submit to CRAN; version: 1.0.0

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
 Version: 1.0.0
-Date: 2022-10-04 17:30:05 UTC
-SHA: e7033589ef72e3373eadf95689984b6948312b52
+Date: 2022-10-06 23:09:03 UTC
+SHA: 34914e140c76557859f50ae3c6f5ce9f94ee90d9

vignettes/missoNet.Rmd

@@ -402,7 +402,7 @@ plot(cvfit.BM)
 knitr::include_graphics(system.file("extdata", "cvfitBM.png", package = "missoNet"))
 ```
 
-Now we can pick better ranges for $\lambda_B$ and $\lambda_\Theta$ for a further fine-grid search according to the figure above. We observed that the current optimal $\lambda$ = $({\lambda_B}_\text{min}^\text{coarse}, {\lambda_\Theta}_\text{min}^\text{coarse})$ achieving the minimum cross-validated error (white box) is about (0.24, 0.62), while $\text{max}(\lambda_B)$ and $\text{max}(\lambda_\Theta)$ are around 2.66 (appropriate) and 29.94 (too large), respectively. Therefore, we decide to shrink `'lamTheta.scale.factor'` from `3` to `0.3` (= 3/10) as we believe 2.99 (= 29.94/10) is a more appropriate upper boundary for $\lambda_\Theta$. `'lamBeta.scale.factor'` can remain at `3` so that $\text{max}(\lambda_B)$ will still be 2.66. For the same reason, the new $\text{min}(\lambda_B)$ and $\text{min}(\lambda_\Theta)$ are determined to be 0.027 (= $2.66*0.01$) and 0.030 (= $2.99*0.01$) respectively by setting `'lamBeta.min.ratio' = 0.01` and `'lamTheta.min.ratio' = 0.01`.
+Now we can pick more reasonable ranges for $\lambda_B$ and $\lambda_\Theta$ for a further fine-grid search according to the figure above. We observed that the current optimal $\lambda$ = $({\lambda_B}_\text{min}^\text{coarse}, {\lambda_\Theta}_\text{min}^\text{coarse})$ achieving the minimum cross-validated error (white box) is about (0.24, 0.62), while $\text{max}(\lambda_B)$ and $\text{max}(\lambda_\Theta)$ are around 2.66 (appropriate) and 29.94 (too large), respectively. Therefore, we decide to shrink `'lamTheta.scale.factor'` from `3` to `0.3` (= 3/10) as we believe 2.99 (= 29.94/10) is a more appropriate upper boundary for $\lambda_\Theta$. `'lamBeta.scale.factor'` can remain at `3` so that $\text{max}(\lambda_B)$ will still be 2.66. For the same reason, the new $\text{min}(\lambda_B)$ and $\text{min}(\lambda_\Theta)$ are determined to be 0.027 (= $2.66*0.01$) and 0.030 (= $2.99*0.01$) respectively by setting `'lamBeta.min.ratio' = 0.01` and `'lamTheta.min.ratio' = 0.01`.
 
 > **_TIPS:_** Sometimes the lambda sequences automatically computed by the program may have inappropriate ranges, if the optimal lambda pair `"lambda.min"` := $({\lambda_B}_\text{min}, {\lambda_\Theta}_\text{min})$ selected by the cross-validation is located at the boundary of the search domain, users can use the arguments `'x.scale.factor'` and/or `'x.min.ratio'` to shift and/or zoom the search ranges accordingly.
 
@@ -425,7 +425,7 @@ parallel::stopCluster(cl)
 ## commands should return almost the same results as the above ones (subtle differences 
 ## come from rounding the float numbers).
 
-# cl <- parallel::makeCluster(min(parallel::detectCores()-1, 3))
+# cl <- parallel::makeCluster(min(parallel::detectCores()-1, 2))
 # cvfit2.BM <- cv.missoNet(X = SNP[tr, ], Y = BM.mis[tr, ], kfold = 5,
 #                          lambda.Beta = 10^(seq(from = log10(2.66), to = log10(2.66*0.01), length.out = 40)),
 #                          lambda.Theta = 10^(seq(from = log10(2.99), to = log10(2.99*0.01), length.out = 40)),