SER: The single effect regression model
In statwangz/SuSiE: A simple new approach to variable selection in regression, with application to genetic fine-mapping
SER R Documentation
The single effect regression model
Description
The single effect regression model
Usage
SER(X, y, pi0 = NULL, sig2 = 10, sig02 = 0.1)
Arguments
X
SNPs, a n*p matrix.
y
trait, a n*1 vector.
pi0
hyperparameter of the prior distribution of gamma.
sig2
hyperparameter, the variance of the trait y.
sig02
hyperparameter, the variance of the coefficient.
Value
a tibble consists of the result of the function BSLR on the SNPs matrix X.
statwangz/SuSiE documentation built on Nov. 22, 2022, 5:21 p.m.
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| SER | R Documentation |
The single effect regression model
Description
The single effect regression model
Usage
SER(X, y, pi0 = NULL, sig2 = 10, sig02 = 0.1)
Arguments
X |
SNPs, a n*p matrix. |
y |
trait, a n*1 vector. |
pi0 |
hyperparameter of the prior distribution of gamma. |
sig2 |
hyperparameter, the variance of the trait y. |
sig02 |
hyperparameter, the variance of the coefficient. |
Value
a tibble consists of the result of the function BSLR on the SNPs matrix X.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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