Score new observations with a fitted ackwards model

Applies a fitted bass-ackwards model to new data, for example the held-out test split of a cross-validation design. A factor is a summary variable standing in for a group of items that move together, and a factor score is each person's estimated standing on one. This function gives factor scores at every level without retraining. It is the idiomatic predict() front door to the same machinery as augment.ackwards(). The call predict(object, newdata) returns exactly augment(object, data = newdata, append = FALSE), a data frame holding only the .m{k}f{j} score columns, one row per row of newdata.

Usage

# S3 method for class 'ackwards'
predict(object, newdata, scaling = c("fit", "sample"), ...)

Arguments

object: An ackwards object.
newdata: A data frame or numeric matrix with the same variables (columns) used to fit object. Required. To retrieve scores stored at fit time, use augment(object) instead.
scaling: Which item means/SDs standardize newdata: "fit" (default, the training moments) or "sample" (newdata's own moments). See augment.ackwards().
...: Ignored.

Value

A data frame of factor scores (columns .m{k}f{j}, one per factor per level), with one row per row of newdata in the original order.

Details

Under the default scaling = "fit", newdata is standardized by the fit-time item means/SDs stored in the object before the stored weight matrices are applied. The new scores then land on the same metric as the training solution. An observation's score does not depend on which other observations share its split, and train and test scores are directly comparable. See augment.ackwards() (section Scoring new observations) for the full semantics, the scaling = "sample" alternative, and the non-Pearson-basis caveat.

newdata must contain the variables the model was fit on. They are matched by column name, and extra columns are ignored. A bare unnamed matrix is matched positionally. Rows with missing items produce NA scores (scoring does not impute).

Examples

# Cross-validation: fit on a training split, score the test split
train <- sim16[1:500, ]
test <- sim16[501:1000, ]
x <- ackwards(train, k_max = 5)
test_scores <- predict(x, test)
head(test_scores)
#>        .m1f1       .m2f1      .m2f2       .m3f1       .m3f2      .m3f3
#> 1  0.9866825  1.10925975  0.2651224  0.22302480  0.83381091  0.7455003
#> 2 -1.3121549  0.41877448 -2.3452698 -2.31934440 -0.09306001  0.5863426
#> 3  1.0695420 -0.96685053  2.5697581  2.66347846 -0.91744168 -0.2973669
#> 4  0.4676516 -0.17389635  0.8617838  1.01558023 -0.99973200  0.8581230
#> 5  0.5951633  0.47949881  0.3594096  0.09180377  1.93864339 -1.3480924
#> 6  1.3390624  0.01471201  1.9282385  1.84818068  0.62412605 -0.5416109
#>         .m4f1      .m4f2       .m4f3       .m4f4      .m5f1      .m5f2
#> 1  0.70928336  0.8094370  0.45520440 -0.05611942  0.7250445  0.8034631
#> 2 -0.08628128  0.6373020 -1.40074580 -1.85689000 -0.1442605  0.6544344
#> 3 -0.78400610 -0.4193713  1.15211798  2.47154121 -0.7642160 -0.4206876
#> 4 -0.76118123  0.7194296 -0.07002827  1.34363823 -0.7490204  0.7191977
#> 5  1.29627226 -1.0086779  1.62903644 -1.17221196  1.2950204 -1.0113150
#> 6  0.25414070 -0.3714401  1.87301298  0.86141204  0.2659999 -0.3729043
#>        .m5f3       .m5f4      .m5f5
#> 1  0.4524271 -0.07636977  0.3129451
#> 2 -1.4086669 -1.75811718 -1.6661291
#> 3  1.1651161  2.42899090  0.7111726
#> 4 -0.0638014  1.32385148  0.3743907
#> 5  1.6229873 -1.17404983 -0.1157916
#> 6  1.8770026  0.83487823  0.3350342

# Identical to the augment() spelling:
identical(test_scores, augment(x, data = test, append = FALSE))
#> [1] TRUE