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Build starting values for a multilevel latent profile fit

Source: R/accessors.R
starting_values.Rd

Returns the starting-value list multilpa() accepts, taken from a fitted model or from any object that already carries the parameter blocks, such as a retained reference solution. This exists so that callers never assemble the list by hand, and so that starting values are validated where they are built rather than deep inside the fitting loop.

Usage

starting_values(
  x,
  covariance = c("auto", "drop", "keep"),
  what = c("all", "measurement")
)

Arguments

x

A fitted multilpa model, or a list carrying profile_probabilities, group_probabilities, and whichever measurement blocks the model uses: means with variances or covariances for continuous indicators, response_probabilities for categorical ones. An all-categorical model needs no Gaussian block. Any other elements are ignored.

covariance

"auto" keeps covariances when the object carries them, "drop" always returns the diagonal parameterization, and "keep" requires covariances and fails when they are absent.

what

"all" returns the measurement and mixing blocks. "measurement" omits profile_probabilities and group_probabilities, which are sized for the model that produced them and must not be carried into a staged fit that changes the number of group classes. Pass the result as start alongside fixed in multilpa().

Value

An object of class multilpa_start: the list multilpa() accepts as start, with elements means, variances, profile_probabilities, and group_probabilities, plus covariances when the full-covariance parameterization is returned and response_probabilities when the object carries a categorical measurement model. what = "measurement" drops profile_probabilities and group_probabilities. The Gaussian blocks are unnamed and indexed by position, matching what multilpa() expects of start; read them with as.data.frame(), which labels the positions, rather than out of the list. response_probabilities is the exception: the list keeps each indicator's name and each block keeps its category names, because those labels are what lets multilpa() attach a block to the item it was estimated for rather than to whichever item happens to sit in that position. A fit that names its categorical indicators in another order, or that has other categories, therefore refuses the start with latents_bad_start instead of reporting a likelihood for a measurement model it never held. The list payload is unchanged by the class, so a multilpa_start can be passed straight back as start.

See also

get_results() for the values as tidy tables.

Examples

set.seed(7)
example_data <- data.frame(
  school = rep(seq_len(12), each = 10),
  score_a = rnorm(120), score_b = rnorm(120)
)
fit <- multilpa(example_data, c("score_a", "score_b"), "school",
                  n_profiles = 2, n_group_classes = 1, n_starts = 2, seed = 1)
starting_values(fit)
#> Starting values: 2 profile(s), 2 continuous indicator(s)
#> Residual covariance: diagonal
#> Categorical indicators: 0
#> Mixing blocks: 1 group class(es)
#> Pass this to multilpa(start = ) as it is.
as.data.frame(starting_values(fit))
#>   profile indicator        mean  variance standard_deviation
#> 1       1         1  0.31636979 0.7483074          0.8650476
#> 2       1         2 -0.05291982 0.9149785          0.9565451
#> 3       2         1 -1.11359585 0.1018727          0.3191750
#> 4       2         2  0.79091589 0.2857973          0.5346002
refit <- multilpa(example_data, c("score_a", "score_b"), "school",
                    n_profiles = 2, n_group_classes = 1, n_starts = 1,
                    start = starting_values(fit))
logLik(refit)
#> 'log Lik.' -323.0197 (df=9)