run_misty {mistyR} | R Documentation |
Trains multi-view models for all target markers, estimates the performance, the contributions of the view specific models and the importance of predictor markers for each target marker.
run_misty( views, results.folder = "results", seed = 42, target.subset = NULL, cv.folds = 10, cached = FALSE, append = FALSE, ... )
views |
view composition. |
results.folder |
path to the top level folder to store raw results. |
seed |
seed used for random sampling to ensure reproducibility. |
target.subset |
subset of targets to train models for. If |
cv.folds |
number of cross-validation folds to consider for estimating the performance of the multi-view models. |
cached |
a |
append |
a |
... |
all additional parameters are passed to
|
Default values passed to ranger()
for training the
view-specific models: num.trees = 100
, importance = "impurity"
,
num.threads = 1
, seed = seed
.
Path to the results folder that can be passed to
collect_results()
.
create_initial_view()
for
starting a view composition.
# Create a view composition of an intraview and a paraview with radius 10 then # run MISTy for a single sample. library(dplyr) # get the expression data data("synthetic") expr <- synthetic[[1]] %>% select(-c(row, col, type)) # get the coordinates for each cell pos <- synthetic[[1]] %>% select(row, col) # compose misty.views <- create_initial_view(expr) %>% add_paraview(pos, l = 10) # run with default parameters run_misty(misty.views)