Sum of Single Effects Linear Regression
Extract regression coefficients from susie fit
Compute sufficient statistics for input to susie_suff_stat
Compute sufficient statistics for input to susie_suff_stat
Estimate s in susie_rss Model Using Regularized LD
Get Correlations Between CSs, using Variable with Maximum PIP From Eac...
Compute Distribution of z-scores of Variant j Given Other z-scores, an...
Predict outcomes or extract coefficients from susie fit.
Bayesian single-effect linear regression
Summarize Susie Fit.
Sum of Single Effects (SuSiE) Regression
Attempt at Automating SuSiE for Hard Problems
Inferences From Fitted SuSiE Model
Initialize a susie object using regression coefficients
Plot changepoint data and susie fit using ggplot2
SuSiE Plots.
Sum of Single Effects (SuSiE) Regression using Summary Statistics
Apply susie to trend filtering (especially changepoint problems), a ty...
susieR: Sum of Single Effects Linear Regression
Perform Univariate Linear Regression Separately for Columns of X
Implements methods for variable selection in linear regression based on the "Sum of Single Effects" (SuSiE) model, as described in Wang et al (2020) <DOI:10.1101/501114> and Zou et al (2021) <DOI:10.1101/2021.11.03.467167>. These methods provide simple summaries, called "Credible Sets", for accurately quantifying uncertainty in which variables should be selected. The methods are motivated by genetic fine-mapping applications, and are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse. The fitting algorithm, a Bayesian analogue of stepwise selection methods called "Iterative Bayesian Stepwise Selection" (IBSS), is simple and fast, allowing the SuSiE model be fit to large data sets (thousands of samples and hundreds of thousands of variables).