beezdemand (version 0.2.0)

check_unsystematic_cp: Check for Unsystematic Patterns in Cross-Price Data

Description

Analyzes whether consumption data shows systematic trends or unsystematic patterns ("bounces") with respect to price. Includes detection of zero-value reversal/return sequences and allows flexible output based on the level of detail requested. See Rzeszutek et al. (in press) for more details.

Usage

check_unsystematic_cp(
  data,
  delta_threshold = 0.025,
  bounce_down_threshold = 0.1,
  bounce_up_threshold = 0.1,
  bounce_none_threshold = 0.1,
  rev_zeroes = 2,
  ret_nums = 2,
  expected_down = FALSE,
  verbose = FALSE,
  detailed = FALSE
)

Value

A data frame of class cp_unsystematic with core results:

delta_direction

Character: 'down', 'up', or 'none'.

bounce_direction

Character: 'up', 'down', 'significant', or 'none'.

bounce_any

Logical. TRUE if any bounce pattern detected.

bounce_above

Integer. Number of upward changes meeting threshold.

bounce_below

Integer. Number of downward changes meeting threshold.

reversals

Integer. Detected reversals from 0 to non-0.

returns

Integer. Detected returns from non-0 to 0.

If detailed = TRUE, returns additional columns:

delta_down

Logical. Significant downward trend.

delta_up

Logical. Significant upward trend.

delta_none

Logical. No significant trend.

bounce_up

Logical. Significant bounce up in a downward trend.

bounce_down

Logical. Significant bounce down in an upward trend.

bounce_none

Logical. Significant bounces in no-trend data.

Arguments

data

A data frame with columns 'x' and 'y', where 'x' is price and 'y' is consumption.

delta_threshold

Numeric. Threshold for detecting log-scale trends (default 0.025).

bounce_down_threshold

Numeric. Minimum downward bounce proportion to count as significant in upward trends.

bounce_up_threshold

Numeric. Minimum upward bounce proportion to count as significant in downward trends.

bounce_none_threshold

Numeric. Minimum bounce proportion to count as significant in no-trend cases.

rev_zeroes

Integer. Length of zero sequences to detect reversals (default 2).

ret_nums

Integer. Length of non-zero sequences to detect returns (default 2).

expected_down

Logical. If TRUE, suppress reversal detection.

verbose

Logical. If TRUE, print intermediate values (default FALSE).

detailed

Logical. If TRUE, return additional columns including all trend/bounce flags.

Examples

x_seq <- 10^(seq(-2, 2, length.out = 10))
pattern <- data.frame(x = x_seq, y = c(10, 5, 10, 9, 10, 13, 10, 10, 7, 9))
check_unsystematic_cp(pattern)