Cognitive composite (z-mean or PCA1)

Usage

cognitive_score(
  data,
  col_map = list(),
  na_action = c("keep", "omit", "error"),
  missing_prop_max = 0.2,
  method = c("z_mean", "pca1"),
  prefix = "cog",
  verbose = TRUE
)

Arguments

data: Data frame containing questionnaire item columns.
col_map: Named list with tasks mapping task IDs to column names (>= 2 tasks required).
na_action: How to handle rows with missing items: keep, omit, or error.
missing_prop_max: Maximum allowed proportion of missing items per row before the score is set to NA.
method: Aggregation method: z_mean (average of z-scores) or pca1 (first PC).
prefix: Prefix for output column names.
verbose: Logical; if TRUE, emits informational messages about column resolution and scoring progress via hm_inform().

Value

A tibble of score columns only: {prefix}_z_mean or {prefix}_pca1. Input columns are not included.

Examples

df <- data.frame(task_a = c(1, 2), task_b = c(2, 3), task_c = c(3, 4))
cm <- list(tasks = list(
  task_a = "task_a",
  task_b = "task_b",
  task_c = "task_c"
))
cognitive_score(df, col_map = cm, method = "z_mean")
#> # A tibble: 2 × 1
#>   cog_z_mean
#>        <dbl>
#> 1     -0.707
#> 2      0.707