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tse_lca() fits the Step-1 measurement model from a
formula, cbind(Y1, Y2, ...) ~ 1, with a
control = tse_control() argument. With several numbers of
classes (e.g. nclass = 1:6) it returns a
tseLCA_select class-enumeration table: log-likelihood,
parameters, AIC, BIC, SABIC, entropy R^2, and smallest class. The table
has print(), plot(),
as.data.frame(), best_model(), and
[[ methods. The one-class (independence) model is fitted in
closed form.tse_classify() (Step 2) assigns observations to
classes with a fixed measurement model
(assignment = "modal" or "proportional"). It
reports the classification-error probabilities P(W = s | X = t) and
entropy R^2. With newdata it classifies another sample,
replacing the step1 = route for using a measurement model
from one sample on another. tse_lca() models now keep their
data for this purpose.tse_covariate() fits the multinomial logit of class
membership on a covariate formula (factors, interactions,
transformations). Arguments: method = "ML",
"BCH", or "none" (the uncorrected three-step
estimator, for comparison); se = "corrected" or
"robust"; ref (reference class);
start. Methods: predict() (class probabilities
given covariates), relevel(), and anova()
(Wald tests per term).tse_distal() fits a distal outcome
(Zo ~ 1, any supported family, as a string or
family object). Given a tse_covariate() model it fits the
combined model.tse_twostep() gives two-step estimates (Bakk & Kuha
2018). With se = TRUE they come with multilevLCA’s
corrected standard errors.tseLCA() fits a whole model in one call:
cbind(indicators) ~ covariates | distal outcome (Formula
package). It chains the step-wise functions. Accessors
measurement(), classification(),
covariate(), and distal() extract the
components of any fitted model.tse_covariate() and tse_distal() accept
data: the classified data (same rows) with additional
columns, e.g. variables created after classification.omnibus_test() returns a standard htest
object.three_step() arguments to the new
functions.predict() for measurement models gives posterior class
probabilities (or modal classes) for new data, coded with the model’s
stored categories. fitted(), formula(), and
update() also work.item_probs(se = TRUE) and
class_sizes(se = TRUE) also return standard errors (delta
method from the measurement model’s variance).start in tse_lca() and
tseLCA() accepts item_probs() of a fitted
model directly, e.g. to refit a chosen model with covariates.as_tse_lca() builds a measurement model from given
class sizes and item-response probabilities (e.g. estimates reported
elsewhere, or saved from an earlier fit) without re-estimating it. The
result can be passed to tse_classify() like any
tse_lca() model.tseLCA is
the parent class of every fit; tseLCA_structural is the
parent of tseLCA_covariate, tseLCA_distal, and
tseLCA_both. Methods are defined once on the parent
classes, not separately for each subclass.logLik() (with df
and nobs, so AIC() and BIC()
work), nobs(), posterior(),
classes(), class_sizes(), and
item_probs().summary() now returns a
summary.tseLCA_structural or
summary.tseLCA_measurement object. Its tables print with
printCoefmat() and are extracted with
coef(summary(fit)).plot() now works on distal-outcome fits. These objects
previously did not store the measurement model, so the plot failed.$measurement_model$Y.levels). Previously they had to be
integers starting at 0. Codes such as 1..K were accepted but silently
misread: the highest category was never matched.step1 applies its
own categories to new data. The new data may omit categories, and values
outside them are an error.model.frame()/model.matrix(), so factor
covariates are dummy coded and unused factor levels are dropped. The
intercept is named "(Intercept)" (was
"Intercept").tse_control() collects the numerical estimation
settings.tse_lca(missing = "fiml") with random starts
(n_init) failed with a cryptic error from multilevLCA
(“sort_index(): detected NaN”) when indicator values were missing:
multilevLCA cannot start from a given classification on incomplete data.
It now warns and uses multilevLCA’s default initialization; a
user-supplied start gives a clear error.
Corrected (ML) standard errors: the Jacobian of the classification-error matrix with respect to the Step-1 parameters, which carries the Step-1 uncertainty into Step 3, had its item-parameter columns ordered item by item, while the Step-1 variance is ordered class by class; with modal assignment it also differentiated the assignment as if it were the posterior probabilities. Both are fixed (checked against numerical derivatives). Point estimates, robust, and BCH standard errors are unchanged; corrected covariate standard errors typically increase by a few percent, and corrected standard errors no longer depend on the choice of reference class.
Structural models with a reference class other than the first
(ref =): posterior(), classes(),
item_probs(), class_sizes(), and
plot() reported the classes in the rebased order under the
original labels, and in combined covariate + distal models the distal
parameters of class t belonged to another class. All now use the
measurement model’s class order.
Step-1 variance of polytomous indicators when the first
(reference) category of an item has a boundary probability in some
class: the free parameters of that item are log-ratios against this
category, and their common shift, log P(Y = first | class), is not
informed by the data. It is now treated as fixed, like other boundary
parameters. Previously the variance of these log-ratios was huge (the
information matrix was nearly singular) or all NA
(singular), which made the corrected standard errors of the Step-3
models NA.
If the Step-1 information matrix is still singular, Step-3 models
warn and report robust standard errors (was NA).
tse_twostep(se = TRUE) with a reference class other
than the first returned the class-1-reference estimates and variance
under the new class labels. They are now transformed to the requested
reference class. It also no longer warns, wrongly, that multilevLCA’s
measurement model differs.
For measurement-only fits, posterior() /
$posteriors and classes() /
$classifications were not in data-row order. They were read
from multilevLCA’s fit0$mU, which is sorted by response
pattern. They are now computed from the Step-1 data in data-row order.
Code that used $classifications from a measurement model,
e.g. as startval, received mismatched rows in
1.1.x.
Polytomous indicators were decoded from fit0$mU as
category 0 whenever the measurement model was fitted with listwise
deletion (incomplete = FALSE). The Step-1 information
matrix was then singular, so vcov() of a polytomous
measurement model and the Step-1-corrected Step-3 variance
(use.simple.cov = FALSE) were all NA. The
measurement model now stores its Step-1 sample in data-row order and
uses it for all of these computations. The fallback decoder for objects
without stored data handles both of multilevLCA’s codings.
Parameter counts behind the reported AIC/BIC were wrong.
Covariate models counted one-hot indicator columns, not free item
parameters (e.g. 40, not 22, for six binary items, three classes, one
covariate). Combined covariate + distal models counted class sizes, not
the covariate coefficients. Reported AIC/BIC
for these models change; estimates and standard errors do not.
Gaussian distal outcomes: the within-class variance was fixed at
sigma2 = 1 in the Step-3 likelihood. Three-step ML now estimates a
common sigma2 jointly with the class means, as in Bakk, Tekle &
Vermunt (2013). Unlike ordinary regression, sigma2 does not factor out
of the mean estimates here: it enters the posterior weights P(X = t | W,
Zo). With the variance fixed at 1, ML class means were biased whenever
the outcome’s variance differed from 1 (e.g. roughly 3.5x too far apart
for a residual SD of 3) and were not equivariant to rescaling the
outcome. The Hessian, score, and Step-1/Step-2 uncertainty propagation
now include sigma2. It is reported as $sigma2 (estimate
and, under ML, standard error), and counted in the log-likelihood’s
degrees of freedom. BCH class means were unaffected; their
log-likelihood now uses the estimated variance.
Three-step ML for distal outcomes with proportional assignment
(use.modal.assignment = FALSE) maximized the wrong
likelihood. It used log sum_t P(t) f(z|t) sum_s w_s P(W=s|t), with the
assignment weights inside the log. The likelihood of Bakk, Tekle &
Vermunt (2013), for the expanded data file weighted by the posterior
assignment probabilities, is sum_s w_s log sum_t P(t) f(z|t) P(W=s|t).
The covariate model already used this form. The E-step, score,
Hessian/Jacobian, and Step-1/Step-2 uncertainty propagation now use the
correct likelihood for all distal families. Proportional-assignment ML
distal estimates were biased away from zero (e.g. class means of -1.07
and 1.06, not -1 and 1, at mid separation). Modal-assignment fits, whose
single assignment makes the two forms identical, BCH, and covariate
models are unchanged.
Covariate models with include.intercept = FALSE
failed with a “length of ‘dimnames’” error. The coefficient rows were
always labeled with an Intercept row.
In combined covariate + distal models, rows with a missing
covariate but an observed distal outcome were kept in the distal model
with NA class priors. The distal model now uses rows with
complete covariates.
When the Step-3 Hessian of an ML covariate model could not be
inverted without an error being raised (non-finite entries), standard
errors fell back to the outer product of the scores without a warning.
Every fallback now warns. The documentation of
hessian = "opg" (correct.spec in
three_step()) described the outer-product estimator as
model-robust; it is valid only under a correctly specified Step-3
model.
three_step() is deprecated in favor of
tseLCA() and the step-wise functions. It keeps working,
with the same estimates, and warns once per session. Its help page maps
each argument to the 2.0 interface.lca_step1(), lca_step1_startval(),
fitZ_from_fit0(), and fitZ_from_multiLCA() are
deprecated in favor of tse_lca() and
tse_twostep(). They warn once per session when called
directly.options(tseLCA.warn.deprecated = FALSE) silences these
warnings.coef() returns a named vector whose names match
vcov(), so confint() works.
coef(fit, matrix = TRUE) gives the previous Q x (T-1)
(covariate) or T x C (multinomial distal) layout.coef() on a measurement model returns the log-ratio
parameters that vcov() describes. The previous list is now
available as class_sizes() and
item_probs()."(Intercept)" (was
"Intercept"), e.g. "(Intercept):C2".which argument of coef() and
vcov() is replaced by component
("covariate" or "distal") and
step ("two_step"). Passing which
gives an error, so old code cannot silently return a different
quantity.vcov() on a tseLCA_both object returns one
matrix. The covariate and distal blocks are on the diagonal; the
cross-covariances are not computed and are NA.Authors@R from
contributor ("ctb") to author and copyright holder
(c("aut", "cph")), reflecting his contribution to the
package. No code changes.lca_step1_startval(), a wrapper around
multilevLCA::multiLCA() that injects a user-supplied Step-1
starting value and sets kmea = FALSE, bypassing
multilevLCA’s default deterministic k-means-on-principal-
components initialization. This addresses feedback that the default
initialization can consistently converge to a local optimum of the
Step-1 log-likelihood on some datasets. startval accepts
either:
1..n_classes, one
entry per row of data), e.g. the modal class from an
external solver run with many random starts (‘StepMix’, ‘poLCA’);
orP(Y_h = k | X = t), from which a classification is derived
internally (naive-Bayes argmax under a flat class prior). This is the
natural format for an externally estimated Step-1 solution that isn’t
tied to the current sample, e.g. poLCA’s probs
output or a published item-response table. Either way, users can pass
their external Step-1 solution straight through instead of
hand-assembling a tseLCA measurement object.startval argument to
lca_step1() and fitZ_from_multiLCA(), so every
place a measurement model is fit from raw data can use an external
starting value instead of multilevLCA’s k-means
initialization. Supplying startval skips the
iter.measurement/R2.threshold random-restart
logic, since restarting from fresh k-means seeds would defeat the
purpose of a user-vetted start.startval argument to three_step(),
forwarded to lca_step1() (and to
fitZ_from_multiLCA() when
get.twostep.vcov = TRUE) so the measurement model can be
fit from an external starting value in a single call.n_init)n_init argument to lca_step1(),
fitZ_from_multiLCA(), and three_step(): the
unconditional multi-random-start analog of n_init in
StepMix or nrep in poLCA. When
supplied, the measurement model is fit n_init times from
independent uniform-random classifications (kmea = FALSE,
not multilevLCA’s deterministic k-means-on-PCA path), and
the highest-log-likelihood fit is kept. This is a separate mechanism
from the existing
iter.measurement/R2.threshold restart logic,
which reruns multilevLCA’s own k-means initialization and
only when entropy R^2 is low; n_init restarts always run
and never use k-means.step1, startval, and n_init
are mutually exclusive ways of controlling Step 1 in
three_step() (and startval/n_init
in lca_step1() and fitZ_from_multiLCA());
supplying more than one errors.use.bch = TRUE estimation (both covariate and
distal-outcome models). pwx[s, t] = P(W = s | X = t) is
column-stochastic; the correct BCH weight matrix is
w.is %*% t(pwx)^-1 (Mplus Web Note 21: the row of
H^-1 for each case’s most likely class, where
H = t(pwx) is row-stochastic), not
w.is %*% pwx^-1, which the code had been computing. The two
orientations only agree when pwx is symmetric, so the bug
was largely invisible in well-separated, balanced test cases; with
genuine classification-error asymmetry it biased BCH point estimates and
could produce the “negative column sums” error the package warns about
(users were advised to fall back to use.bch = FALSE). With
the corrected orientation, every row of the weight matrix sums to 1 and
the class totals it implies exactly match the posterior class sizes. The
weight-matrix computation is now consolidated into a single internal
helper (bch_weight_matrix()) used by all four call sites
that previously duplicated it, with a regression test asserting
rowSums(w.it) == 1 under a deliberately asymmetric
classification-error matrix.family = "multinomial" to
three_step()’s distal-outcome estimation, for a nominal
categorical outcome with 2 or more categories (Zo.name may
be a factor, character, or integer column). Estimates a saturated model
– the T x C matrix of class-conditional category
probabilities pi_hat[t, c] = P(Zo = c | X = t) – with the
closed-form weighted-proportion estimator
pi_hat[t, c] = sum_i w_it * 1(y_i = c) / sum_i w_it, for
both use.bch = TRUE (BCH weights) and
use.bch = FALSE (ML, through EM with the same closed-form
M-step and responsibility-weighted E-step). This replaces the previous
workaround of fitting one family = "binomial" model per
category and renormalizing the resulting probabilities by hand, which
wasn’t constrained to the simplex before renormalizing and had no joint
covariance across categories.
coef() returns the T x C probability
matrix (rows sum to 1) instead of a length-T vector;
vcov() returns its (T*C) x (T*C) sandwich
covariance, which is necessarily rank-deficient (each class’s row sums
to 1).use.bch = FALSE,
use.simple.cov = FALSE (the default) fully propagates both
Step-1 measurement uncertainty and, when Zp.names is also
supplied, Step-3 covariate uncertainty into the SEs, matching the
existing gaussian/poisson/binomial ML paths exactly (the T x C
generalization of each chain-rule term – C1_mat for Step 1,
C_mat for Step 2 – only required expanding the “unit score”
g_it across categories, since neither term’s derivation
otherwise depends on the outcome’s dimensionality). The ML bread
(multinomial_ml_jacobian()) is a closed-form Jacobian of
the estimating equation, generalizing ml_hessian_distal()’s
“observed = complete - missing information” correction to the T x C
case. Both the bread and the full propagation (including the Step-2
term) were cross-validated against independent, from-scratch numerical
differentiation (explicit per-case loops sharing no code with the
package, optim() from multiple starting points for the
point estimate) and matched to machine precision.omnibus_test(), a generalized Wald test of
H0: theta_1 = theta_2 = ... = theta_T (the distal outcome’s
class-t parameter vector is the same for every class) for any
tseLCA_distal or tseLCA_both object,
regardless of family. Uses a Moore-Penrose pseudo-inverse of the
contrast covariance so it remains valid when that covariance is
singular, as it always is for family = "multinomial"; the
resulting degrees of freedom recover the textbook
(T-1)*(C-1) for a T x C chi-squared test of
homogeneity in that case, and T-1 for the scalar-parameter
families.family = "binomial", whose
coef()/vcov() are on the logit scale,
family = "multinomial" reports
coef()/vcov() directly on the probability
scale – Std.Error is directly interpretable without a
delta-method back-transform, but a symmetric interval
Estimate +/- 1.96*Std.Error can fall outside the unit
interval for a probability near a boundary, the same known limitation as
a naive Wald interval for a sample proportion, and the per-cell
z.value/p.value (testing each probability
against 0) are rarely the question of interest.
print()/summary() now print a one-line
reminder of this after a multinomial distal-outcome table, pointing to
omnibus_test() for the intended, boundary-safe test of
whether the distribution differs across classes.use.simple.cov = TRUE) that bypasses the
measurement-uncertainty correction for faster computation in large,
well-separated samples.incomplete = TRUE argument (using a two-pass row-filtering
strategy).step1 argument) to reuse across multiple structural models
or apply to different sample subsets.use.modal.assignment).rebase argument to allow users to easily
change the reference latent class for the multinomial logit
parameterization while maintaining invariant log-likelihoods.fitZ_from_fit0())
to generate stable starting values for the three-step structural
model.tseLCA objects:
summary(), coef(), vcov(), and
plot() (which delegates to ‘multilevLCA’ for item-profile
visualization).generate_data()) that
replicates the Bakk & Kuha (2018) simulation study design for both
covariates and distal outcomes under varying separation conditions.T/F with
TRUE/FALSE throughout internal codebase\value tags to all exported functions missing
them, including bk2018_params.inst/examples: examples now write to
tempdir() instead of the home filespace.inst/examples: commented out
rm(list = ls()) calls.inst/examples: commented out
install.packages() calls.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.