Description Usage Arguments Author(s)
Iteratively estimating scaled parameters and biomass
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dat |
OTU count/relative abundance matrix (each OTU in one row) |
external.perturbation |
external perturbation presence matrix (each perturbation in one row, each sample in one column) (Default: NULL) |
ncpu |
number of CPUs (default: 1) |
m.init |
initial biomass values (default: use CSS normalization) |
scaling |
a scaling factor to keep the median of all biomass constant (default: 1000) |
equil.filter |
threshold for detecting and removing samples not at equilibrium (default: Inf - all the samples will be considered) |
model.filter |
threshold for detecting and removing samples from different models (default: Inf - all the samples will be considered) |
refresh.iter |
refresh the removed samples every X iterations (default: 1) |
lambda.iter |
number of iterations to run before fixing lambda (default: Inf) |
warm.iter |
number of iterations to run before removing any samples (default: run until convergence and start to remove samples) |
max.iter |
maximal number of iterations (default 30) |
epsilon |
convergence threshold (in relative difference): uqn of the relative error in biomass changes (default 1e-3) |
lambda.choice |
1: use lambda.1se for LASSO, 2: use lambda.min for LASSO, a number between (0, 1): this will select a lambda according to (1-lambda.choice)*lambda.min + lambda.choice*lambda.1se |
alpha |
the alpha parameter for the Elastic Net model (1-LASSO [default], 0-RIDGE) |
debug |
output debugging information (default FALSE) |
verbose |
print out messages |
Chenhao Li, Gerald Tan, Niranjan Nagarajan
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