Conditions this package raises — latents-conditions • latents

Every error a caller might reasonably want to handle carries a class, so it can be caught by what went wrong rather than by matching message text, which changes between versions. This page is the contract: the classes below are stable, the messages are not.

Value

Nothing; this page documents conditions rather than a function.

Input and data contracts

latents_bad_data: The data frame, the indicator columns or the group column break the input contract: too few rows, a duplicated or absent column name, an indicator that is not a numeric vector, an indicator that is constant or wholly unobserved, missing or non-finite values under missing = "error", or a missing group identifier.
latents_bad_argument: A scalar argument is outside its contract: n_profiles, n_group_classes, n_starts, max_iter, tol, min_variance, or a seed or seeds that is not a whole number set.seed() accepts.
latents_bad_time: time is missing, or repeats a position within a group, so the ordering it is supposed to supply does not exist.
latents_bad_categorical: A categorical indicator cannot be encoded, or min_probability leaves no room for its number of categories.
latents_bad_start: Supplied starting values are missing a block the model needs, do not have the shape it requires, or carry labels that name an indicator or a category this fit does not have.
latents_bad_fixed: A fixed request names a block the model does not have, or one that start does not supply, or start is absent.
latents_bad_stage: A measurement fit offered to fit_staged() has already estimated multiple group classes, or was fitted to different indicators, profiles or measurement options than the stage requested.
latents_bad_transition: A transition model was asked for with fewer than two profiles, or with no group observed at two occasions.
latents_bad_inference_data: The data passed to a verb alongside a fit do not reproduce the fit: a different number of rows, different values, or a different group ordering. Raised for a plain multilpa fit and for a covariate fit alike.
latents_no_indicator_data: A fit carries no stored indicators, so the request cannot be answered without the original data.
latents_incomplete_fit: A fit is missing fields the request needs, usually because it was made by an older version. Raised instead of letting a list subset by an absent name yield a NULL element keyed NA, which would leave a hole in the result.

Estimation and identification

latents_unidentified: The requested model cannot be identified from these data: more profiles than distinct observed indicator rows, more group classes than groups, or more than one group class with only one profile or only singleton groups.
latents_all_starts_failed: Every EM start raised, so no fit was produced. The message carries the distinct start errors.
latents_empty_profile: A profile has no observed responses for an indicator, so its response probabilities are not identified; or a group class has no effective membership, so the profile prevalence within it is not defined.
latents_too_few_groups: Fewer independent groups than the quantities being reported, so the covariance cannot be formed. Raised by robust parameter_inference(), and by three_step() and r3step(), whose cluster-robust contributions sum to zero at the estimate and so span at most one dimension fewer than the number of groups.
latents_inseparable_classes: Classes cannot be separated well enough for the requested three-step correction.
latents_no_converge: A fit required by the request did not converge, and using it would report an unconverged estimate as final. Raised for a plain multilpa fit and for a covariate fit alike.
latents_boundary_fit: A variance or a response probability sits at its bound, or a mixing probability is zero, so the parameter is on the edge of its space and the asymptotic normal approximation Wald inference relies on does not hold. The standard error is refused rather than reported.
latents_singular_information: The observed information is not positive definite, or is numerically singular, so it cannot be inverted and no standard error can be formed.
latents_unknown_candidate: The requested profile and group-class combination was never enumerated.
latents_failed_candidate: An enumerated candidate could not be fitted. The message carries that candidate's error.
latents_no_group_classes: A verb that needs discrete group classes was called on a model that has none. Every model family this version fits has them, so this is a guard against a future family rather than a refusal the current surface can produce.

Model comparison

latents_bad_nesting: Two models are not nested in the way the comparison requires, including a bootstrap_lrt() pair whose held measurement blocks differ, are held at different values, or are held in only one of the two models.
latents_incomparable_models: Two models were fitted to different data, or differ in covariance structure or centering mode where the comparison requires them to match.
latents_reversed_likelihood: The model with more parameters has the lower likelihood, so at least one fit is at a local optimum and the comparison is meaningless.
latents_bad_outcome: A distal outcome given to three_step() is not of a type the requested method can handle, varies within a group when a group-level outcome is requested, or is a measurement indicator that already helped define the classes.
latents_bad_covariate: A predictor given to r3step() varies within a group when group-level membership is predicted, makes the design rank deficient, or is a measurement indicator that already helped define the classes. The counterpart of latents_bad_outcome.
multilpa_indicator_reused: A measurement indicator was offered as an external variable, to three_step() as an outcome or to r3step() as a predictor. Raised alongside latents_bad_outcome or latents_bad_covariate, so one handler catches the mistake from either verb.
latents_bad_scores: Score contributions could not be formed for the robust sandwich.

Unsupported requests

These name capabilities the package does not have. They are raised in place of returning a number that would be wrong.

latents_unsupported_inference: Standard errors are not available for this particular fit. Raised for a covariate fit with categorical indicators, where no score is implemented for the response probabilities, for fits made by an older version, for a fit recording a covariance structure code this version does not know, and by parameter_inference(method = "bootstrap") for a fit holding a measurement block, whose held values came from a fit these data do not resample, and by parameter_inference() for a fit made with prior, which sits at a posterior mode rather than a likelihood maximum.
latents_unsupported_prior: multilpa(prior = ) was asked for a combination the conjugate prior is not defined for: the VEE, EVE, VVE or EVV covariance structure (mclust defines none), categorical indicators, a fixed block, membership covariates, or missing indicator values.
latents_unsupported_noise: multilpa(noise = TRUE) was asked for more than one group class, categorical indicators, a fixed block, membership covariates or missing indicator values; or a verb that does not yet account for a noise component — parameter_inference(), three_step(), r3step(), bootstrap_lrt(), starting_values(), bivariate residuals, fit_staged() or a posterior plot — was given a fit that has one.
latents_unsupported_structure: multilpa() was asked for a covariance structure other than EEI, VVI, EEE or VVV together with profile_covariates or group_covariates, which fit those four only.
latents_bad_weights: weights does not name a column, or its values are missing, negative, non-finite, vary within a unit, or are positive for fewer than two units.
latents_unsupported_weights: A weighted fit was given to a verb or option that does not account for sampling weights: bootstrap_lrt(), enumerate_regressions(bootstrap = ), three_step(), r3step(), prior or noise; or a non-sandwich vcov_type was asked of one.
latents_unsupported_indicator: ordinal or count indicators were combined with an option that does not take them yet: membership covariates, start, fixed, prior or noise.
latents_pooling_failed: An imputation's fit or inference failed in pool_imputations(), or the imputations' fits report different parameters. Rubin's rules need every imputation, so none is dropped; the message names the imputation and carries the original reason.
latents_bootstrap_failed: Fewer than two resamples produced a usable fit, so there is nothing to read a spread from. Raised by parameter_inference(method = "bootstrap"); the message carries the first reason a resample gave.
latents_no_free_parameters: The fit holds every parameter it has, so there is no free coordinate to report a standard error for. Raised by parameter_inference() and vcov() on a fully held fit.
latents_held_parameter: A named parameter was held fixed by this fit, so it has no sampling distribution and no confidence interval. Raised by confint() when parm names a held coordinate.
latents_unsupported_sensitivity: sensitivity() was called on a model family whose refit needs arguments the shared refit does not carry, whose profile labels cannot yet be aligned between two fits, or a directly fixed fit whose original free starts cannot be replayed.
latents_unsupported_three_step: three_step() or r3step() was called on a membership-covariate fit. Its classification errors depend on the fitted predictors, but the correction here uses one unconditional error matrix.
latents_unsupported_bootstrap: bootstrap_lrt() was called on a person-centred fit. Group baselines were removed before estimation and no distribution for them is available to simulate raw data under the null model.
latents_unsupported_tna: get_tna() or get_group_tna() was called on a general transition fit (a lta() fit with occasion-varying or covariate-dependent transitions, a second order, movers and stayers, or ordinal, count or weighted data), which has no single transition matrix to hand over; its transitions are in get_results(x, "transitions").
latents_no_plot: plot(x, what = "all") was called on an object whose plot method names no views to draw.
latents_no_time: A sequence verb was called on a fit made without time, so it carries no ordering.
latents_missing_package: A verb needs a package listed in Suggests that is not installed. Raised by get_tna() and get_group_tna() when the tna package is absent; the message names the package and how to install it.

Plotting

latents_no_continuous, latents_no_categorical: The requested panel needs indicators of a kind this fit does not have.
latents_nothing_to_describe: descriptives() was given no variables to describe: either vars named none, or the frame has no numeric column outside id and by to fall back on.
latents_nothing_to_plot: The requested panel has no rows to draw.
latents_unknown_category, latents_unknown_criterion: A named category or information criterion does not exist in this fit.
latents_bad_scale: An unrecognised plotting scale was requested.

Warnings

A fit that is returned but qualified warns rather than errors, so that a simulation loop can catch the qualification it cares about and let the others through.

latents_failed_starts: Some, but not all, EM starts raised. The surviving starts are in get_results(fit, "starts").
latents_not_identified: mixture_regression() with a binary outcome and one trial per class assignment: a mixture of Bernoulli regressions is not identified there.
latents_missing_data: mixture_regression() with missing = "error" met a missing value in a variable the model uses.
latents_rows_dropped: Warning. mixture_regression() with missing = "omit" dropped incomplete rows; the message states how many.
latents_no_valid_start: Every mixture_regression() start degenerated: a class emptied or its variance collapsed.
latents_degenerate_start: Warning. Some mixture_regression() starts degenerated and were set aside.
latents_separation: Warning. A mixture_regression() logit or log-link coefficient diverged (perfect separation); it and its standard error are unreliable.
latents_em_decrease: The mixture_regression() EM likelihood decreased beyond roundoff. This indicates a defect and should be reported.
latents_unconverged: The best start had not converged when max_iter was reached, so the returned estimate is not a maximum.
latents_boundary: A variance, or a covariance eigenvalue, sits at min_variance, so the fit is bound-active and its standard errors are not valid at that parameter.
latents_small_classes: A profile or group class has effective membership below one, so it is supported by less than one observation.
latents_weak_class: multilpa(family = "additive") only: a group class is supported by fewer than 50 effective groups (see effective_groups in get_results.multilpa_additive()), because it is small, poorly separated, or both. Standard errors are still reported, but intervals for class means and weights can be miscalibrated.
latents_failed_replicates: Some bootstrap replicates failed validation, so the bootstrap p-value is NA.
latents_single_level: A message, not a warning: id = NULL was passed, so the fit has one observation per unit and no second level. Raised by multilpa() on every such fit and once by an enumerate_classes() grid; suppressMessages() or a latents_single_level handler silences it.
latents_bootstrap_dropped: Some resamples did not produce a usable fit and were left out of the interval. Raised by parameter_inference(method = "bootstrap"), naming how many, so the count the interval rests on is never quietly smaller than iter.
latents_sensitivity_dropped: One or more seeds did not produce a fit in sensitivity(). Their rows are NA rather than absent, and the warning names how many failed and the first reason, so the table is never quietly shorter than seeds.
latents_extreme_coefficients: A membership logit coefficient is large enough that the class is close to separated, so the estimate is driven by scaling, a sparse class or separation rather than by the data.
latents_empty_transition_row: A profile is never occupied before a final occasion, so its transition row is uniform by construction rather than estimated. get_results(fit, "transitions", estimated = FALSE) lists them. The transition-network handoff also warns when it includes such a row, because it cannot be read as evidence of persistence.
latents_unverified_alignment: A supplied data frame has too little fitted information to establish row order, for example only a repeated group identifier. The result is returned on the caller's assurance that the rows are in fitting order.

Examples

set.seed(1)
example_data <- data.frame(
  school = rep(seq_len(8), each = 6),
  score_a = stats::rnorm(48), score_b = stats::rnorm(48)
)
fit <- multilpa(example_data, c("score_a", "score_b"), "school",
                n_profiles = 2, n_group_classes = 1, n_starts = 2, seed = 1)
# A sequence verb on a fit made without `time` is catchable by class.
tryCatch(get_results(fit, "sequences"),
         latents_no_time = function(condition) {
  "this fit carries no ordering"
})
#> [1] "this fit carries no ordering"