> F1 ~~ F4   .236   .196   .277
#> F1 ~~ F5   .404   .372   .436
#> F1 ~~ F6   .363   .329   .396
#> F2 ~~ F3  -.074  -.104  -.043
#> F2 ~~ F4   .397   .358   .435
#> F2 ~~ F5   .469   .440   .498
#> F2 ~~ F6   .151   .114   .188
#> F3 ~~ F4  -.147  -.191  -.103
#> F3 ~~ F5   .112   .082   .142
#> F3 ~~ F6   .195   .162   .229
#> F4 ~~ F5   .312   .269   .355
#> F4 ~~ F6   .290   .247   .334
#> F5 ~~ F6   .181   .142   .220
#> 
#> ── Structure Matrix ────────────────────────────────────────────────────────────
#> 
#>           F1     F2     F3     F4     F5    F6
#> ethR_1   .231   .595  -.009   .339   .364  .242
#> ethR_2   .216   .639   .032   .366   .371  .169
#> ethR_3   .252   .801  -.258   .388   .394  .168
#> ethR_4   .077   .601  -.086   .196   .276  .086
#> ethR_5   .272   .612  -.139   .301   .345  .119
#> ethR_6   .142   .680  -.032   .279   .310  .053
#> finR_1   .312   .427  -.106   .893   .366  .396
#> finR_2   .222   .030   .175   .148   .022  .687
#> finR_3   .322   .434  -.073   .900   .365  .393
#> finR_4   .303   .233   .055   .383   .204  .805
#> finR_5   .308   .443  -.081   .914   .351  .404
#> finR_6   .326   .191   .185   .358   .237  .799
#> heaR_1   .283   .515   .097   .325   .438  .148
#> heaR_2   .234   .482   .149   .269   .471  .107
#> heaR_3   .258   .424  -.022   .317   .712  .125
#> heaR_4   .325   .447  -.066   .392   .763  .133
#> heaR_5   .189   .330   .118   .141   .513  .094
#> heaR_6   .376   .460   .167   .277   .589  .224
#> recR_1   .483   .035   .320   .019   .389  .245
#> recR_2   .663   .297  -.060   .368   .560  .346
#> recR_3   .699   .211   .096   .252   .526  .355
#> recR_4   .939   .235   .113   .222   .266  .316
#> recR_5   .896   .313   .054   .284   .333  .300
#> recR_6   .711   .205   .160   .245   .365  .383
#> socR_1  -.013  -.158   .695  -.221  -.006  .061
#> socR_2   .141   .027   .770   .052   .149  .171
#> socR_3   .011  -.199   .667  -.213  -.032  .117
#> socR_4   .129  -.073   .702   .008   .090  .122
#> socR_5   .206   .055   .495  -.140   .115  .213
#> socR_6   .108  -.025   .638  -.169   .079  .226
#> 
#> ── Simple Structure Diagnostics ────────────────────────────────────────────────
#> 
#> Items with cross-loadings, |loading| >= .300 on multiple factors:
#> • recR_1: F1 = .364, F5 = .308
#> • recR_2: F1 = .489, F5 = .354
#> • recR_3: F1 = .549, F5 = .328
#> 
#> Items with primary-loading gap < .200:
#> • heaR_1: F2 = .373, F5 = .179
#> • heaR_2: F2 = .337, F5 = .267
#> • heaR_6: F5 = .397, F2 = .234
#> • recR_1: F1 = .364, F5 = .308
#> • recR_2: F1 = .489, F5 = .354
#> 
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                      F1     F2     F3     F4     F5     F6
#> SS loadings        3.280  3.153  2.887  2.708  2.250  2.053
#> Prop Tot Var        .109   .105   .096   .090   .075   .068
#> Cum Prop Tot Var    .109   .214   .311   .401   .476   .544
#> Prop Comm Var       .201   .193   .177   .166   .138   .126
#> Cum Prop Comm Var   .201   .394   .571   .737   .874  1.000
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> scaled χ²(270) = 3521.43, p < .001
#> CFI: .96
#> TLI: .93
#> RMSEA [90% CI]: .06 [.06; .06]
#> AIC: NA
#> BIC: NA
#> CAF: .41
#> SRMR: .03
#> 
#> ── Residual Diagnostics ────────────────────────────────────────────────────────
#> 
#> Residual cutoff: |r| > .100
#> Number of large residuals: 5
#> Largest absolute residual: .153
#> 
#> Largest residuals:
#> • socR_5 ~~ socR_6: .153
#> • heaR_1 ~~ heaR_2: .130
#> • heaR_1 ~~ heaR_4: -.112
#> • heaR_3 ~~ recR_1: -.110
#> • heaR_4 ~~ recR_1: -.100
#> 
#> Inspect the residual matrix for details (e.g., with residuals()).

# The same robust SEs and scaled chi-square for continuous data: a Pearson
# correlation with estimator ML or ULS (the fourth-moment ADF covariance).
ML_rob <- efa_fit(GRiPS_raw, n_factors = 1, cor_method = "pearson",
                  estimator = "ML", rotation = "none", se = "sandwich")
#> ℹ `x` is not a correlation matrix; computing correlations from the raw data.
ML_rob
#> 
#> EFA performed with estimator = 'ML' and rotation = 'none'.
#> 
#> ── Unrotated Loadings ──────────────────────────────────────────────────────────
#> 
#>             F1    h2    u2
#> fun        .796  .634  .366
#> friends    .851  .725  .275
#> enjoy      .872  .760  .240
#> hurt       .767  .588  .412
#> part       .817  .667  .333
#> commonly   .832  .692  .308
#> chances    .788  .621  .379
#> attracted  .845  .715  .285
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                 F1
#> SS loadings   5.401
#> Prop Tot Var   .675
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> scaled χ²(20) = 33.64, p = .029
#> CFI: 1.00
#> TLI: 1.00
#> RMSEA [90% CI]: .03 [.01; .05]
#> AIC: NA
#> BIC: NA
#> CAF: .50
#> SRMR: .01
summary(ML_rob)
#> 
#> EFA performed with estimator = 'ML' and rotation = 'none'.
#> 
#> ── Model Diagnostics ───────────────────────────────────────────────────────────
#> 
#> Factors: 1
#> Variables: 8
#> N: 810
#> Heywood cases: 0
#> Cross-loading items (|loading| >= .300): 0
#> Items without salient loading (|loading| >= .300): 0
#> Factors with fewer than 3 salient indicators: 0
#> Items with primary-loading gap < .200: 0
#> Largest |residual|: .030
#> 
#> ── Unrotated Loadings ──────────────────────────────────────────────────────────
#> 
#>             F1    h2    u2
#> fun        .796  .634  .366
#> friends    .851  .725  .275
#> enjoy      .872  .760  .240
#> hurt       .767  .588  .412
#> part       .817  .667  .333
#> commonly   .832  .692  .308
#> chances    .788  .621  .379
#> attracted  .845  .715  .285
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── 95% Wald CIs for salient unrotated loadings ─────────────────────────────────
#> 
#> Variable   Factor  est    lower  upper
#> fun        F1       .796   .761   .831
#> friends    F1       .851   .825   .878
#> enjoy      F1       .872   .851   .893
#> hurt       F1       .767   .726   .808
#> part       F1       .817   .782   .851
#> commonly   F1       .832   .800   .864
#> chances    F1       .788   .745   .831
#> attracted  F1       .845   .814   .877
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                 F1
#> SS loadings   5.401
#> Prop Tot Var   .675
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> scaled χ²(20) = 33.64, p = .029
#> CFI: 1.00
#> TLI: 1.00
#> RMSEA [90% CI]: .03 [.01; .05]
#> AIC: NA
#> BIC: NA
#> CAF: .50
#> SRMR: .01
#> 
#> Note: Wald CIs from the robust (Godambe) sandwich covariance.
#> 
#> ── Residual Diagnostics ────────────────────────────────────────────────────────
#> 
#> Residual cutoff: |r| > .100
#> Number of large residuals: 0
#> Largest absolute residual: .030
#> 
#> No absolute residuals > .100 occurred.
#> 
#> Inspect the residual matrix for details (e.g., with residuals()).
# }

# \donttest{
# Two-stage FIML correlations from raw data with missing values: the saturated
# multivariate-normal moments are EM-estimated (assuming the data are missing at
# random) and the standardized covariance is analysed.
x_miss <- GRiPS_raw
x_miss[cbind(1:20, 1)] <- NA
efa_fiml <- efa_fit(x_miss, n_factors = 1, estimator = "ML", cor_method = "fiml")
#> ℹ `x` is not a correlation matrix; computing correlations from the raw data.
efa_fiml
#> 
#> EFA performed with estimator = 'ML' and rotation = 'none'.
#> Correlations: FIML (two-stage, missing data)
#> 
#> ── Unrotated Loadings ──────────────────────────────────────────────────────────
#> 
#>             F1    h2    u2
#> fun        .795  .632  .368
#> friends    .852  .725  .275
#> enjoy      .871  .759  .241
#> hurt       .767  .588  .412
#> part       .817  .667  .333
#> commonly   .832  .692  .308
#> chances    .788  .620  .380
#> attracted  .845  .715  .285
#> 
#> Legend:
#>   bold = |loading| >= .300
#>   grey = below cutoff
#>   red h2/u2 = Heywood-relevant value
#> 
#> ── Variances Accounted for ─────────────────────────────────────────────────────
#> 
#>                 F1
#> SS loadings   5.400
#> Prop Tot Var   .675
#> 
#> ── Model Fit ───────────────────────────────────────────────────────────────────
#> 
#> scaled χ²(20) = 56.85, p < .001
#> CFI: 1.00
#> TLI: 1.00
#> RMSEA [90% CI]: .05 [.03; .06]
#> AIC: NA
#> BIC: NA
#> CAF: .50
#> SRMR: .01
# }

if (FALSE) { # \dontrun{
# Bootstrap standard errors from raw data, reproducible via a fixed seed and run
# in parallel across replicates. future::plan() returns the plan it replaces, so
# on.exit() puts the session back as it was -- also if the fit fails.
efa_boot <- local({
  old_plan <- future::plan(future::multisession, workers = 2)
  on.exit(future::plan(old_plan), add = TRUE)
  efa_fit(GRiPS_raw, n_factors = 1, estimator = "PAF", rotation = "none",
          se = "np-boot", b_boot = 1000, seed = 42)
})
} # }