getRobustWeightsSingleBinary | R Documentation |
Compute aggregated (SmCCA) canonical weights for single omics data with quantitative phenotype (subampling enabled).
getRobustWeightsSingleBinary(
X1,
Trait,
Lambda1,
s1 = 0.7,
SubsamplingNum = 1000,
K = 3
)
X1 |
An |
Trait |
An |
Lambda1 |
LASSO penalty parameter for |
s1 |
Proportion of mRNA features to be included, default at |
SubsamplingNum |
Number of feature subsamples. Default is 1000. Larger number leads to more accurate results, but at a higher computational cost. |
K |
Number of hidden components for PLSDA, default is set to 3. |
A partial least squared weight matrix with p_1
rows. Each
column is the canonical correlation weights based on subsampled X1
features. The number of columns is SubsamplingNum
.
X <- matrix(rnorm(600,0,1), nrow = 60)
Y <- rbinom(60,1,0.5)
Ws <- getRobustWeightsSingleBinary(X1 = X, Trait = as.matrix(Y), Lambda1 = 0.8,
0.7, SubsamplingNum = 10)
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