Tables of a mixture-of-regressions fit

Usage

# S3 method for class 'latents_mixture_regression'
get_results(
  x,
  what = "coefficients",
  level = 0.95,
  vcov_type = NULL,
  data = NULL,
  truth = NULL,
  by = c("class", "group_class"),
  ...
)

# S3 method for class 'latents_mixture_regression'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  what = "coefficients",
  level = 0.95,
  vcov_type = NULL,
  ...
)

# S3 method for class 'latents_mixture_regression'
coef(object, ...)

# S3 method for class 'latents_mixture_regression'
vcov(object, type = NULL, ...)

# S3 method for class 'latents_mixture_regression'
confint(object, parm, level = 0.95, ...)

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

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

Arguments

x: A fit from mixture_regression().
what: The table to return; see Tables. "all" returns every table in a named list.
level: Confidence level for the intervals.
vcov_type: NULL for the covariance stored with the fit, or "observed", "robust" or "opg" to recompute it.
data, truth: For what = "recovery": the data frame the model was fitted to and the name of its column holding a known classification.
by: For what = "recovery": "class", or "group_class" for the group classes of a two-level fit.
...: Unused.
row.names, optional: Unused; part of the generic.
object: A fit from mixture_regression().
type: Covariance type for vcov(); as vcov_type.
parm: Parameter names or positions; all by default.

Value

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

Tables

coefficients: One row per class and regression term: class ("common" for a shared term), term, estimate, std_error, statistic (Wald z), p_value, conf_low, conf_high, p_adjusted (Benjamini-Hochberg across the non-intercept rows; NA for intercepts) and, for the binomial and Poisson families, exp_estimate (odds or rate ratio). For the ordinal family each class's thresholds lead its rows as terms "threshold:<lower>|<upper>" (delta-method errors; no p_adjusted or exp_estimate), and exp_estimate of a slope is the cumulative odds ratio of a higher category.
classes: One row per class: share (mean posterior), count (summed posterior), n_assigned (modal assignment), mean_posterior (average posterior of the units assigned to it); sigma and sigma_std_error for the Gaussian family; prior and prior_std_error when membership has no covariates. Units are groups under class_level = "group".
membership: One row per multinomial-logit coefficient: model ("class" or "group_class"), class, term, reference, the Wald columns and odds_ratio. In the two-level model the class intercepts are per group class, (Intercept):group_class_h.
group_classes: Two-level model: one row per group class and class, with the model-implied class probability, the group_share, n_groups_assigned, and std_error of the probability when membership has no covariates.
fit: One row: log likelihood, parameter count, aic, bic (penalized by the number of independent units: rows, or groups when id defines them), bic_rows (always by rows, as flexmix reports it), sabic, icl, relative entropy (and group_entropy), the smallest class share, convergence and start replication.
assignments: One row per data row used: row (its position in the supplied data), the id column, modal class, its posterior, and one probability_class_k column per class; two-level fits add group_class and group_posterior.
groups: One row per group (class_level = "group" or the two-level model): its size, modal class or group class, and posterior probabilities.
fitted: One row per data row: observed, the posterior-weighted fitted mean, fitted_modal (the modal class's mean), residual and class.
starts: One row per start: its log likelihood, convergence, iterations, stage ("screened": stopped after the 50-iteration screening because better starts existed; "completed": run to convergence or max_iter), whether it degenerated, and which one was selected. n_best_replicated counts completed starts only.
classification: One row per assigned (modal) class and class: the mean_posterior probability of class among the units assigned to assigned, and n_assigned. Its diagonal is each class's average posterior probability; values near one mean well-separated classes.
recovery: Needs data and truth: the modal classes cross-tabulated against a known classification, one row per assigned class and true value, with the count n and its share of the assigned class. Group-level under class_level = "group" or by = "group_class", where truth must be constant within groups.

Examples

fit <- mixture_regression(score ~ hours, data = study_hours, n_classes = 2,
                          n_starts = 3, seed = 1)
get_results(fit, "coefficients")
#> Regression coefficients (95% CI)
#> 
#> Class    Term       Estimate          95% CI      p
#> -------  ---------  --------  --------------  -----
#> Class 1  Intercept     34.35  [32.94, 35.76]  <.001
#> Class 1  hours          4.57  [ 4.36,  4.77]  <.001
#> Class 2  Intercept     54.90  [52.75, 57.04]  <.001
#> Class 2  hours          0.81  [ 0.48,  1.14]  <.001
get_results(fit, "fit")
#> Model fit
#> 
#> Family          gaussian
#> Nesting         observation
#> Classes         2
#> Group classes   1
#> Observations    900
#> Parameters      7
#> Log likelihood  -3144.01
#> AIC             6302.01
#> BIC             6335.63
#> SABIC           6313.40
#> ICL             7030.59
#> Entropy         0.443
#> Smallest class  43.6%
#> Converged       yes
get_results(fit, "coefficients", vcov_type = "robust")
#> Regression coefficients (95% CI)
#> 
#> Class    Term       Estimate          95% CI      p
#> -------  ---------  --------  --------------  -----
#> Class 1  Intercept     34.35  [32.77, 35.94]  <.001
#> Class 1  hours          4.57  [ 4.34,  4.79]  <.001
#> Class 2  Intercept     54.90  [52.71, 57.08]  <.001
#> Class 2  hours          0.81  [ 0.46,  1.15]  <.001
get_results(fit, "recovery", data = study_hours, truth = "strategy")
#> Recovery of a known classification
#> 
#> Assigned  strategy  Rows  Share of assigned
#> --------  --------  ----  -----------------
#> Class 1   deep       448              0.782
#> Class 2   deep        49              0.150
#> Class 1   surface    125              0.218
#> Class 2   surface    278              0.850