Realised-control report for a continuous design

Returns the same list shape as match_report() (descriptives + comparisons), but the comparisons describe a continuous predictor: its realised span and, for each control, the Pearson correlation with the predictor (near zero when the control is held constant). Mirrors match_report_continuous in validation.py.

Usage

match_report_continuous(
  stimuli,
  predictor,
  controls,
  schema,
  audit = attr(stimuli, "audit")
)

Arguments

stimuli

A stimuli data frame (a single "continuous" group).

predictor

The spanned predictor dimension.

controls

Character vector of control dimensions.

schema

The parsed global schema.

audit

The selector's audit record, by default the "audit" attribute select_continuous_stimuli() attaches to its result. Its control_windows give each control's window and pool SD. Without them, a control's sd_ratio and window are NA.

Value

A list with descriptives and comparisons data frames.

Details

sd is each dimension's realised SD. For the predictor, sd_ratio sets it against the SD of as many values spaced evenly over the same range, (max - min) * sqrt(n * (n + 1) / 12) / (n - 1) with the n - 1 that sd uses, so an even spread has a ratio of 1 whatever its size and a selection that follows a skewed pool a ratio below 1. That benchmark tends to (max - min) / sqrt(12), the SD of a uniform distribution over the range, as n grows. For a control, sd_ratio sets the realised SD against the SD of the pool the selector drew from, and window_low and window_high give the window the selector banded it to, before any relaxation. Both come from audit, the selector's record.