Predicted difficulty and predictive SD for new items

Usage

# S3 method for class 'cs_predictor'
predict(object, features, family = NULL, item = rownames(features), ...)

Arguments

object: A `cs_predictor`.
features: Feature matrix for new items (same columns as training).
family: Family per new item (families not seen in training get the larger unseen-family SD).
item: Optional item ids (default: rownames of `features`).
...: Unused.

Value

Data frame: `item`, `family`, `mean`, `sd`, `sd_shared`, `family_seen`. `sd` is each item's total predictive SD; `sd_shared` is the part of it shared by all items of the same family (for an unseen family, mostly its unknown family effect). [cs_check()] uses it so that a family is not flagged merely for the shared error its SD already allows.

Examples

sim <- cs_simulate(n_train = 200, n_new = 60, seed = 1)
it <- sim$items; tr <- it$set == "train"
pr <- cs_predictor(it$b_legacy[tr], sim$features[tr, ], it$family[tr], seed = 1)
pred <- predict(pr, sim$features[!tr, ], it$family[!tr])
head(pred)
#>        item family       mean        sd sd_shared family_seen
#> G0201 G0201    F01 -0.9760557 0.6186199         0        TRUE
#> G0202 G0202    F07  0.6804164 0.6186199         0        TRUE
#> G0203 G0203    F05 -0.5732907 0.6186199         0        TRUE
#> G0204 G0204    F09 -0.9805451 0.6186199         0        TRUE
#> G0205 G0205    F03 -0.2853529 0.6186199         0        TRUE
#> G0206 G0206    F05 -0.6911932 0.6186199         0        TRUE