insilico / npdr Goto Github PK

I'm getting an error when trying to run the following in quantitative-trait-maineffect-simulation.R:

rncv.qtrait <- regular_nestedCV(train.ds = qtrait.data, 
+                                   validation.ds =  qtrait.3sets$validation, 
+                                   label = "qtrait",
+                                   method.model = "classification",
+                                   is.simulated = TRUE,
+                                   ncv_folds = c(10, 10),
+                                   param.tune = FALSE,
+                                   learning_method = "rf", 
+                                   importance.algorithm = "RReliefFequalK",
+                                   relief.k.method = "k_half_sigma",     # surf k
+                                   num_tree = 500,
+                                   verbose = F)

The error:

Error in if (tmps < .Machine$double.eps^0.5) 0 else tmpm/tmps : 
  missing value where TRUE/FALSE needed

Perhaps we need to revisit the regular_nestedCV function?

For large data, npdr runs out of memory when dopar.nn=T (parallel nearest-neighbor calculation). Temporary fix is dopar.nn=F. A long-term solution may be to use big.matrix from the bigmemory library to enforce a shared-memory version of the distance matrix instead of allowing copies of the matrix to be exported to the workers.

https://stackoverflow.com/questions/31575585/shared-memory-in-parallel-foreach-in-r

I think we want to remove mdd.RNAseq.rda from the package and replace it with a smaller dataset (perhaps a smaller number of randomly selected genes or just top genes).

• modularize the code (e.g. move simulation function elsewhere, break down functions with more than 500 lines of code, etc.)
• create examples that work (hopefully independent of the data simulation step, move the examples from inst to vignettes)

We discussed adding a "batch" variable in the individual regression to alleviate some violation of the independence assumption (hence the term pseudo). For example, a diff between sample 3 and 2 would have 3 as the batch number (rule of thumb: take the first sample id).

I thought about considering this variable as a random effect term, but the independence assumption there is not quite what we want. For instance, within the neighborhood of 3, these differences (e.g. 3-2, 3-5, 3-6) are independent. However, they may not be independent of other differences in a different neighborhood (e.g. 2-5). In short, we have within-neighborhood independence but not between-neighborhood (which a mixed model would correct for).

Maybe we should stick with the fixed model and adding the batch variable as a fixed effect term.