Applies specified weight methods to the datasets X and Y, returning weight matrices for each method. Handles both univariate and multivariate methods, and filters out columns in X with zero standard error. This function utilizes parallel processing to handle multiple methods.
Usage
learnTwasWeights(
X,
Y,
weightMethods,
study = "",
context = "",
trait = "",
numThreads = 1,
fittedModels = NULL,
retainFits = FALSE,
retainFitDetail = c("slim", "full"),
standardized = FALSE,
dataType = NULL,
ldSketch = NULL,
verbose = 1
)Arguments
- X
A matrix of samples by features, where each row represents a sample and each column a feature.
- Y
A matrix (or vector, which will be converted to a matrix) of samples by outcomes, where each row corresponds to a sample.
- weightMethods
A list of methods and their specific arguments, formatted as list(method1 = method1_args, method2 = method2_args), or alternatively a character vector of method names (eg, c("susie_weights", "enet_weights")) in which case default arguments will be used for all methods. methods in the list can be either univariate (applied to each column of Y) or multivariate (applied to the entire Y matrix).
- numThreads
The number of threads to use for parallel processing. If set to -1, the function uses all available cores. If set to 0 or 1, no parallel processing is performed. If set to 2 or more, parallel processing is enabled with that many threads.
- fittedModels
Optional named list of fitted SuSiE-family models.
- retainFits
If TRUE, retain fitted model objects as attributes on returned weight matrices when supported by the weight method.
- verbose
Integer controlling verbosity level: 0 = suppress all messages, 1 = suppress external package messages (default), 2 = show all messages including those from external packages.