sp_bgsmtr_path: A Bayesian Spatial Model for Imaging Genetics

In bgsmtr: Bayesian Group Sparse Multi-Task Regression

Description

A plotting function can be used to demonstrate the regularization paths for estimating parameters of each ROI when the spatial model is fitted with multiple values of tuning parameter lambda-squared.

Usage

sp_bgsmtr_path(lambda_v, W_est_list)

Arguments

lambda_v	A vector containing all the different tuning parameter lambda-squared values for model fitting.
W_est_list	A list containing all the estimated coefficients matrices W for each corresponding lambda squared value used in lambda_v for model fitting. Each element of this list is a d-by-c matrix.

Value

Regularization plots files in PDF format for each ROI.

Author(s)

Yin Song, [e-mail]

Shufei Ge, [e-mail]

Farouk S. Nathoo, [e-mail]

Liangliang Wang, [e-mail]

Jiguo Cao, [e-mail]

References

Song, Y., Ge, S., Cao, J., Wang, L., Nathoo, F.S., A Bayesian Spatial Model for Imaging Genetics. arXiv:1901.00068.

Examples

data(sp_bgsmtr_example_data$path_data)


## Not run: 

# Creating the regulazaition path plots  as follow:
sp_bgsmtr_path(lambda_v = sp_bgsmtr_example_data$path_data$lambda_v,
 W_est_list = sp_bgsmtr_example_data$path_data$W_est_list )


## End(Not run)

bgsmtr documentation built on Dec. 16, 2019, 1:35 a.m.