Run XSL model
Usage
xsl_run(model, data, control = xslControl())Value
A list with sse, unweighted_sse, and fits (one entry per
dataset). Each fit has matrix (the word-by-object matrix, summed over
simulations for a stochastic model), perf, sse, data, responses
(an n_sim x n-words matrix of each simulated participant's final
per-word accuracy), and sims (the full per-simulation xslFit list,
NULL unless control had keep_sims = TRUE).
For a multi-block condition (an xslData whose accuracy has one row
per block; see xslData()), block b is scored on a run trained on b
repetitions of the training sequence. Its fit's perf then has one row
per block, sse (and loglik) sum over blocks, matrix, responses
and sims are the last block's, and blocks holds each block's own
fit. Each block is a separate run, so for a stochastic model the blocks'
predictions come from independent simulated learners: right on average,
but not one learner's trajectory.
With a likelihood objective (xslControl(objective = "binomial") or
"multinomial"), each fit also has loglik, the log-likelihood of that
condition's human test responses, and the result has loglik (summed
over conditions) and nll (its negative, which xsl_fit() minimizes).
Unlike sse, which averages conditions weighted by n_subj, the
likelihood sums over every response, so larger conditions count for more
in the same way.