get_mean_var_shifts_model_selection | R Documentation |
Performs model selection to estimate the locations and magnitudes of evolutionary shifts in optimal trait values (mean) and diffusion variance under an Ornstein-Uhlenbeck (OU) process. This function searches across user-defined grids of shrinkage parameters for both types of shifts, uses cross-validation for selecting lambda1
, and applies backward correction to refine top candidate models.
get_mean_var_shifts_model_selection(
Y,
tree,
alpha,
t = 0.01,
lambda1_list = NULL,
lambda2_list = exp(1:10 * 0.4 - 6),
criterion = "BIC",
max.steps = 300,
nfolds = 8,
top_k = 10,
measurement_error = FALSE,
lambda.type = "lambda.1se",
max.num.shifts = Inf,
verbose = TRUE
)
Y |
A numeric vector of trait values for the species at the tips of the phylogenetic tree. |
tree |
A phylogenetic tree of class |
alpha |
A non-negative numeric value representing the selection strength in the OU process. |
t |
Step size for iterative optimization. Default is 0.01. |
lambda1_list |
A numeric vector of candidate |
lambda2_list |
A numeric vector of candidate |
criterion |
Model selection criterion to optimize. Options include |
max.steps |
Maximum number of optimization steps. Default is 300. |
nfolds |
Number of cross-validation folds for tuning |
top_k |
Number of top candidate models (ranked by criterion) to further refine using backward correction. Default is 10. |
measurement_error |
Logical. If |
lambda.type |
A character string specifying the cross-validation rule used to select |
max.num.shifts |
An integer specifying the maximum number of allowed shifts (combined across mean and variance). Default is |
verbose |
Logical. If |
A list containing:
best_model |
The final selected OU model object, with estimated shifts and parameters. |
score_summary |
A data frame summarizing the model selection results, including pre- and post-correction scores and shift locations. |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.