Functional suppression profiles of disease control treatments

Computes disease suppression trajectories relative to a reference treatment, estimates functional distances among suppression curves, clusters treatments according to their temporal suppression profiles, and summarizes each profile using functional suppression metrics.

Usage

functional_suppression_profiles(
  data,
  reference,
  time = "time",
  response = "severity",
  treatment = "treatment",
  threshold = 5,
  metrics = c("protected_area", "max_suppression", "persistence", "centroid"),
  dist_method = "euclidean",
  hclust_method = "average",
  k = NULL,
  cut_height = NULL,
  ...
)

Arguments

data

A data frame or tibble containing the disease progress data.

reference

Character string specifying the reference treatment name.

time

Character string specifying the time column. Default is "time".

response

Character string specifying the response column. Default is "severity".

treatment

Character string specifying the treatment column. Default is "treatment".

threshold

Numeric threshold for the persistence metric. Default is 5.

metrics

Character vector of metrics to include in the ranking. Default is c("protected_area", "max_suppression", "persistence", "centroid").

dist_method

Character string specifying the distance method. Default is "euclidean".

hclust_method

Character string specifying the hierarchical clustering method. Default is "average".

k

Integer specifying the number of clusters. If NULL and cut_height is NULL, defaults to 4 if there are 6 or more treatments, else min(3, n_treatments).

cut_height

Numeric specifying the cut height for clustering. If provided, overrides k.

...

Additional arguments passed to functional_contrast.

Value

A list of class "functional_suppression_profiles" containing:

• call: The matched call.

• reference: The reference treatment name.

• contrast: The contrast data from functional_contrast.

• dsp_curves: The fitted curves from functional_curves.

• distances: The distances object from functional_distances.

• hclust: The hierarchical clustering object.

• clusters: A tibble with treatment cluster assignments.

• summary: A tibble with functional summary metrics.

• ranking: A tibble with metric rankings.

• cluster_summary: A tibble combining metrics, ranks, and cluster assignments.

• parameters: A list of input parameters.

Details

A Functional Suppression Profile (FSP) is the temporal trajectory of disease suppression produced by a treatment relative to an untreated or reference control. The workflow includes contrasting treatments against a reference, fitting functional curves, calculating distances, clustering, and summarizing the suppression using metrics like protected area, maximum suppression, and persistence.

See also

functional_contrast, functional_curves, functional_distances, functional_summary, rank_dsp, plot_dendrogram

Examples

if (FALSE) { # \dontrun{
sim_dat <- tibble::tibble(
  treatment = rep(c("Control", "A", "B", "C"), each = 6),
  time = rep(seq(0, 25, by = 5), times = 4),
  severity = c(
    c(5, 10, 20, 35, 50, 65),
    c(3, 5, 10, 18, 30, 40),
    c(4, 6, 12, 22, 35, 45),
    c(2, 4, 8, 14, 22, 32)
  )
)
fsp <- functional_suppression_profiles(
  data = sim_dat,
  reference = "Control",
  time = "time",
  response = "severity",
  treatment = "treatment",
  threshold = 5,
  k = 2
)

fsp
summary(fsp)
plot(fsp, type = "dendrogram")
plot(fsp, type = "profiles")
plot(fsp, type = "heatmap")
plot(fsp, type = "rank")
} # }