cv_choose | R Documentation |
Extracting the CV choices of SPCR parameters.
cv_choose(scor, scor_lwr, scor_upr, K, fit_measure)
scor |
|
scor_lwr |
|
scor_upr |
|
K |
numeric vector of length 1 storing the number of folds for the K-fold cross-validation procedure |
fit_measure |
character vector of length 1 indicating the type of fit measure to be used in the to cross-validation procedure |
Given a matrix of npcs \times nthrs
, returns the best choice based on the type of fit measure (best overall and 1se rule versions.)
This function returns as solutions:
default
: the best choice based on the given fit measure (e.g. highest likelihood ratio test statistic, lowest BIC)
oneSE
: the solution that defined the most parsimonious model within 1 standard error from the best one.
When both the number of components and the threshold parameter are cross-validated, the 1-standard error rule finds the candidate alternative solutions using the lowest number of PCs and having the best fit-measure.
This decision is guided by the desire to counterbalance the tendency of GSPCR of selecting the highest number of components available when using cross-validation.
A list of two numeric vectors:
default
: numeric vector of length 2 that reports the coordinates in scor
defining the default solution.
oneSE
: numeric vector of length 2 that reports the coordinates for scor
defining the solution based on the one standard error rule
Edoardo Costantini, 2023
# Score matrices
scor <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 3, ncol = 2)
scor_lwr <- matrix(c(1, 2, 3, 4, 5, 6) - 1.5, nrow = 3, ncol = 2)
scor_upr <- matrix(c(1, 2, 3, 4, 5, 6) + 1.5, nrow = 3, ncol = 2)
# Number of folds
K <- 10
# Type of fit_measure
fit_measure <- "F"
# Use the function
cv_choose(scor, scor_lwr, scor_upr, K, fit_measure)
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