Implements the Base Index where coefficients are set equal to economic weights. This is a simple, non-optimized approach that serves as a baseline comparison.
Unlike the Smith-Hazel index which requires matrix inversion, the Base Index is computationally trivial and robust when covariance estimates are unreliable.
Usage
base_index(
pmat,
gmat,
wmat,
wcol = 1,
selection_intensity = 2.063,
compare_to_lpsi = TRUE,
GAY = NULL
)Arguments
- pmat
Phenotypic variance-covariance matrix (n_traits x n_traits)
- gmat
Genotypic variance-covariance matrix (n_traits x n_traits)
- wmat
Economic weights matrix (n_traits x k), or vector
- wcol
Weight column to use if wmat has multiple columns (default: 1)
- selection_intensity
Selection intensity constant (default: 2.063)
- compare_to_lpsi
Logical. If TRUE, compares Base Index efficiency to optimal LPSI (default: TRUE)
- GAY
Optional. Genetic advance of comparative trait for PRE calculation
Value
List with:
summary- Data frame with coefficients and metricsb- Vector of Base Index coefficients (equal to w)w- Named vector of economic weightsDelta_G- Named vector of expected genetic gains per traitlpsi_comparison- Optional comparison with Smith-Hazel LPSI
Details
Mathematical Formulation:
Index coefficients: \(b = w\)
The Base Index is appropriate when: - Covariance estimates are unreliable - Computational simplicity is required - A baseline for comparison is needed
Examples
if (FALSE) { # \dontrun{
gmat <- gen_varcov(seldata[, 3:9], seldata[, 2], seldata[, 1])
pmat <- phen_varcov(seldata[, 3:9], seldata[, 2], seldata[, 1])
weights <- c(10, 8, 6, 4, 2, 1, 1)
result <- base_index(pmat, gmat, weights, compare_to_lpsi = TRUE)
print(result)
} # }