Find the rank that minimizes the mean squared error of test set reconstruction using cross-validation.
Usage
cross_validate_nmf(
A,
ranks,
n_replicates = 3,
tol = 1e-04,
maxit = 100,
verbose = 1,
L1 = 0.01,
L2 = 0,
threads = 0,
test_density = 0.05
)
# S3 method for cross_validate_nmf_data
plot(x, ...)Arguments
- A
sparse matrix (ideally variance-stabilized) of data for genes x cells (rows x columns)
- ranks
a vector of ranks at which to fit a model and compute test set reconstruction error
- n_replicates
number of random test sets
- tol
tolerance of the fit (1e-5 for publication quality, 1e-3 for cross-validation)
- maxit
maximum number of iterations
- verbose
verbosity level
- L1
L1/LASSO penalty to increase sparsity of model
- L2
L2/Ridge penalty to increase angles between factors
- threads
number of threads for parallelization across CPUs, 0 = use all available threads
- test_density
fraction of values to include in the test set
- x
the result of
cross_validate_nmf- ...
additional arguments (not implemented)