Tables of an additive group-class fit — get_results.multilpa

Tidy tables of a fit from multilpa(family = "additive"). Every table is a base data.frame; the fit itself never needs to be reached into.

Usage

# S3 method for class 'multilpa_additive'
get_results(
  x,
  what = "parameters",
  level = 0.95,
  vcov_type = c("observed", "robust", "opg"),
  adjust = .multilpa_p_adjust_methods,
  data = NULL,
  truth = NULL,
  ...
)

# S3 method for class 'multilpa_additive'
as.data.frame(x, row.names = NULL, optional = FALSE, what = "parameters", ...)

# S3 method for class 'multilpa_additive'
coef(object, scale = c("natural", "unconstrained"), ...)

# S3 method for class 'multilpa_additive'
vcov(
  object,
  type = c("observed", "robust", "opg"),
  scale = c("natural", "unconstrained"),
  ...
)

# S3 method for class 'multilpa_additive'
confint(object, parm, level = 0.95, type = c("observed", "robust", "opg"), ...)

# S3 method for class 'multilpa_additive'
logLik(object, ...)

# S3 method for class 'multilpa_additive'
nobs(object, ...)

Arguments

x: A multilpa_additive fit.
what: Which table; "all" returns a named list of every table.
level: Confidence level of the intervals in "parameters".
vcov_type: "observed" (inverse observed information), "robust" (CR0 sandwich over groups) or "opg" (inverse outer product of the group scores).
adjust: p-value adjustment across the class means, one of stats::p.adjust.methods; "none" by default, as elsewhere in latents.
data, truth: For what = "recovery" only: the data the fit was made from and the name of a column, constant within groups, holding a known group classification.
...: Unused.
row.names, optional: Unused; part of the generic.
object: A multilpa_additive fit.
scale: "natural" (means, variances, class weights) or "unconstrained" (means, log variances, class-1-baseline logits). Names of coef() and the rows of vcov() agree on either scale.
type: Covariance type, as vcov_type.
parm: Natural parameter names or positions; all by default.

Value

A base data.frame, or a named list of them for what = "all".

Tables

parameters: One row per natural parameter: level ("between" for class means and between-group variances, "within" for the shared within-group variances, "group_class" for class weights), group_class ("shared" for a parameter common to all classes), indicator (NA for weights), parameter ("mean", "variance", "weight"), estimate, standard_error, statistic, p_value, p_adjusted, conf_low, conf_high. Only means carry a Wald test. Intervals for variances are formed on the log scale and for weights on the logit scale. When inference is refused (boundary, unconverged, singular information) the standard-error columns are NA and a latents_no_standard_errors message says why.
group_classes: One row per group class: weight, count (summed posterior), n_assigned (modal), mean_posterior among the groups assigned to it, and effective_groups: the number of perfectly classified groups that would estimate the class weight as precisely as this fit does, w^2 (1 - w) / Var(w). It equals the class's expected group count when classes are perfectly separated and falls when a class is small or poorly separated. Below 50, intervals for class parameters are flagged with a latents_weak_class warning (threshold and its simulation evaluation: validation/ADDITIVE_SIMULATION.md in the source repository). NA for one class or when inference is refused.
groups: One row per group (named by the id column): size n, modal group_class, its posterior, and probability_group_class_h.
intercepts: One row per group and indicator: n, the observed group average, and the posterior mean and variance of the group's intercept, mixed over its class posterior (so the variance includes uncertainty about class membership). Conditional on the estimated parameters; not a statement about their sampling uncertainty.
conditional_intercepts: As intercepts, one row per group, indicator and group class, conditional on that class, with the class posterior.
assignments: One row per data row: row, the id column, the group's modal group_class and posterior, and assignment_level = "group": every row inherits its group's class.
classification: One row per assigned and group class: the mean_posterior of group_class among groups assigned to assigned.
fit: One row: log likelihood, n_parameters, aic, bic (penalized by the number of groups), bic_individual (by rows), n_groups, n_obs, relative entropy over groups, the smallest class weight, min_effective_groups, converged, boundary, kkt and start replication.
recovery: Needs data and truth: one row per modal group class and known value, with the number of groups n and their share of the assigned class. Not part of what = "all".
starts: One row per start: likelihood, convergence, iterations, number of between variances held at zero, error message of a failed start, and which start was selected.

Examples

set.seed(1)
ratings <- data.frame(
  team = rep(seq_len(40), each = 6),
  climate = rep(rnorm(40, rep(c(-1, 1), each = 20), 0.5), each = 6) +
    rnorm(240),
  support = rep(rnorm(40, rep(c(-1, 1), each = 20), 0.5), each = 6) +
    rnorm(240))
fit <- multilpa(ratings, c("climate", "support"), "team",
                n_group_classes = 2, family = "additive", n_starts = 3,
                seed = 1)
get_results(fit)
#> Warning: Group classes supported by fewer than 50 effective groups (group_class_1: 19.7 effective of 20.4 expected groups, 96% of the information kept; group_class_2: 18.8 effective of 19.6 expected groups, 96% of the information kept). A small information share means poor separation; a small expected count means few groups. Wald intervals for class means and weights can be miscalibrated; see get_results(x, "group_classes").
#>          level   group_class indicator parameter    estimate standard_error
#> 1      between group_class_1   climate      mean -0.90841249     0.13365766
#> 2      between group_class_1   support      mean -0.91030607     0.13337034
#> 3      between group_class_2   climate      mean  1.07151874     0.12180149
#> 4      between group_class_2   support      mean  1.05245634     0.14155613
#> 5       within        shared   climate  variance  1.02556718     0.10255672
#> 6       within        shared   support  variance  1.14788037     0.11478804
#> 7      between group_class_1   climate  variance  0.17466828     0.11199090
#> 8      between group_class_1   support  variance  0.14745703     0.11201928
#> 9      between group_class_2   climate  variance  0.09965351     0.09154283
#> 10     between group_class_2   support  variance  0.18641044     0.12340834
#> 11 group_class group_class_1      <NA>    weight  0.51044770     0.08056876
#> 12 group_class group_class_2      <NA>    weight  0.48955230     0.08056876
#>    statistic      p_value   p_adjusted    conf_low  conf_high
#> 1  -6.796562 1.071455e-11 1.071455e-11 -1.17037668 -0.6464483
#> 2  -6.825401 8.768002e-12 8.768002e-12 -1.17170714 -0.6489050
#> 3   8.797255 1.402033e-18 1.402033e-18  0.83279221  1.3102453
#> 4   7.434905 1.046435e-13 1.046435e-13  0.77501142  1.3299013
#> 5         NA           NA           NA  0.84303180  1.2476256
#> 6         NA           NA           NA  0.94357511  1.3964223
#> 7         NA           NA           NA  0.04971126  0.6137242
#> 8         NA           NA           NA  0.03326823  0.6535837
#> 9         NA           NA           NA  0.01646529  0.6031368
#> 10        NA           NA           NA  0.05092766  0.6823179
#> 11        NA           NA           NA  0.35660584  0.6623357
#> 12        NA           NA           NA  0.33766432  0.6433942
get_results(fit, "group_classes")
#>     group_class    weight    count n_assigned mean_posterior effective_groups
#> 1 group_class_1 0.5104477 20.41791         20      0.9996703         19.65026
#> 2 group_class_2 0.4895523 19.58209         20      0.9787749         18.84587
get_results(fit, "parameters", vcov_type = "robust")
#> Warning: Group classes supported by fewer than 50 effective groups (group_class_1: 19.7 effective of 20.4 expected groups, 96% of the information kept; group_class_2: 18.9 effective of 19.6 expected groups, 96% of the information kept). A small information share means poor separation; a small expected count means few groups. Wald intervals for class means and weights can be miscalibrated; see get_results(x, "group_classes").
#>          level   group_class indicator parameter    estimate standard_error
#> 1      between group_class_1   climate      mean -0.90841249     0.13395472
#> 2      between group_class_1   support      mean -0.91030607     0.12998999
#> 3      between group_class_2   climate      mean  1.07151874     0.12302933
#> 4      between group_class_2   support      mean  1.05245634     0.14287131
#> 5       within        shared   climate  variance  1.02556718     0.09223567
#> 6       within        shared   support  variance  1.14788037     0.11855311
#> 7      between group_class_1   climate  variance  0.17466828     0.09511175
#> 8      between group_class_1   support  variance  0.14745703     0.08484683
#> 9      between group_class_2   climate  variance  0.09965351     0.07702866
#> 10     between group_class_2   support  variance  0.18641044     0.11534586
#> 11 group_class group_class_1      <NA>    weight  0.51044770     0.08049887
#> 12 group_class group_class_2      <NA>    weight  0.48955230     0.08049887
#>    statistic      p_value   p_adjusted    conf_low  conf_high
#> 1  -6.781489 1.189432e-11 1.189432e-11 -1.17095891 -0.6458661
#> 2  -7.002894 2.507293e-12 2.507293e-12 -1.16508177 -0.6555304
#> 3   8.709457 3.053329e-18 3.053329e-18  0.83038568  1.3126518
#> 4   7.366464 1.752136e-13 1.752136e-13  0.77243371  1.3324790
#> 5         NA           NA           NA  0.85982533  1.2232578
#> 6         NA           NA           NA  0.93752859  1.4054285
#> 7         NA           NA           NA  0.06007739  0.5078284
#> 8         NA           NA           NA  0.04774015  0.4554568
#> 9         NA           NA           NA  0.02190502  0.4533582
#> 10        NA           NA           NA  0.05543311  0.6268610
#> 11        NA           NA           NA  0.35673162  0.6622131
#> 12        NA           NA           NA  0.33778693  0.6432684