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Function to plot the Rate-of-Change sequence

Source: R/plot_roc.R
plot_roc.Rd

Plot Rate-of-Change sequence with a error estimate and trend and/or peak-points in present.

Usage

plot_roc(
  data_source,
  age_threshold = NULL,
  roc_threshold = NULL,
  peaks = FALSE,
  trend = NULL
)

Arguments

data_source

Data.frame. Output of estimate_roc function

age_threshold

Numeric. Cut-off value used as maximum age.

roc_threshold

Numeric Cut-off value used as maximum RoC value.

peaks

Logical. If peak-points are presented in the dataset and peaks == TRUE, then peak points will be displayed

trend

If peak-points are presented in the dataset and peaks == TRUE, then one of the three method can be used to visualise the process of peak detection:

  • "threshold" - Each point in the RoC sequence is compared to a median of all RoC scores from the whole sequence (i.e. threshold value). The ROC value for a point is considered significant if the 95th quantile of the RoC scores from all calculations is higher than the threshold value.

  • "trend_linear" - A linear model is fitted between the RoC values and their ages. Differences between the model and each point are calculated (residuals). The standard deviation (SD) is calculated from all the residuals. A peak is considered significant if it is 2 SD higher than the model.

  • "trend_non_linear" - A conservative generalised additive model (GAM) is fitted through the RoC scores and their ages (GAM = RoC ~ s(age, k = 3) using the mgcv package (Wood, 2011). The distance between each point and the fitted value is calculated (residuals). The standard deviation (SD) is calculated from all the residuals. A peak is considered significant if it is 2 SD higher than the model.

Examples

if (FALSE) {

example_data <- RRatepol::example_data

# example 1
sequence_01 <-
  estimate_roc(
    data_source_community = example_data$pollen_data[[1]],
    data_source_age = example_data$sample_age[[1]],
    age_uncertainty = FALSE,
    smooth_method = "shep",
    working_units = "MW",
    rand = 1e3,
    treads = TRUE,
    dissimilarity_coefficient = "chisq"
  )

plot_roc(
  sequence_01,
  age_threshold = 8e3,
  roc_threshold = 1
)

# example 2
sequence_02 <-
  estimate_roc(
    data_source_community = example_data$pollen_data[[2]],
    data_source_age = example_data$sample_age[[2]],
    age_uncertainty = FALSE,
    smooth_method = "shep",
    working_units = "MW",
    rand = 1e3,
    treads = TRUE,
    dissimilarity_coefficient = "chisq"
  )

sequence_02_peak <-
  detect_peak_points(sequence_01, sel_method = "trend_non_linear")

plot_roc(
  sequence_02_peak,
  age_threshold = 8e3,
  roc_threshold = 2,
  peaks = TRUE,
  trend = "trend_non_linear"
)
}