Fits a candidate from state_choices(), selected by candidate_id, by
any combination of n_states, method, and lpa_model, or — when
nothing is specified — the recommended candidate.
Usage
fit_state_choice(
choices,
candidate_id = NULL,
labels = NULL,
state = "state",
n_states = NULL,
method = NULL,
lpa_model = NULL
)Arguments
- choices
A
vasstra_state_choicesobject fromstate_choices().- candidate_id
Optional explicit candidate number to fit.
- labels
Optional state labels ordered from low to high profile.
- state
Name of the state column created in the returned data.
- n_states
Optional state count used to select the candidate.
- method
Optional clustering method used to select the candidate.
- lpa_model
Optional LPA covariance model used to select the candidate.
Value
A vasstra_states object from step1_states() with the selected
candidate and complete comparison table recorded in diagnostics.
Examples
data <- expand.grid(student = 1:12, course = 1:3)
group <- rep(rep(1:3, each = 4), times = 3)
data$views <- group * 5 + data$course * 0.01
data$duration <- group * 10 - data$course * 0.01
choices <- state_choices(
data, "student", "course", c("views", "duration"),
n_states = 2:3, method = "kmeans"
)
fit_state_choice(choices) # the recommended candidate
#> Fitting recommended candidate 2.
#> VaSSTra Step 1: Variables -> States
#> 36 rows | 12 subjects | 3 times | 3 states | kmeans
#> Average silhouette: 1.000
#> State sizes: State 1=12, State 2=12, State 3=12
fit_state_choice(choices, n_states = 3)
#> VaSSTra Step 1: Variables -> States
#> 36 rows | 12 subjects | 3 times | 3 states | kmeans
#> Average silhouette: 1.000
#> State sizes: State 1=12, State 2=12, State 3=12