GLmom (version 1.3.1)

magev.ksensplot: K Sensitivity Plot for MAGEV

Description

Plots return level estimates, standard errors, and first-order differences across different numbers of candidate submodels K. This helps identify stable regions where estimates converge and select an optimal K value.

Usage

magev.ksensplot(
  data = NULL,
  q.cut = 0.6,
  mink = 4,
  maxk = 20,
  quant = c(0.99, 0.995)
)

Value

The optimal K value (integer) selected by the algorithm.

Arguments

data

A numeric vector of data to be fitted (e.g., annual maxima).

q.cut

Quantile cutoff for determining stability (default 0.6).

mink

Minimum number of candidate submodels to test (default 4).

maxk

Maximum number of candidate submodels to test (default 20).

quant

The probabilities for high quantile estimation. Default is c(0.99, 0.995).

Author

Yonggwan Shin, Seokkap Ko, Jihong Park, Yire Shin, Jeong-Soo Park

Details

The function computes MAGEV estimates for K ranging from mink to maxk. For each K, it calculates:

• Return level estimates (black points)

• Normalized standard errors (blue line)

• First-order differences (red line with triangles)

The optimal K is selected as the smallest value where both the normalized standard error and first-order difference are below their respective q.cut quantile cutoffs. The selected K is indicated by a purple vertical line.

References

Shin, Y., Shin, Y., & Park, J. S. (2026). Model averaging with mixed criteria for estimating high quantiles of extreme values: Application to heavy rainfall. Stochastic Environmental Research and Risk Assessment, 40(2), 47. tools:::Rd_expr_doi("10.1007/s00477-025-03167-x")

See Also

ma.gev for the main model averaging function.

Examples

# \donttest{
data(streamflow)
optimal_k <- magev.ksensplot(streamflow$r1)
print(optimal_k)
# }