Inferences From Fitted SuSiE Model — susie_get

These functions access basic properties or draw inferences from a fitted susie model.

susie_get_objective(res, last_only = TRUE, warning_tol = 1e-06)

susie_get_posterior_mean(res, prior_tol = 1e-09)

susie_get_posterior_sd(res, prior_tol = 1e-09)

susie_get_niter(res)

susie_get_prior_variance(res)

susie_get_residual_variance(res)

susie_get_lfsr(res)

susie_get_posterior_samples(susie_fit, num_samples)

susie_get_cs(
  res,
  X = NULL,
  Xcorr = NULL,
  coverage = 0.95,
  min_abs_corr = 0.5,
  dedup = TRUE,
  squared = FALSE
)

susie_get_pip(res, prune_by_cs = FALSE, prior_tol = 1e-09)

Arguments

res: A susie fit, typically an output from susie or one of its variants. For susie_get_pip and susie_get_cs, this may instead be the posterior inclusion probability matrix, alpha.
last_only: If last_only = FALSE, return the ELBO from all iterations; otherwise return the ELBO from the last iteration only.
warning_tol: Warn if ELBO is decreasing by this tolerance level.
prior_tol: Filter out effects having estimated prior variance smaller than this threshold.
susie_fit: A susie fit, an output from susie.
num_samples: The number of draws from the posterior distribution.
X: n by p matrix of values of the p variables (covariates) in n samples. When provided, correlation between variables will be computed and used to remove CSs whose minimum correlation among variables is smaller than min_abs_corr.
Xcorr: p by p matrix of correlations between variables (covariates). When provided, it will be used to remove CSs whose minimum correlation among variables is smaller than min_abs_corr.
coverage: A number between 0 and 1 specifying desired coverage of each CS.
min_abs_corr: A "purity" threshold for the CS. Any CS that contains a pair of variables with correlation less than this threshold will be filtered out and not reported.
dedup: If dedup = TRUE, remove duplicate CSs.
squared: If squared = TRUE, report min, mean and median of squared correlation instead of the absolute correlation.
prune_by_cs: Whether or not to ignore single effects not in a reported CS when calculating PIP.

Value

susie_get_objective returns the evidence lower bound (ELBO) achieved by the fitted susie model and, optionally, at each iteration of the IBSS fitting procedure.

susie_get_residual_variance returns the (estimated or fixed) residual variance parameter.

susie_get_prior_variance returns the (estimated or fixed) prior variance parameters.

susie_get_posterior_mean returns the posterior mean for the regression coefficients of the fitted susie model.

susie_get_posterior_sd returns the posterior standard deviation for coefficients of the fitted susie model.

susie_get_niter returns the number of model fitting iterations performed.

susie_get_pip returns a vector containing the posterior inclusion probabilities (PIPs) for all variables.

susie_get_lfsr returns a vector containing the average lfsr across variables for each single-effect, weighted by the posterior inclusion probability (alpha).

susie_get_posterior_samples returns a list containing the effect sizes samples and causal status with two components: b, an num_variables x num_samples matrix of effect sizes; gamma, an num_variables x num_samples matrix of causal status random draws.

susie_get_cs returns credible sets (CSs) from a susie fit, as well as summaries of correlation among the variables included in each CS. If desired, one can filter out CSs that do not meet a specified “purity” threshold; to do this, either X or Xcorr must be supplied. It returns a list with the following elements:

cs: A list in which each list element is a vector containing the indices of the variables in the CS.
coverage: The nominal coverage specified for each CS.
purity: If X or Xcorr iis provided), the purity of each CS.
cs_index: If X or Xcorr is provided) the index (number between 1 and L) of each reported CS in the supplied susie fit.

Examples

set.seed(1)
n = 1000
p = 1000
beta = rep(0,p)
beta[1:4] = 1
X = matrix(rnorm(n*p),nrow = n,ncol = p)
X = scale(X,center = TRUE,scale = TRUE)
y = drop(X %*% beta + rnorm(n))
s = susie(X,y,L = 10)
#> Error in susie(X, y, L = 10): could not find function "susie"
susie_get_objective(s)
#> Error in susie_get_objective(s): could not find function "susie_get_objective"
susie_get_objective(s, last_only=FALSE)
#> Error in susie_get_objective(s, last_only = FALSE): could not find function "susie_get_objective"
susie_get_residual_variance(s)
#> Error in susie_get_residual_variance(s): could not find function "susie_get_residual_variance"
susie_get_prior_variance(s)
#> Error in susie_get_prior_variance(s): could not find function "susie_get_prior_variance"
susie_get_posterior_mean(s)
#> Error in susie_get_posterior_mean(s): could not find function "susie_get_posterior_mean"
susie_get_posterior_sd(s)
#> Error in susie_get_posterior_sd(s): could not find function "susie_get_posterior_sd"
susie_get_niter(s)
#> Error in susie_get_niter(s): could not find function "susie_get_niter"
susie_get_pip(s)
#> Error in susie_get_pip(s): could not find function "susie_get_pip"
susie_get_lfsr(s)
#> Error in susie_get_lfsr(s): could not find function "susie_get_lfsr"