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dev/articles/latent.html

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dev/articles/latent.md

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@@ -314,7 +314,7 @@ print(time_lat)
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```
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user system elapsed
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1.062 0.336 1.006
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1.076 0.357 1.033
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The message telling that `<refmodel>$dis` consists of only `NA`s will
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not concern us here because we will only focus on response-scale
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```
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user system elapsed
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4.107 0.348 4.056
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4.208 0.366 4.173
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``` r
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( gg_trad <- plot(vs_trad, stats = "gmpd", deltas = TRUE) )

dev/articles/projpred.html

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dev/articles/projpred.md

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@@ -354,12 +354,12 @@ Although not strictly necessary here, this is helpful in practice
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because often,
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[`cv_varsel()`](https://mc-stan.org/projpred/dev/reference/cv_varsel.md)
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needs to be re-run multiple times in order to try out different argument
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settings. We also illustrate how **projpred**’s CV (i.e., the CV
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comprising search and performance evaluation, after refitting the
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reference model \\K\\ times) can be parallelized, even though this is of
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little use here (we have only `K = 2` folds and the fold-wise searches
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and performance evaluations are quite fast, so the parallelization
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overhead eats up any runtime improvements). Note that for this final
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settings. We also show how **projpred**’s CV (i.e., the CV comprising
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search and performance evaluation, after refitting the reference model
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\\K\\ times) can be parallelized (though we disable it here for vignette
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building). In practice, parallelization is of little use with only
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`K = 2` folds and fast fold-wise operations, as the parallelization
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overhead eats up any runtime improvements. Note that for this final
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[`cv_varsel()`](https://mc-stan.org/projpred/dev/reference/cv_varsel.md)
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run, we cannot make use of
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[`cv_varsel.vsel()`](https://mc-stan.org/projpred/dev/reference/cv_varsel.md)
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###
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)
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# For running projpred's CV in parallel (see cv_varsel()'s argument `parallel`):
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doParallel::registerDoParallel(ncores)
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# Note: Parallel processing is disabled during package building to avoid issues
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use_parallel <- FALSE # Set to TRUE for actual parallel processing
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if (use_parallel) {
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doParallel::registerDoParallel(ncores)
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}
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# Final cv_varsel() run:
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cvvs <- cv_varsel(
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refm_obj,
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nclusters_pred = 20,
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###
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nterms_max = 9,
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parallel = TRUE,
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parallel = use_parallel,
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### In interactive use, we recommend not to deactivate the verbose mode:
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verbose = 0
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###
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)
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# Tear down the CV parallelization setup:
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doParallel::stopImplicitCluster()
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foreach::registerDoSEQ()
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if (use_parallel) {
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doParallel::stopImplicitCluster()
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foreach::registerDoSEQ()
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}
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```
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#### Predictive performance plot from final `cv_varsel()` run
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5 X6 0.0 -0.208 -0.27 -0.151
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6 X3 0.0 -0.214 -0.26 -0.169
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7 X8 0.5 -0.003 -0.02 0.016
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8 X11 0.0 -0.010 -0.03 0.009
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9 X7 0.0 0.003 -0.01 0.021
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8 X11 0.0 -0.010 -0.03 0.008
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9 X7 0.0 0.003 -0.01 0.020
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Reference model performance evaluation summary with `deltas = TRUE`:
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mlpd mlpd.lower mlpd.upper
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$ submodels :'data.frame': 10 obs. of 4 variables:
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..$ size : num [1:10] 0 1 2 3 4 5 6 7 8 9
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..$ mlpd : num [1:10] -0.976 -0.786 -0.555 -0.553 -0.519 ...
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..$ mlpd.lower: num [1:10] -1.049 -0.867 -0.623 -0.637 -0.609 ...
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..$ mlpd.lower: num [1:10] -1.049 -0.868 -0.623 -0.637 -0.609 ...
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..$ mlpd.upper: num [1:10] -0.903 -0.704 -0.487 -0.469 -0.429 ...
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$ reference_model: Named num [1:3] 0 0 0
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..- attr(*, "names")= chr [1:3] "mlpd" "mlpd.lower" "mlpd.upper"
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dev/pkgdown.yml

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articles:
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latent: latent.html
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projpred: projpred.html
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last_built: 2025-11-11T22:39Z
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last_built: 2025-11-19T13:48Z
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urls:
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reference: https://mc-stan.org/projpred/reference
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article: https://mc-stan.org/projpred/articles

dev/search.json

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