Target Population of Environments (TPE) Weights

Converts an env_risk_profile() into per-environment-type weights summing to one, so that selection indices, weighted GxE models and testing network decisions can be made proportional to how often each environment type actually occurs – rather than how often it happened to be trialled.

Usage

tpe_weights(
  risk,
  stage = NULL,
  var = NULL,
  by = c("class", "site"),
  measure = c("freq", "days"),
  drop.benign = FALSE,
  verbose = TRUE
)

Arguments

risk

An "env_risk" object from env_risk_profile().

stage

character. Restrict to one stage (e.g. "R1"). If NULL, weights are computed across all stages jointly.

var

character. Restrict to one variable. If NULL, classes from all variables are pooled, which is only sensible if they are disjoint.

by

character. "class" (default) weights environment TYPES; "site" weights SITES by how representative they are of the TPE.

measure

character. "freq" (probability of occurrence, default) or "days" (expected days, which weights by intensity).

drop.benign

boolean. Exclude classes named safe, optimal, none, non_limiting. Default FALSE.

verbose

boolean. Print the weight table.

Value

A data.frame with the grouping column, the raw measure, and weight summing to 1. Attribute "measure" records which measure was used.

Details

What "site weight" means. With by = "site" the weight is the share of the TPE's total stress-occurrence mass that a site contributes. A high weight means the site frequently expresses the stresses of interest, NOT that it is a good discriminator of genotypes – those are different criteria, and a site can be highly representative yet have poor heritability. Combine these weights with per-site repeatability before cutting a testing location.

Weights are as good as the record. They inherit every sampling limitation of the underlying profile, and they assume the historical climatology still holds.

See also

env_risk_profile

Examples

if (FALSE) { # \dontrun{
## Requires an observed multi-year weather series (wth).
rp <- env_risk_profile(wth, planting = "10-15", crop = "maize")

## How much of the TPE is each drought class at flowering?
tpe_weights(rp, stage = "R1", var = "PETP")

## Which sites carry the most of the TPE's stress mass?
tpe_weights(rp, var = "PETP", by = "site", drop.benign = TRUE)
} # }