danknights / mwas Goto Github PK

Should be agnostic to the model evaluation method used inside it.

For gradient plots, PCoA

Using same tuning method as the internal nested cross validation.

Probably just use save() to save to a .rdat file that contains a single mwas model with all the necessary info to do predictions on new data.

Some labels get cut off at the bottom of the PDF. Just need to make bigger PDFs with more padding.

$ Rscript $MWAS_DIR/bin/plot-gradients.r -i seqs_V4V6_R1only_otus_metadata_Pygathrix_nemaeus_L6.txt -d Douc_V4V6_R1only_SplitOTUs_unweighted_unifrac_dm.txt -o L6_n30_Douc_splitOTUs_V4V6_R1only_unweighted_gradients_e2000 -n 30 -s

Error in x[, taxon.names[i]] : subscript out of bounds
Calls: show.gradients
Execution halted

input files:

http://goo.gl/KZFo4G
http://goo.gl/Y9iSq1

for gradient plots

Not sure best approach yet, needs to be explored. We could do a grid search over mtry and nfeatures, or we could just train mtry, and then after picking it, tune nfeatures, but these two parameters interact heavily so probably better to do grid search with (2, 5, 10, 25, 50, 100... features) (and it will be very slow).

Hi there,

Thanks for the repository and tool.
I'm unable to find paper for this tool and having troubles to find elucidated tutorial. Can you help?