Evaluating cross-validation performance of a TPLS_cv model at compvec and threshvec
evalTuningParam(
TPLScvmdl,
type = c("Pearson", "negMSE", "ACC", "AUC", "LLbinary", "Spearman"),
X,
Y,
compvec,
threshvec,
subfold = NULL
)TPLS_cv model created from TPLS_cv
CV performance metric type. One of LLbinary, negMSE, Pearson, Spearman, AUC, ACC.
The same X as used in TPLScvmdl.
The SAME Y as used in TPLScvmdl.
Vector of number of components to test in cross-validation.
Vector of threshold level (0 ~ 1) to test in cross-validation.
(Optional) vector of subdivision within testing fold to calculate performance. For example scan run division within subject.
A evalTuningParam object that contains the following attributes.
type: Cross validation performance measure type, as specified in the input
threshval: Same as the input threshvec
compval: Same as the input compvec
perfmat: Performance measure 3D matrix: length(compvec)-by-length(threshvec)-by-numfold
perf_best: Best CV performance out of all combinations of compvec and threshvec
compval_best: Number of components that gave the best performance (i.e., perf_best)
threshval_best: Threshold level that gave the best performance (i.e., perf_best)
perf_1se : Performance of the most parsimonious model (least number of coefficients) that is within 1 standard error of perf_best.
compval_1se : Number of components that gave perf_1se
threshval_1se : Threshold level that gave perf_1se
best_at_threshold : a 3-column matrix; first column is max performance at threshold, second column is threshold values, third column is number of components for the best model at threshold