Pre-process Coh-Metrix and ReaderBench data files before applying predictive models. Uses the artifact registry to load the correct variable lists and applies centering and scaling per sub-model, preserving the original behavior by model key.
Arguments
- model
Character scalar. Which scoring model to use. Supported values include: ReaderBench: 'rb_mod1','rb_mod2','rb_mod3narr','rb_mod3exp','rb_mod3per','rb_mod3all', 'rb_mod3narr_v2','rb_mod3exp_v2','rb_mod3per_v2','rb_mod3all_v2'; Coh-Metrix: 'coh_mod1','coh_mod2','coh_mod3narr','coh_mod3exp','coh_mod3per','coh_mod3all'; GAMET: 'gamet_cws1'. Legacy keys for RB mod3 (non-v2) are mapped to their v2 equivalents internally.
- data
A data.frame produced by
import_rb,import_coh, orimport_gamet, with anIDcolumn and the expected feature columns.
Value
A list of pre-processed data frames, one per sub-model. For models with no
varlists (e.g., 'rb_mod1','coh_mod1'), returns six copies of the input data.
For 'gamet_cws1', returns two copies (CWS/CIWS). For 1-part/3-part models, returns
a list of length 1/3 with centered & scaled features plus the ID column.
Details
**Offline/examples:** Examples use a built-in 'example' model seeded in a temporary
directory via writeAlizer::wa_seed_example_models("example"), so no downloads
are attempted and checks stay fast.
Examples
# Minimal, offline example using the built-in 'example' model (no downloads)
rb_path <- system.file("extdata", "sample_rb.csv", package = "writeAlizer")
rb <- import_rb(rb_path)
pp <- preprocess("example", rb)
length(pp); lapply(pp, nrow)
#> [1] 1
#> [[1]]
#> [1] 3
#>