sfpl_select {SFPL} | R Documentation |
Model selection for SFPL
Description
This function selects the "best" fitted SFPL model using either the AIC or the BIC, see Hermes et al., (2024).
Usage
sfpl_select(beta_est, x, y, ls_vec, lf_vec)
Arguments
beta_est |
A list of length ls_vec |
x |
A |
y |
A list consisting of |
ls_vec |
Vector containing shrinkage parameters. |
lf_vec |
Vector containing fusion penalty parameters. |
Value
model_aic |
A |
model_bic |
A |
Author(s)
Sjoerd Hermes
Maintainer: Sjoerd Hermes sjoerd.hermes@wur.nl
References
1. Hermes, S., van Heerwaarden, J., and Behrouzi, P. (2024). Joint Learning from Heterogeneous Rank Data. arXiv preprint, arXiv:2407.10846
Examples
# we first obtain the rankings and object variables
data(ghana)
y <- list(ghana[[1]], ghana[[2]])
x <- ghana[[3]]
# our next step consists of creating two vectors for the penalty parameters
ls_vec <- lf_vec <- c(0, 0.25)
# we choose epsilon to be small: 10^(-5), as we did in Hermes et al., (2024)
# now we can fit our model
epsilon <- 10^(-5)
verbose <- FALSE
result <- sfpl_approx(x, y, ls_vec, lf_vec, epsilon, verbose)
# now we select the best models using our model selection function
sfpl_select(result, x, y, ls_vec, lf_vec)