Returns the fit statistics of the selected clustering as one tidy row,
or one row per compared candidate with compare = TRUE. LPA solutions
report the complete information-criterion family (log-likelihood, AIC,
BIC, SABIC, CAIC, AWE, CLC, KIC, ICL — all on the conventional
lower-is-better scale), normalized entropy, and the smallest and largest
average posterior class probabilities. Hard clustering methods report
their own objectives (total within-cluster sum of squares, PAM
objective). Silhouette and group sizes are always included, and columns
that do not apply to the fitted method are dropped.
Usage
fit_indices(x, ...)
# S3 method for class 'vasstra_states'
fit_indices(x, compare = FALSE, n_states = NULL, ...)
# S3 method for class 'vasstra_trajectories'
fit_indices(x, compare = FALSE, n_trajectories = NULL, ...)
# S3 method for class 'vasstra'
fit_indices(x, step = c("states", "trajectories"), compare = FALSE, ...)Arguments
- x
A
vasstra_states,vasstra_trajectories, or completevasstraobject.- ...
Method-specific arguments.
- compare
Return one row per compared candidate instead of only the fitted solution.
- n_states
Candidate state counts used when
compare = TRUE. Defaults to the stored automatic comparison when one exists, otherwise 2 through 6.- n_trajectories
Candidate trajectory counts used when
compare = TRUE. Defaults to the stored automatic comparison when one exists, otherwise 2 through 6.- step
Which clustering of a complete fit to summarize:
"states"(default) or"trajectories".
Value
A vasstra_fit_indices data frame: one row for the fitted
solution, or one row per candidate (with best and fitted markers)
when compare = TRUE.
Examples
set.seed(1)
data <- expand.grid(student = 1:12, course = 1:3)
level <- rep(c(2, 8, 16), length.out = nrow(data))
data$views <- level + rnorm(nrow(data), sd = 0.4)
data$duration <- level * 3 + rnorm(nrow(data), sd = 0.4)
states <- step1_states(data, n_states = 3)
#> Detected id = "student", time = "course", variables = 2 numeric indicators.
fit_indices(states)
#> VaSSTra fit indices: states (lpa)
#> n_states method lpa_model log_likelihood n_parameters aic bic sabic
#> 3 lpa EEI 101.91 10 -183.83 -167.99 -199.23
#> caic awe clc kic icl entropy prob_min prob_max silhouette
#> -157.99 -102.16 -203.83 -170.83 -167.99 1 1 1 0.95
#> min_size max_size size_ratio
#> 12 12 1
fit_indices(states, compare = TRUE)
#> VaSSTra fit indices: states (lpa)
#> n_states method lpa_model log_likelihood n_parameters aic bic sabic
#> 2 lpa EEI -60.80 7 135.59 146.68 124.81
#> 3 lpa EEI 101.91 10 -183.83 -167.99 -199.23
#> 4 lpa EEI 102.42 13 -178.84 -158.26 -198.87
#> 5 lpa EEI 105.40 16 -178.81 -153.47 -203.46
#> 6 lpa EEI 107.28 19 -176.55 -146.46 -205.82
#> caic awe clc kic icl entropy prob_min prob_max silhouette
#> 153.68 192.76 121.61 145.59 146.69 1.000 1.000 1.000 0.776
#> -157.99 -102.16 -203.83 -170.83 -167.99 1.000 1.000 1.000 0.950
#> -145.26 -72.67 -202.73 -162.84 -156.14 0.979 0.858 1.000 0.820
#> -137.47 -48.14 -207.06 -159.81 -149.72 0.968 0.810 1.000 0.604
#> -127.46 -21.38 -208.96 -154.55 -140.87 0.957 0.851 0.996 0.457
#> min_size max_size size_ratio eligible best fitted
#> 12 24 2 TRUE FALSE FALSE
#> 12 12 1 TRUE TRUE TRUE
#> 1 12 12 FALSE FALSE FALSE
#> 1 12 12 FALSE FALSE FALSE
#> 1 11 11 FALSE FALSE FALSE