AB_permutation_distancing_test

Description

Performs a randomisation test for two phases (A and B) that corrects for dependency. The correcting is done through stepwise resampling the time series while varying the distance between observations. The required distance 0,1,2,3.. is determined based on repeated dependency testing while stepwise increasing the distance. The input x and y values should be equidistant (with NA's included) using insert_NA_and_try_to_shift. The distance per cycles = k-1. k_max should be max 25 If de_A_trend=TRUE, phase A will be first de_A_trended. If de_B_trend=TRUE, phase B will be first de_B_trended. If detrend_x_position = "first" : take predicted value for first valid observation. If detrend_x_position = "center": take predicted value for center observation. If detrend_x_position = "last" : take predicted value for last valid observation. The p-value returned corresponds with the lowest Ljung-Box test (minimal) p-value found. The statistic returned correspond with median chi-square k with p_box larger than alpha_p_box_test or statistic_box smaller than max_statistic_p_box_test (i.e., not dependent).

Usage

AB_permutation_distancing_test(
  x,
  y,
  test_statistic = "*",
  test_statistic_function = "mean",
  reps_max = 2000,
  k_max = NULL,
  alpha_p_box_test = 0.1,
  max_statistic_p_box_test = 2.7,
  no_duplicates = FALSE,
  remove_NA_series_above_k = TRUE,
  de_A_trend = FALSE,
  detrend_A_position = "center",
  de_B_trend = FALSE,
  detrend_B_position = "center",
  show_plot = FALSE,
  show_plot_header = ""
)

Arguments

Value

List with the permutation distancing test results: de_A_trend setting in call, detrend_A_position in call, de_B_trend setting in call, detrend_B_position in call, ar1 = vector of computed ar1 values per distancing step (0,1,2, etc), p_box = vector of computed box-test p-values per distancing step, statistic_box = vector of box-test statistics per distancing step, observed_test_statistic = computed overall AB test statistic (before distancing), effect_size_overall = computed overall effect size (before distancing), p = vector of computed permutation test p-values per distancing step, effect_size vector of computed permutation test effect-sizes per distancing step, p_fitted = vector of lm-fitted line p-values through p_box, k_max = k_max setting in call or computed based on the number of observations, k_selected_based_on_Box_test = selected k values, p_selected_based_on_Box_test = selected p-value, effect_size_selected_based_on_Box_test = selected effect-size values.

Examples

pdt::AB_permutation_distancing_test(
  as.factor(c(rep("A",20), rep("B",20))),
  c(rnorm(20), rnorm(20)+2),
  test_statistic="B-A",
  test_statistic_function="mean",
  reps_max=1000,
  k_max=NULL,
  alpha_p_box_test=0.1,
  max_statistic_p_box_test=2.7,
  no_duplicates=FALSE,
  remove_NA_series_above_k=TRUE,
  de_A_trend=FALSE,
  detrend_A_position="center",
  de_B_trend=FALSE,
  detrend_B_position="center",
  show_plot=FALSE,
  show_plot_header="")

AB_permutation_test

Description

Performs a regular permutations test for two conditions or phases (A and B).

Usage

AB_permutation_test(
  x,
  y,
  test_statistic = "*",
  test_statistic_function = "mean",
  reps_max = 2000,
  no_duplicates = FALSE,
  show_plot = FALSE,
  show_plot_header = ""
)

Arguments

Value

List with the permutation test results: observed_test_statistic = computed test statistic, effect_size = computed effect size (similar to Cohen's d), random_assignments, p_randomization_AB = p value randomization AB test, one_sided_p = one-sided p-value in case of B-A or A-B.

Examples

pdt::AB_permutation_test(
  as.factor(c(rep("A",20), rep("B",20))),
  c(rnorm(20), rnorm(20)+2),
  test_statistic="B-A",
  test_statistic_function="mean",
  reps_max=1000,
  no_duplicates=FALSE,
  show_plot=FALSE,
  show_plot_header="")

AB_permutation_test_plot

Description

Creates a permutation distancing test plot. Several plot options are available, e.g., to show both the observed and detrended lines.

Usage

AB_permutation_test_plot(
  x,
  x_values,
  y,
  test_statistic_function = "mean",
  de_A_trend = FALSE,
  detrend_A_position = "center",
  show_de_A_trended = FALSE,
  de_B_trend = FALSE,
  detrend_B_position = "center",
  show_de_B_trended = FALSE,
  show_plot_header = "",
  xlab = "",
  ylab = "",
  ylim = NULL,
  labels = NULL,
  line_colors = c("blue", "red", "blue", "red", "blue", "cyan"),
  show_legend = TRUE
)

Arguments

Value

NULL.

Examples

pdt::AB_permutation_test_plot(
  as.factor(c(rep("A",20), rep("B",20))),
  1:40,
  c(rnorm(20), rnorm(20)+2),
  test_statistic_function="mean",
  de_A_trend=TRUE,
  detrend_A_position="center",
  show_de_A_trended=TRUE,
  de_B_trend=TRUE,
  detrend_B_position="center",
  show_de_B_trended=TRUE,
  show_plot_header="",
  xlab="",
  ylab="",
  ylim=NULL,
  labels=NULL,
  line_colors=c("blue", "red", "blue", "red", "blue", "cyan"),
  show_legend=TRUE)

detrend_A

Description

Detrends the phase A part of time series y. detrend A is optional and not validated. The mean of the detrended signal will be set to the predicted value based on detrend_A_position: detrend_A_position = "first" : take predicted value for first valid observation detrend_A_position = "center" : take predicted value for center observation detrend_A_position = "last" : take predicted value for last valid observation.

Usage

detrend_A(x, x_values, y, detrend_A_position = "center")

Arguments

Value

List with the trend and the detrended y-values: x_values_A_trend = vector with distance (time markers) between A-detrended signal, y_A_trend = vector with computed A-trend, y_detrended = vector with computed A-detrended y values.

Examples

pdt::detrend_A(as.factor(c(rep("A",20), rep("B",20))), 1:40,
  c(rnorm(20), rnorm(20)+2), detrend_A_position="center")

detrend_B

Description

Detrends the phase B part of time series y. detrend B is optional and not validated. The mean of the detrended signal will be set to the predicted value based on detrend_B_position: detrend_B_position = "first" : take predicted value for first valid observation detrend_B_position = "center" : take predicted value for center observation detrend_B_position = "last" : take predicted value for last valid observation.

Usage

detrend_B(x, x_values, y, detrend_B_position = "center")

Arguments

Value

List with the trend and the detrended y values: x_values_B_trend = vector with distance (time markers) between B-detrended signal, y_B_trend = vector with computed B-trend, y_detrended = vector with computed B-detrended y values.

Examples

pdt::detrend_B(as.factor(c(rep("A",20), rep("B",20))), 1:40,
  c(rnorm(20), rnorm(20)+2), detrend_B_position="center")

do_remove_NA_series_above_k

Description

Remove series of more than k succeeding NA's in x, y, and x_values. This function is recommended before performing a permutation distancing test.

Usage

do_remove_NA_series_above_k(x, y, k, x_values = NULL)

Arguments

Value

List with the modified x, y, x_values: x = factor vector with conditions (e.g., "A" and "B"). y = vector with observed values. x_values = vector with distance (time markers) between observations x,y.

Examples

pdt::do_remove_NA_series_above_k(as.factor(c("A","A","A","B","B","B")),
  c(1.1,NA,NA,7.1,8.3,9.8), 1, c(1,2,4,5,6,8))

insert_NA_and_try_to_shift

Description

Makes the input time series equidistant. This is recommended before performing a permutation distancing test. This function first inserts NA's for missing x_values, then it tries to shift double value to previous or next NA's, finally it aggregates the remaining identical x_values.

Usage

insert_NA_and_try_to_shift(x, x_values, y)

Arguments

Value

List with the modified x, x_values, y: x = factor vector with conditions or phases (e.g., "A" and "B"). x_values = (optional) vector with distance (time markers) between observations. y = vector with observed values.

Examples

pdt::insert_NA_and_try_to_shift(as.factor(c("A","A","A","B","B","B")),
  c(1,2,4,5,6,8), c(1.1,3.2,5.3,7.1,8.3,9.8))