| sfa_leximax | R Documentation |
Chooses, among the orientations that reproduce a fitted factor solution
identically, the one whose factors sit closest to real construct terms.
The optimizer alternates naming (the full sfa_name() selection rule on
the current pattern matrix) with oblique target rotation toward the
retrieved terms' predicted loading profiles, from multiple deterministic
starts, until the retrieved term tuple recurs. The recurrent states are
the candidate solutions (a fixed point is the one-state case), and the
recurrent state with the highest nameability wins, with ties broken by
start order and then iteration. The winner is re-named through the
canonical blocked pool path and those labels are authoritative.
sfa_leximax(
x,
Phi = NULL,
lexmap = NULL,
model = NULL,
pool = NULL,
instruction = NULL,
n_random = 10L,
seed = 42L,
col_scale = c("unitmax", "z"),
rotation = c("oblique", "orthogonal"),
normalize = FALSE,
max_iter = 30L,
block_size = 50000L,
baseline = NULL,
baseline_sd = NULL,
...
)
x |
An |
Phi |
Factor correlations when |
lexmap |
An |
model, pool, instruction |
Passed to |
n_random |
Number of seeded random orthonormal starts appended to the oblimin, varimax, and geominQ starts (default 10). |
seed |
Base seed for the random starts (default 42). |
col_scale |
Target column scaling: |
rotation |
|
normalize |
Kaiser row normalization inside the target rotation. |
max_iter |
Iteration cap per start (default 30). |
block_size |
Passed to |
baseline, baseline_sd |
Optional per-pool-word null mean and standard
deviation for surprise scoring, passed through to
|
... |
Passed to the embedding backend via |
Also available as rotate = "leximax" in sfa().
An object of class sfa_leximax: loadings,
Phi, Th, A0, labels (with the canonical
retrieved labels and leave-one-out candidate sets), criterion,
per_factor, start, iteration, history,
converged.
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