Inferences From Fitted SuSiE Model — susie_get

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

susie_get_cs_attainable returns credible sets after a post-hoc filter based on attainable coverage and per-effect entropy. Use this as a fallback when no LD reference is available for the standard purity filter in susie_get_cs.

Usage

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,
  check_symmetric = TRUE,
  n_purity = "auto",
  use_rfast = NULL,
  median_abs_corr = NULL,
  cs_extension_corr = NULL
)

susie_get_cs_attainable(res, coverage = 0.95, ethres = NULL, ...)

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, applied to the minimum absolute correlation among the CS variables: a CS is filtered out unless this minimum is at least min_abs_corr. Set to NULL to disable this clause. This filter is only applied when X or Xcorr is provided; otherwise it is ignored and a warning is issued.
dedup: If dedup = TRUE, remove duplicate CSs.
squared: If squared = TRUE, report min, mean and median of squared correlation instead of the absolute correlation.
check_symmetric: If check_symmetric = TRUE, perform a check for symmetry of matrix Xcorr when Xcorr is provided (not NULL).
n_purity: Maximum number of CS variables used for purity when correlations are computed from X. The default, "auto", uses a resource-aware cap; a positive number gives a fixed cap; a negative number uses all CS variables. Xcorr inputs always use all variables.
use_rfast: Use the Rfast package for the purity calculations. By default use_rfast = TRUE if the Rfast package is installed.
median_abs_corr: An optional second purity threshold applied to the median absolute correlation among the CS variables. The default, NULL, leaves it off. When both min_abs_corr and median_abs_corr are non-NULL, they are combined with OR: a CS is kept if it clears either threshold (its min $\ge$ min_abs_corr or its median $\ge$ median_abs_corr).
cs_extension_corr: Either NULL or a single number between 0 and 1. If non-NULL, each credible set is extended to include every variable whose absolute correlation with a credible-set member exceeds this threshold, pulling in near-perfectly correlated proxies (variables statistically indistinguishable from the selected ones). Works from either X or Xcorr. The default, NULL, disables correlation-based extension; 0.99 is the recommended value when extension is desired.
ethres: Entropy threshold expressed as an effective number of variables: an effect is dropped when its attainable-projection entropy is at least log(ethres). Defaults to max(100, 0.1 * p) where p is the number of variables.
...: Additional arguments (e.g. X or Xcorr) accepted for compatibility with susie_get_cs; they are ignored here.
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.

Details

For each effect $l$, the attainable coverage is computed by zeroing every entry of res$alpha that is not the column maximum, then summing across variables. Intuitively, it is the coverage effect $l$ could attain if it did not have to share probability mass with other effects. Effects with attainable coverage at most coverage, or with attainable-projection entropy at least log(ethres), are dropped before delegating to susie_get_cs. Any X or Xcorr passed through ... is ignored, since the procedure is intended for the no-LD setting.

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)
susie_get_objective(s)
#> [1] -1455.415
susie_get_objective(s, last_only = FALSE)
#> [1] -1927.147 -1477.646 -1455.416 -1455.415 -1455.415
susie_get_residual_variance(s)
#> [1] 0.9899114
susie_get_prior_variance(s)
#>  [1] 1.1327140 1.0495380 1.0093910 0.9784211 0.0000000 0.0000000 0.0000000
#>  [8] 0.0000000 0.0000000 0.0000000
susie_get_posterior_mean(s)
#>    [1]   1.063825e+00   1.023986e+00   1.004192e+00   9.886512e-01
#>    [5] -8.427293e-217 -1.412823e-213  1.197896e-214  4.547656e-216
#>    [9]  1.728992e-216 -4.643562e-217  2.147373e-216  5.597200e-216
#>   [13]  1.116111e-216  2.854075e-216 -1.150395e-216  1.518459e-218
#>   [17]  1.213585e-215  2.948697e-215  2.864941e-216 -3.395543e-216
#>   [21] -4.740613e-215 -1.668472e-215  2.566936e-215 -4.166241e-216
#>   [25]  5.337269e-216 -1.393402e-216  9.656544e-217  1.572319e-217
#>   [29]  5.903837e-217 -1.353308e-216 -2.461175e-217  8.719484e-214
#>   [33]  8.272766e-217 -6.303617e-217 -4.594664e-217  1.156508e-212
#>   [37]  3.583529e-217 -3.872393e-216 -1.539145e-216  4.790814e-216
#>   [41]  5.542943e-217  9.483214e-218 -9.159829e-217 -9.106199e-216
#>   [45] -5.302846e-216  1.602963e-213 -6.408667e-215  2.387749e-216
#>   [49] -2.439808e-217  3.154539e-215 -1.415859e-215 -2.323585e-217
#>   [53]  1.867347e-217 -1.015534e-216  1.506567e-216  4.922827e-216
#>   [57]  7.473913e-217 -5.927407e-217 -6.329524e-216  1.207291e-215
#>   [61]  7.024712e-217 -2.800797e-215  3.555042e-217 -6.728893e-217
#>   [65] -2.873130e-217  5.014823e-217  1.199486e-215  1.888200e-216
#>   [69] -1.300137e-212  5.322228e-217  8.659521e-216  1.794971e-214
#>   [73]  2.844840e-217 -5.221637e-215 -3.609444e-216 -4.319542e-217
#>   [77]  1.140943e-216  1.604113e-215  4.151320e-214 -2.354860e-215
#>   [81] -7.175493e-217  6.335035e-217  4.789552e-216 -1.575946e-217
#>   [85]  3.790813e-216 -3.286198e-216 -3.703422e-218  6.656262e-217
#>   [89] -3.196099e-216  2.737793e-216 -4.346676e-216 -5.890447e-215
#>   [93]  6.747794e-217  1.811533e-216 -4.780408e-217  8.435735e-217
#>   [97] -7.100761e-217  1.484520e-215 -8.594564e-216 -1.359732e-216
#>  [101]  1.706251e-217  8.729743e-216 -1.900868e-214 -1.905685e-216
#>  [105]  3.775645e-216  3.300160e-214 -1.760936e-216 -5.319700e-216
#>  [109]  6.904052e-217  2.325632e-216  4.719111e-217  2.223240e-215
#>  [113] -6.653648e-216  3.666480e-218  5.439197e-216  1.428062e-216
#>  [117] -2.765192e-217  1.206716e-216 -9.852632e-217  7.811457e-216
#>  [121]  4.968853e-217 -1.347926e-216  8.717257e-217 -1.514236e-215
#>  [125]  8.973286e-217 -7.789675e-217 -1.195231e-215  4.452718e-216
#>  [129]  6.132242e-217  3.072157e-215  2.755883e-215  5.318986e-217
#>  [133] -4.439713e-217 -3.039752e-214  3.322935e-217  4.047436e-217
#>  [137]  6.791423e-218 -3.089188e-216  5.082729e-216  5.795248e-217
#>  [141] -5.049778e-217  1.002182e-216  3.088396e-217 -5.360825e-217
#>  [145] -1.024601e-216 -1.434245e-215  1.514155e-214  1.165505e-216
#>  [149]  4.893656e-216 -5.593975e-216  3.405242e-217  1.948198e-216
#>  [153]  2.980136e-216  5.490966e-217  2.084137e-217 -2.400516e-215
#>  [157] -1.360127e-216 -2.105002e-216 -1.132104e-215 -1.620506e-216
#>  [161] -9.817121e-217 -9.730135e-215 -1.402454e-214  4.274856e-217
#>  [165] -3.559636e-216  7.856199e-215 -5.986806e-217  1.880655e-217
#>  [169]  4.386460e-216  1.910457e-214  2.067950e-216  7.274866e-217
#>  [173] -2.998238e-216 -2.463727e-216 -5.272489e-216 -7.887065e-217
#>  [177] -2.151962e-216  2.337070e-215 -3.242070e-216  7.708985e-215
#>  [181]  1.053629e-217 -8.198640e-216 -1.829692e-216  2.379769e-216
#>  [185]  1.490568e-215  2.539829e-217 -3.752172e-215 -1.199840e-217
#>  [189] -1.790177e-216 -2.278080e-215 -4.351309e-217 -1.150192e-216
#>  [193]  7.903862e-216  2.664398e-217  3.466397e-217 -1.761401e-216
#>  [197] -1.093142e-216 -2.941011e-216 -1.042507e-216  1.256272e-216
#>  [201]  5.486802e-216 -2.281031e-214  1.014568e-216 -4.544429e-215
#>  [205]  3.698812e-215 -7.361131e-217  3.720020e-216  1.200931e-215
#>  [209] -1.097429e-217 -2.289199e-215 -8.221703e-217  3.215608e-218
#>  [213] -1.078937e-216 -1.354830e-213 -4.767434e-216 -1.527253e-217
#>  [217] -1.727097e-214 -5.192089e-216  1.599518e-215 -1.352559e-217
#>  [221] -8.550692e-217 -8.066136e-217 -8.098033e-217  7.569796e-217
#>  [225] -6.010890e-216  1.182882e-216  2.920104e-216 -1.588433e-215
#>  [229] -6.366531e-216 -4.172300e-213  2.560693e-216 -1.207909e-216
#>  [233]  2.449939e-216  8.277753e-216  1.856787e-215 -3.376833e-216
#>  [237]  1.655465e-217 -9.613350e-216  4.993672e-216 -1.757509e-215
#>  [241]  6.892461e-217 -9.028907e-218  8.875657e-216  4.761949e-216
#>  [245]  7.969440e-217  3.776571e-215  4.244003e-217 -1.317717e-216
#>  [249]  5.899957e-218  1.735292e-216 -5.314392e-217  1.510904e-217
#>  [253] -1.204661e-216 -1.317978e-216 -4.276038e-217  2.327742e-216
#>  [257]  4.512373e-217 -7.203680e-217  4.858737e-217  4.092413e-217
#>  [261] -1.123005e-216 -4.628788e-217 -5.793301e-217 -5.989722e-216
#>  [265]  2.116331e-215  2.587444e-217 -5.679557e-216 -1.802464e-216
#>  [269]  3.305436e-216  2.282423e-216 -1.472104e-216 -1.629957e-217
#>  [273] -1.434795e-212 -2.928897e-217  2.566889e-213 -1.460375e-216
#>  [277]  1.345215e-216  6.624465e-219 -3.509454e-216  2.697840e-216
#>  [281] -1.900449e-215 -8.854448e-216  8.173807e-217 -3.234932e-216
#>  [285]  1.359840e-218 -2.388152e-218 -1.859251e-216  2.509083e-217
#>  [289] -8.424867e-218  8.575970e-217 -1.159797e-217  1.599034e-214
#>  [293] -4.182515e-217  2.910351e-217 -2.781596e-216  4.057664e-216
#>  [297] -4.865283e-215 -8.927966e-216  5.069249e-217  3.912639e-216
#>  [301] -3.878243e-218 -9.429768e-217  9.685593e-213 -1.208695e-216
#>  [305]  7.131748e-217 -3.981147e-216  7.690427e-216 -2.988147e-216
#>  [309] -2.592952e-215  2.132277e-216  7.714168e-217 -6.045736e-219
#>  [313]  1.040165e-216  1.161783e-216  4.804776e-216  1.799120e-215
#>  [317] -4.062012e-216  3.435413e-216  3.045137e-212 -1.342025e-216
#>  [321]  4.534616e-217 -1.003861e-215  9.142973e-218  6.627613e-216
#>  [325] -6.438476e-217  3.201409e-215 -2.136037e-216  6.228674e-217
#>  [329] -1.093102e-215 -1.583204e-216  2.114756e-217  1.628946e-215
#>  [333]  6.022633e-216  6.367870e-218  1.303886e-217  6.441551e-215
#>  [337] -8.241467e-216  4.549452e-216  1.612158e-217 -1.129125e-216
#>  [341]  5.212832e-216 -2.467621e-217  3.219307e-216 -7.182413e-216
#>  [345]  8.429947e-217  1.552295e-214  4.257891e-216 -1.601396e-216
#>  [349]  1.612615e-217  8.286025e-216  3.257763e-216 -3.646519e-216
#>  [353]  6.349001e-216 -4.029640e-215  1.204125e-215 -6.155889e-217
#>  [357] -9.964909e-215 -1.107133e-216 -7.302429e-214 -1.239040e-217
#>  [361] -3.285467e-215  1.700570e-215  2.565218e-218 -2.291219e-214
#>  [365] -9.686419e-215 -2.955004e-216 -1.205257e-216  1.430171e-216
#>  [369]  3.256538e-215  5.324864e-217 -7.189157e-216  1.976100e-217
#>  [373]  8.725705e-217 -4.570514e-215  1.287734e-216  2.710923e-218
#>  [377] -5.283806e-216  5.170475e-216 -2.873513e-216  8.693468e-216
#>  [381] -5.479057e-217 -1.581481e-216  8.646189e-214  3.460520e-217
#>  [385] -1.454582e-214  2.759747e-217 -1.150475e-216 -2.099536e-216
#>  [389] -7.940816e-218  4.576486e-217  1.154860e-214  1.429349e-216
#>  [393] -7.761766e-217 -6.093247e-217  7.333918e-217 -5.226647e-216
#>  [397] -1.563437e-213  5.694360e-217  7.214456e-218  1.533936e-215
#>  [401]  1.572778e-216 -2.134332e-216  2.728864e-216 -1.423921e-215
#>  [405]  3.902191e-217 -1.829428e-216 -3.279894e-216 -5.762813e-217
#>  [409] -6.136403e-215 -3.391914e-216 -3.665966e-216  1.311186e-216
#>  [413]  4.331209e-216  4.507646e-217 -8.223027e-215 -1.716117e-216
#>  [417] -3.830627e-216  1.381175e-215 -1.794195e-216 -8.112567e-214
#>  [421]  7.268815e-216  2.827735e-216  2.874609e-216 -4.474448e-217
#>  [425] -3.729133e-216 -6.659258e-216  5.008038e-217 -1.750574e-215
#>  [429]  4.290034e-216 -1.931953e-216 -3.794616e-217  3.820498e-217
#>  [433]  1.652278e-214  1.949924e-216  2.872536e-216 -1.840404e-216
#>  [437]  2.407243e-216  1.000197e-216  5.731320e-217 -1.891350e-216
#>  [441]  2.130603e-216  4.593965e-217 -1.837456e-216 -3.521377e-216
#>  [445] -1.092255e-215 -4.171617e-217 -2.483831e-217 -9.805807e-216
#>  [449]  2.708780e-217  1.105804e-216 -4.310678e-214 -2.971953e-216
#>  [453]  4.319947e-216  1.050273e-216  9.335745e-216  1.826471e-216
#>  [457]  4.100484e-215 -3.077658e-216  3.689891e-215 -3.363657e-216
#>  [461]  1.056273e-215  7.307120e-216 -2.967899e-216  1.617689e-216
#>  [465] -2.109286e-217  7.238754e-217 -4.870692e-217  5.641310e-216
#>  [469]  1.696519e-215  2.190414e-215 -7.410489e-216  1.294478e-216
#>  [473] -2.663873e-216  3.709709e-218  3.758271e-216 -1.532517e-216
#>  [477] -3.974601e-216  1.562119e-215 -9.120058e-217  1.830299e-216
#>  [481] -1.161797e-215 -4.946233e-215  1.085741e-215 -6.647611e-217
#>  [485]  1.995635e-217 -1.644936e-213 -1.697922e-216 -8.404488e-217
#>  [489] -6.178084e-216 -3.543323e-215 -8.730654e-217 -6.031741e-217
#>  [493]  1.402329e-217 -5.761114e-216  5.205525e-218 -1.203300e-216
#>  [497]  1.232587e-216  9.272275e-217 -7.548049e-217  1.002864e-215
#>  [501]  2.843804e-217 -4.645350e-215  2.061029e-215  3.169740e-217
#>  [505]  4.622307e-217 -8.907265e-217 -1.902662e-217  2.090043e-216
#>  [509] -4.614599e-216  3.926129e-216 -1.669221e-215 -5.224746e-215
#>  [513]  2.077290e-216  4.773862e-218 -1.145553e-214 -1.854111e-215
#>  [517]  3.031492e-216  1.627296e-216 -1.171625e-214 -7.473846e-217
#>  [521]  5.902219e-218 -4.498334e-216 -2.931717e-218  7.957162e-218
#>  [525]  9.913587e-217  1.927584e-214  4.146244e-217 -6.877152e-217
#>  [529]  3.388528e-215 -2.634311e-216  7.760353e-216  4.261709e-216
#>  [533] -4.096932e-214 -1.981520e-216  5.926607e-217  1.335122e-216
#>  [537] -4.717581e-216  1.072728e-216 -3.991107e-216 -5.001788e-217
#>  [541]  1.600698e-218  2.396961e-216 -6.823347e-219 -5.609668e-216
#>  [545]  2.527788e-216 -1.173170e-216 -1.027556e-216  5.212214e-216
#>  [549]  1.359553e-213  1.775640e-216  1.260938e-216 -5.758384e-217
#>  [553] -9.461087e-217 -1.341010e-214 -2.052874e-211  8.415776e-217
#>  [557]  1.575963e-217  3.412178e-216  1.410143e-216  4.234330e-216
#>  [561]  8.537952e-217 -4.434859e-216  4.511638e-217  8.161487e-217
#>  [565]  3.348945e-214 -4.640565e-217 -4.579623e-216 -4.389377e-215
#>  [569]  6.105205e-216  3.381074e-217 -2.733037e-215 -6.859263e-216
#>  [573] -6.851297e-217 -7.468425e-217  3.117397e-216  3.568915e-217
#>  [577]  4.027443e-216  2.160790e-216  2.642393e-216 -9.394323e-218
#>  [581]  2.636733e-216  4.072860e-216 -2.090416e-216  4.225910e-216
#>  [585] -2.243221e-216 -1.450717e-215 -2.178369e-217  1.103577e-216
#>  [589]  2.792895e-217  6.725536e-216  6.511219e-216 -4.149990e-216
#>  [593]  4.128998e-216  4.814881e-214 -1.509313e-216 -3.461388e-216
#>  [597]  2.109938e-216  1.158179e-217  1.411677e-216  2.045761e-215
#>  [601] -6.641925e-218  1.019001e-216  2.027095e-215  4.206809e-217
#>  [605]  7.600182e-216  1.112121e-216  1.292432e-217 -1.327969e-215
#>  [609]  3.160206e-217  1.665477e-215  3.143820e-217  3.546735e-211
#>  [613] -5.433058e-217 -6.035703e-216 -6.945220e-218 -1.563008e-216
#>  [617] -6.421475e-217  2.585751e-215 -1.150114e-215  4.512489e-217
#>  [621]  9.308500e-216  9.762291e-217 -4.064195e-217 -1.218381e-214
#>  [625] -2.749352e-215 -1.953349e-215 -1.566064e-216  6.599225e-216
#>  [629] -1.543486e-216 -1.432908e-217 -1.587486e-215  4.904915e-217
#>  [633] -4.107396e-216 -6.537847e-217  1.313153e-216 -8.078592e-215
#>  [637]  4.850114e-216 -1.225605e-216  3.064262e-217 -9.832432e-216
#>  [641]  5.883175e-215  7.774481e-218 -4.133560e-213 -6.433292e-216
#>  [645]  2.401906e-217 -5.719294e-217 -2.453749e-217 -1.441506e-216
#>  [649]  2.608977e-216 -1.812683e-216  5.511209e-217  4.243272e-217
#>  [653]  2.475665e-216 -1.253978e-217  6.033558e-216 -9.515128e-217
#>  [657] -9.735938e-217  6.096173e-216 -2.382726e-217  6.706837e-217
#>  [661]  9.440285e-216  2.342263e-217 -7.217719e-215  1.216135e-216
#>  [665]  1.422733e-216  6.100864e-217 -2.999267e-216 -6.886908e-217
#>  [669] -9.089097e-217  1.272056e-216  1.374619e-215 -5.318052e-218
#>  [673]  1.847424e-216 -3.198436e-217  4.025458e-217  6.792777e-217
#>  [677]  1.498965e-217 -1.187481e-216 -1.128666e-217  7.230911e-216
#>  [681]  6.926727e-217  7.251052e-216 -3.297156e-216 -8.029874e-217
#>  [685] -2.161351e-216 -1.687859e-213 -1.417861e-216 -2.848885e-218
#>  [689]  1.526208e-213  1.584823e-216  3.042934e-215  3.547259e-216
#>  [693]  2.188315e-217  1.188507e-216 -4.426148e-217 -8.586845e-217
#>  [697] -4.403551e-219  1.752902e-217  2.833020e-216 -9.465437e-217
#>  [701] -1.477964e-215 -1.280008e-215 -1.947773e-216  1.045258e-216
#>  [705]  1.395285e-216  1.023629e-215  9.421619e-216  8.942266e-217
#>  [709] -3.013921e-216 -3.213966e-215  6.122296e-215  1.174812e-214
#>  [713]  8.412495e-217 -3.826357e-216  1.756199e-216 -4.909296e-215
#>  [717]  4.101994e-216 -1.503358e-216 -8.528036e-217 -7.482856e-216
#>  [721]  5.469136e-217  1.227685e-216 -6.670684e-217  5.888138e-215
#>  [725] -2.099995e-216  2.598866e-217 -8.002005e-215 -9.336647e-214
#>  [729]  7.192576e-219  1.635682e-216 -1.800400e-216  2.744414e-213
#>  [733] -1.460948e-216 -6.638511e-216 -7.008765e-216 -7.821387e-217
#>  [737] -3.802895e-216  7.493557e-216 -2.873647e-216  1.410225e-216
#>  [741] -6.450642e-216 -5.230267e-217  4.273859e-217  3.412417e-218
#>  [745]  1.359885e-215 -2.809054e-215 -1.069440e-216 -4.529827e-216
#>  [749] -1.905327e-215  8.122053e-217  3.253895e-216 -6.592755e-216
#>  [753]  5.634121e-217 -5.770983e-217 -2.988608e-217 -4.043759e-216
#>  [757] -3.194703e-215  2.767674e-214 -6.420779e-218  2.071929e-215
#>  [761]  2.176633e-217  1.189649e-216 -6.334280e-215  2.209745e-216
#>  [765] -1.600023e-215  2.369590e-214  1.567566e-216  5.456968e-216
#>  [769] -1.407395e-216  5.207616e-217  1.140890e-215  3.229204e-216
#>  [773] -1.680549e-216  1.321344e-213 -2.961452e-217  1.135243e-218
#>  [777]  1.609590e-216 -2.808356e-216 -3.182285e-216  2.324360e-216
#>  [781]  6.867469e-217  1.201680e-216  3.008742e-216  1.627264e-216
#>  [785] -2.157605e-215 -3.433126e-217  2.409478e-216  5.400722e-217
#>  [789] -1.189387e-215  1.155666e-217 -2.602332e-216  4.048266e-216
#>  [793] -3.720733e-214  8.399286e-218  1.090518e-215 -4.492910e-217
#>  [797] -7.855338e-216 -3.157125e-215 -7.707450e-215 -4.604501e-216
#>  [801]  5.256258e-217  8.332085e-215 -1.207271e-216  2.172281e-216
#>  [805] -3.590094e-217 -9.004774e-217  7.987140e-217  4.423918e-217
#>  [809] -1.057586e-215  3.942159e-215  1.130538e-216  1.199009e-215
#>  [813]  6.176593e-216  1.622906e-216 -3.260066e-216 -2.692679e-217
#>  [817] -2.182427e-216  1.875767e-214  4.388170e-217 -3.250889e-215
#>  [821] -8.146582e-216 -1.039836e-215 -3.714667e-217 -4.245228e-216
#>  [825] -3.890216e-216 -3.630442e-216 -1.753481e-217 -5.172573e-217
#>  [829]  1.241677e-217  5.641794e-217  5.492503e-216 -6.678705e-216
#>  [833]  2.857735e-215  1.761412e-216  1.677920e-217  3.709484e-217
#>  [837] -9.474331e-217 -1.040125e-216  5.926256e-216  1.675060e-216
#>  [841] -2.585308e-216  2.443737e-216  2.317672e-216  1.610984e-216
#>  [845]  6.020717e-216  1.939623e-216 -2.612850e-217 -2.438442e-216
#>  [849]  2.461196e-216  1.493024e-217  4.104595e-217  3.269909e-215
#>  [853]  1.267782e-217  2.441145e-216  5.123209e-217 -2.435964e-215
#>  [857]  1.783579e-217  6.531855e-217  2.458507e-216 -8.718238e-216
#>  [861] -3.355384e-216 -2.208147e-217  3.176900e-215 -2.265065e-217
#>  [865]  2.148903e-216  5.064603e-216 -1.269684e-215  5.315100e-217
#>  [869] -1.831934e-216  5.530414e-216 -3.483427e-217 -2.829108e-216
#>  [873] -1.100012e-215 -2.314903e-216  1.416649e-216  7.925281e-217
#>  [877]  8.426842e-216  9.084605e-215  3.025104e-217 -1.775702e-217
#>  [881]  5.135103e-216 -1.788229e-216  1.178715e-215  5.048711e-217
#>  [885] -4.195915e-216 -1.506657e-217 -2.595342e-216 -1.589564e-217
#>  [889]  4.188534e-217  8.228061e-217 -1.804984e-215  7.609760e-217
#>  [893] -1.534968e-216 -2.184856e-217 -1.160144e-217  2.032672e-216
#>  [897]  6.095911e-215 -6.168607e-215 -3.939712e-216 -8.360178e-217
#>  [901]  1.068503e-214  1.337967e-216  1.284205e-215  5.208417e-216
#>  [905] -1.343585e-215 -7.358435e-215 -3.400639e-216  6.097987e-216
#>  [909] -1.144056e-216  1.424526e-216 -4.724832e-217 -2.164591e-216
#>  [913] -1.736573e-216  1.015596e-215  7.879966e-213  2.295225e-218
#>  [917] -4.371412e-217 -1.058333e-217 -1.011101e-216 -2.014668e-216
#>  [921] -2.215191e-216 -1.549498e-216  8.226814e-216  3.364849e-217
#>  [925] -3.735674e-217  1.707696e-217 -1.042749e-217 -1.742368e-217
#>  [929] -4.336390e-217 -3.270096e-215  2.431237e-216 -4.325937e-216
#>  [933]  6.331248e-216 -2.816716e-213  1.629332e-216  1.066731e-217
#>  [937] -1.995510e-216 -1.808701e-216  2.818540e-216 -5.605112e-216
#>  [941] -3.656781e-217  5.832661e-216 -2.131764e-217  1.388040e-216
#>  [945] -2.544291e-214 -4.207061e-216  3.824103e-215  7.443473e-217
#>  [949]  3.752229e-218  2.723645e-216 -5.200838e-217 -4.587746e-216
#>  [953]  8.339154e-217 -1.248232e-216  1.049824e-216 -2.529985e-217
#>  [957] -8.944039e-217  2.467005e-218 -3.755785e-215 -5.073845e-216
#>  [961]  1.671837e-215  2.300336e-216  2.320380e-216 -5.987370e-216
#>  [965] -4.514829e-215  1.416846e-217 -6.963283e-213  1.622704e-217
#>  [969] -3.257799e-217 -3.982050e-217  5.110292e-217 -4.315132e-217
#>  [973]  2.379430e-217 -3.928079e-215 -7.891771e-216 -1.812451e-216
#>  [977] -8.915630e-214  1.072031e-217  1.236350e-216  1.805288e-215
#>  [981]  8.466875e-217 -8.235106e-216  4.686603e-217 -1.596028e-215
#>  [985] -7.148361e-217  1.888517e-217 -1.177087e-216 -6.550663e-217
#>  [989] -1.997028e-216 -7.237191e-216 -1.253000e-216  1.887134e-216
#>  [993] -3.386339e-214 -1.733782e-215  7.797846e-218  9.501416e-214
#>  [997] -7.260097e-217  2.469508e-216 -1.295962e-216  1.242308e-214
susie_get_posterior_sd(s)
#>    [1]  3.146484e-02  3.146375e-02  3.146316e-02  3.146267e-02 2.468100e-109
#>    [6] 1.283671e-107 3.393969e-108 5.500371e-109 3.303433e-109 2.146670e-109
#>   [11] 3.676918e-109 6.175022e-109 2.726654e-109 4.265346e-109 2.759365e-109
#>   [16] 1.957965e-109 9.537558e-109 1.567373e-108 4.274004e-109 4.684027e-109
#>   [21] 2.039577e-108 1.140355e-108 1.450886e-108 5.240018e-109 6.013098e-109
#>   [26] 2.990785e-109 2.583343e-109 1.981819e-109 2.244857e-109 2.952800e-109
#>   [31] 2.015464e-109 9.919215e-108 2.453757e-109 2.278313e-109 2.143169e-109
#>   [36] 3.909034e-107 2.075479e-109 5.033056e-109 3.127839e-109 5.661840e-109
#>   [41] 2.215511e-109 1.966584e-109 2.536451e-109 8.115074e-109 5.991418e-109
#>   [46] 1.373036e-107 2.408630e-108 3.882862e-109 2.014501e-109 1.627318e-108
#>   [51] 1.040026e-108 2.009393e-109 1.991859e-109 2.630669e-109 3.097353e-109
#>   [56] 5.748125e-109 2.380850e-109 2.246800e-109 6.615103e-109 9.510201e-109
#>   [61] 2.340799e-109 1.523113e-108 2.073744e-109 2.314863e-109 2.035400e-109
#>   [66] 2.174274e-109 9.475121e-109 3.447812e-109 4.157486e-107 2.198016e-109
#>   [71] 7.888719e-109 4.230502e-108 2.033949e-109 2.151428e-108 4.842707e-109
#>   [76] 2.123632e-109 2.750332e-109 1.115479e-108 6.659000e-108 1.382747e-108
#>   [81] 2.354153e-109 2.280983e-109 5.661013e-109 1.981926e-109 4.974532e-109
#>   [86] 4.601535e-109 1.959092e-109 2.308557e-109 4.532758e-109 4.171981e-109
#>   [91] 5.364301e-109 2.299317e-108 2.316508e-109 3.378627e-109 2.156701e-109
#>   [96] 2.468810e-109 2.347520e-109 1.068039e-108 7.855383e-109 2.958893e-109
#>  [101] 1.986017e-109 7.924635e-109 4.364330e-108 3.463500e-109 4.963600e-109
#>  [106] 5.883825e-108 3.332619e-109 6.002040e-109 2.330170e-109 3.830236e-109
#>  [111] 2.152186e-109 1.339046e-108 6.803026e-109 1.959066e-109 6.076962e-109
#>  [116] 3.023532e-109 2.029931e-109 2.813184e-109 2.601912e-109 7.444522e-109
#>  [121] 2.170791e-109 2.947695e-109 2.494992e-109 1.079982e-108 2.518936e-109
#>  [126] 2.409434e-109 9.456213e-109 5.436398e-109 2.263856e-109 1.603548e-108
#>  [131] 1.509461e-108 2.197761e-109 2.132054e-109 5.628285e-108 2.059989e-109
#>  [136] 2.105123e-109 1.962287e-109 4.450287e-109 5.851396e-109 2.235941e-109
#>  [141] 2.176936e-109 2.617976e-109 2.046840e-109 2.201049e-109 2.639297e-109
#>  [146] 1.047586e-108 3.856517e-108 2.773808e-109 5.729139e-109 6.173004e-109
#>  [151] 2.064792e-109 3.501506e-109 4.365130e-109 2.211359e-109 1.999560e-109
#>  [156] 1.397645e-108 2.959268e-109 3.639968e-109 9.172115e-109 3.203525e-109
#>  [161] 2.598543e-109 3.029227e-108 3.698819e-108 2.120537e-109 4.806075e-109
#>  [166] 2.693980e-108 2.251720e-109 1.991961e-109 5.391430e-109 4.376290e-108
#>  [171] 3.607494e-109 2.363006e-109 4.379352e-109 3.946677e-109 6.190593e-109
#>  [176] 2.418313e-109 3.680909e-109 1.376906e-108 4.567918e-109 2.666143e-108
#>  [181] 1.968643e-109 7.649781e-109 3.395071e-109 3.876123e-109 1.070478e-108
#>  [186] 2.019070e-109 1.791888e-108 1.971850e-109 3.359245e-109 1.357393e-108
#>  [191] 2.125850e-109 2.759172e-109 7.493900e-109 2.024976e-109 2.068409e-109
#>  [196] 3.333044e-109 2.704661e-109 4.334317e-109 2.656350e-109 2.860482e-109
#>  [201] 6.106620e-109 4.818148e-108 2.629750e-109 1.992464e-108 1.777709e-108
#>  [206] 2.370722e-109 4.923365e-109 9.481534e-109 1.969562e-109 1.361089e-108
#>  [211] 2.449022e-109 1.958896e-109 2.691104e-109 1.255293e-107 5.646460e-109
#>  [216] 1.980477e-109 4.142775e-108 5.921265e-109 1.113687e-108 1.975629e-109
#>  [221] 2.479482e-109 2.434708e-109 2.437648e-109 2.389496e-109 6.426376e-109
#>  [226] 2.790413e-109 4.317800e-109 1.109352e-108 6.636761e-109 2.281974e-107
#>  [231] 4.027865e-109 2.814322e-109 3.935141e-109 7.691203e-109 1.210747e-108
#>  [236] 4.669975e-109 1.984387e-109 8.366279e-109 5.794044e-109 1.174058e-108
#>  [241] 2.329153e-109 1.965765e-109 7.998866e-109 5.642847e-109 2.425857e-109
#>  [246] 1.798356e-108 2.118413e-109 2.918996e-109 1.961173e-109 3.309199e-109
#>  [251] 2.197402e-109 1.980000e-109 2.811229e-109 2.919244e-109 2.120824e-109
#>  [256] 3.832030e-109 2.137219e-109 2.356660e-109 2.162520e-109 2.108122e-109
#>  [261] 2.733189e-109 2.145805e-109 2.235775e-109 6.413691e-109 1.302678e-108
#>  [266] 2.021299e-109 6.225638e-109 3.370400e-109 4.616092e-109 3.793388e-109
#>  [271] 3.065008e-109 1.983585e-109 4.378733e-107 2.038293e-109 1.763790e-107
#>  [276] 3.053978e-109 2.945120e-109 1.957781e-109 4.768976e-109 4.139601e-109
#>  [281] 1.226601e-108 7.988110e-109 2.444610e-109 4.562471e-109 1.957920e-109
#>  [286] 1.958301e-109 3.421763e-109 2.017661e-109 1.964729e-109 2.481832e-109
#>  [291] 1.970932e-109 3.972742e-108 2.114209e-109 2.037326e-109 4.207303e-109
#>  [296] 5.164224e-109 2.069051e-108 8.025347e-109 2.178422e-109 5.061743e-109
#>  [301] 1.959223e-109 2.561887e-109 3.560384e-107 2.815085e-109 2.350267e-109
#>  [306] 5.110330e-109 7.379464e-109 4.371422e-109 1.459065e-108 3.663776e-109
#>  [311] 2.402573e-109 1.957773e-109 2.654119e-109 2.770250e-109 5.671010e-109
#>  [316] 1.189547e-108 5.167270e-109 4.713876e-109 6.500621e-107 2.942091e-109
#>  [321] 2.138808e-109 8.572479e-109 1.966054e-109 6.788074e-109 2.289803e-109
#>  [326] 1.640715e-108 3.667052e-109 2.271970e-109 8.993076e-109 3.168900e-109
#>  [331] 2.000769e-109 1.125129e-108 6.433404e-109 1.961739e-109 1.974377e-109
#>  [336] 2.415426e-108 7.672227e-109 5.501576e-109 1.983034e-109 2.739037e-109
#>  [341] 5.934449e-109 2.015756e-109 4.550530e-109 7.101459e-109 2.468274e-109
#>  [346] 3.909109e-108 5.303404e-109 3.185804e-109 1.983047e-109 7.695524e-109
#>  [351] 4.579880e-109 4.869853e-109 6.626508e-109 1.864171e-108 9.495699e-109
#>  [356] 2.265841e-109 3.069023e-108 2.718024e-109 9.020267e-108 1.972778e-109
#>  [361] 1.664517e-108 1.152594e-108 1.958387e-109 4.829800e-108 3.021768e-108
#>  [366] 4.345353e-109 2.811791e-109 3.025522e-109 1.656358e-108 2.198222e-109
#>  [371] 7.105204e-109 1.996048e-109 2.495782e-109 1.998781e-108 2.890465e-109
#>  [376] 1.958470e-109 5.979401e-109 5.907504e-109 4.280828e-109 7.906098e-109
#>  [381] 2.210410e-109 3.167298e-109 9.874496e-108 2.068059e-109 3.773115e-108
#>  [386] 2.029657e-109 2.759443e-109 3.635216e-109 1.963951e-109 2.141816e-109
#>  [391] 3.326815e-108 3.024746e-109 2.406895e-109 2.260643e-109 2.368285e-109
#>  [396] 5.943219e-109 1.354892e-107 2.227726e-109 1.962869e-109 1.087842e-108
#>  [401] 3.159199e-109 3.665566e-109 4.164758e-109 1.043346e-108 2.095567e-109
#>  [406] 3.394833e-109 4.596715e-109 2.233289e-109 2.351731e-108 4.681303e-109
#>  [411] 4.884050e-109 2.912785e-109 5.353728e-109 2.136880e-109 2.762310e-108
#>  [416] 3.291640e-109 5.003149e-109 1.025644e-108 3.362892e-109 9.543359e-108
#>  [421] 7.149323e-109 4.244312e-109 4.281699e-109 2.134516e-109 4.929973e-109
#>  [426] 6.806241e-109 2.173760e-109 1.171461e-108 5.325507e-109 3.487007e-109
#>  [431] 2.088650e-109 2.090303e-109 4.044182e-108 3.503045e-109 4.280051e-109
#>  [436] 3.404763e-109 3.899292e-109 2.616090e-109 2.230719e-109 3.450640e-109
#>  [441] 3.662317e-109 2.143077e-109 3.402092e-109 4.777810e-109 8.989156e-109
#>  [446] 2.113468e-109 2.016493e-109 8.460083e-109 2.027138e-109 2.716755e-109
#>  [451] 6.795500e-108 4.358697e-109 5.346020e-109 2.663753e-109 8.229512e-109
#>  [456] 3.392157e-109 1.882257e-108 4.441333e-109 1.775329e-108 4.660063e-109
#>  [461] 8.821404e-109 7.170464e-109 4.355508e-109 3.200915e-109 2.000553e-109
#>  [466] 2.359788e-109 2.163413e-109 6.202156e-109 1.151065e-108 1.327975e-108
#>  [471] 7.227280e-109 2.896886e-109 4.111951e-109 1.959097e-109 4.951057e-109
#>  [476] 3.121640e-109 5.105702e-109 1.099008e-108 2.532712e-109 3.395620e-109
#>  [481] 9.306597e-109 2.088002e-108 8.958970e-109 2.307808e-109 1.996169e-109
#>  [486] 1.392073e-107 3.274943e-109 2.465915e-109 6.525921e-109 1.735846e-108
#>  [491] 2.496247e-109 2.255456e-109 1.976952e-109 6.275491e-109 1.960413e-109
#>  [496] 2.809920e-109 2.837885e-109 2.547034e-109 2.387532e-109 8.567691e-109
#>  [501] 2.033896e-109 2.016815e-108 1.283538e-108 2.051301e-109 2.145134e-109
#>  [506] 2.512751e-109 1.992749e-109 3.626916e-109 5.545161e-109 5.071336e-109
#>  [511] 1.140638e-108 2.152136e-108 3.615698e-109 1.960009e-109 3.312139e-108
#>  [516] 1.209769e-108 4.405365e-109 3.209813e-109 3.353112e-108 2.380848e-109
#>  [521] 1.961177e-109 5.467203e-109 1.958588e-109 1.963976e-109 2.607698e-109
#>  [526] 4.397537e-108 2.111749e-109 2.327810e-109 1.693324e-108 4.087793e-109
#>  [531] 7.417105e-109 5.306033e-109 6.611849e-108 3.531158e-109 2.246817e-109
#>  [536] 2.935537e-109 5.613562e-109 2.685171e-109 5.117398e-109 2.173285e-109
#>  [541] 1.957990e-109 3.890630e-109 1.957783e-109 6.182681e-109 4.000019e-109
#>  [546] 2.781132e-109 2.642109e-109 5.934057e-109 1.257626e-107 3.346018e-109
#>  [551] 2.864931e-109 2.232923e-109 2.564845e-109 3.609568e-108 1.761360e-106
#>  [556] 2.466968e-109 1.981927e-109 4.696496e-109 3.006613e-109 5.287160e-109
#>  [561] 2.478300e-109 5.424303e-109 2.137168e-109 2.443474e-109 5.930629e-108
#>  [566] 2.146451e-109 5.521792e-109 1.954561e-108 6.482667e-109 2.063372e-109
#>  [571] 1.502478e-108 6.920230e-109 2.325849e-109 2.380356e-109 4.472151e-109
#>  [576] 2.074597e-109 5.142982e-109 3.688578e-109 4.094407e-109 1.966419e-109
#>  [581] 4.089783e-109 5.174874e-109 3.627206e-109 5.281346e-109 3.759782e-109
#>  [586] 1.054323e-108 2.003317e-109 2.714627e-109 2.031317e-109 6.844174e-109
#>  [591] 6.720934e-109 5.228722e-109 5.214106e-109 7.212429e-108 3.099926e-109
#>  [596] 4.733255e-109 3.644282e-109 1.970905e-109 3.008070e-109 1.278213e-108
#>  [601] 1.962091e-109 2.633971e-109 1.271679e-108 2.115865e-109 7.330667e-109
#>  [606] 2.722791e-109 1.974089e-109 1.003266e-108 2.050769e-109 1.139204e-108
#>  [611] 2.049861e-109 2.340889e-106 2.206756e-109 6.441221e-109 1.962494e-109
#>  [616] 3.150098e-109 2.288350e-109 1.456805e-108 9.253866e-109 2.137226e-109
#>  [621] 8.216230e-109 2.593345e-109 2.106234e-109 3.425540e-108 1.507468e-108
#>  [626] 1.245590e-108 3.152946e-109 6.771748e-109 3.131892e-109 1.977803e-109
#>  [631] 1.108981e-108 2.165975e-109 5.199034e-109 2.298331e-109 2.914657e-109
#>  [636] 2.735578e-108 5.700716e-109 2.831220e-109 2.045503e-109 8.473013e-109
#>  [641] 2.297752e-108 1.963694e-109 2.270731e-107 6.675698e-109 2.012812e-109
#>  [646] 2.229742e-109 2.015131e-109 3.036209e-109 4.067022e-109 3.379669e-109
#>  [651] 2.212974e-109 2.118525e-109 3.956650e-109 1.973140e-109 6.439938e-109
#>  [656] 2.569950e-109 2.590847e-109 6.477291e-109 2.011965e-109 2.312945e-109
#>  [661] 8.281222e-109 2.010199e-109 2.571414e-108 2.822178e-109 3.018503e-109
#>  [666] 2.261228e-109 4.380146e-109 2.328666e-109 2.529803e-109 2.875529e-109
#>  [671] 1.022908e-108 1.960531e-109 3.411094e-109 2.052904e-109 2.103671e-109
#>  [676] 2.320430e-109 1.979653e-109 2.794807e-109 1.970244e-109 7.128356e-109
#>  [681] 2.332162e-109 7.139502e-109 4.609816e-109 2.431382e-109 3.689065e-109
#>  [686] 1.411304e-107 3.013902e-109 1.958539e-109 1.337601e-107 3.170406e-109
#>  [691] 1.595047e-108 4.796943e-109 2.003722e-109 2.795793e-109 2.131096e-109
#>  [696] 2.482844e-109 1.957760e-109 1.987556e-109 4.248535e-109 2.565255e-109
#>  [701] 1.065390e-108 9.827468e-109 3.501127e-109 2.658972e-109 2.992567e-109
#>  [706] 8.667022e-109 8.272060e-109 2.516029e-109 4.391626e-109 1.644288e-108
#>  [711] 2.348751e-108 3.358101e-108 2.466656e-109 5.000085e-109 3.328299e-109
#>  [716] 2.079372e-108 5.195260e-109 3.094346e-109 2.477376e-109 7.266854e-109
#>  [721] 2.209621e-109 2.833206e-109 2.309807e-109 2.298820e-108 3.635588e-109
#>  [726] 2.021839e-109 2.721451e-108 1.028893e-107 1.957788e-109 3.217573e-109
#>  [731] 3.368527e-109 1.827540e-107 3.054517e-109 6.794335e-109 7.004516e-109
#>  [736] 2.412322e-109 4.983228e-109 7.272692e-109 4.280935e-109 3.006690e-109
#>  [741] 6.685789e-109 2.190834e-109 2.120470e-109 1.958892e-109 1.016737e-108
#>  [746] 1.525611e-108 2.682035e-109 5.488398e-109 1.228362e-108 2.439846e-109
#>  [751] 4.576933e-109 6.768019e-109 2.222841e-109 2.233951e-109 2.041442e-109
#>  [756] 5.154455e-109 1.638804e-108 5.350045e-108 1.961807e-109 1.287329e-108
#>  [761] 2.003246e-109 2.796879e-109 2.393197e-108 3.730955e-109 1.113884e-108
#>  [766] 4.918640e-108 3.154345e-109 6.088046e-109 3.004016e-109 2.189504e-109
#>  [771] 9.212070e-109 4.558095e-109 3.258974e-109 1.238645e-107 2.040003e-109
#>  [776] 1.957864e-109 3.193407e-109 4.228791e-109 4.522157e-109 3.829153e-109
#>  [781] 2.326964e-109 2.808372e-109 4.387571e-109 3.209785e-109 1.316815e-108
#>  [786] 2.066432e-109 3.901174e-109 2.204197e-109 9.430196e-109 1.970839e-109
#>  [791] 4.061562e-109 5.157621e-109 6.277296e-108 1.964685e-109 8.981117e-109
#>  [796] 2.136020e-109 7.468001e-109 1.628060e-108 2.665851e-108 5.538422e-109
#>  [801] 2.192858e-109 2.782351e-108 2.813715e-109 3.698549e-109 2.075881e-109
#>  [806] 2.521888e-109 2.427465e-109 2.130939e-109 8.827571e-109 1.841636e-108
#>  [811] 2.740389e-109 9.473001e-109 6.525032e-109 3.205748e-109 4.581634e-109
#>  [816] 2.026350e-109 3.707342e-109 4.332930e-108 2.128427e-109 1.654760e-108
#>  [821] 7.622433e-109 8.743934e-109 2.083598e-109 5.294678e-109 5.045770e-109
#>  [826] 4.858096e-109 1.987574e-109 2.186363e-109 1.972842e-109 2.223461e-109
#>  [831] 6.110200e-109 6.817388e-109 1.540275e-108 3.333055e-109 1.985101e-109
#>  [836] 2.083274e-109 2.566097e-109 2.654081e-109 6.375548e-109 3.253917e-109
#>  [841] 4.047557e-109 3.929945e-109 3.823462e-109 3.194699e-109 6.432258e-109
#>  [846] 3.493857e-109 2.022503e-109 3.925506e-109 3.944561e-109 1.979482e-109
#>  [851] 2.108940e-109 1.660133e-108 1.973477e-109 3.927773e-109 2.182558e-109
#>  [856] 1.409123e-108 1.988587e-109 2.297815e-109 3.942312e-109 7.918759e-109
#>  [861] 4.653832e-109 2.004535e-109 1.633721e-108 2.006903e-109 3.678250e-109
#>  [866] 5.839756e-109 9.782854e-109 2.197466e-109 3.397100e-109 6.133698e-109
#>  [871] 2.069426e-109 4.245408e-109 9.024999e-109 3.821105e-109 3.012758e-109
#>  [876] 2.421804e-109 7.768798e-109 2.917439e-108 2.043391e-109 1.988320e-109
#>  [881] 5.884932e-109 3.357471e-109 9.382560e-109 2.176854e-109 5.260599e-109
#>  [886] 1.979878e-109 4.055816e-109 1.982339e-109 2.114619e-109 2.449608e-109
#>  [891] 1.191717e-108 2.393111e-109 3.123931e-109 2.003581e-109 1.971048e-109
#>  [896] 3.576435e-109 2.343170e-108 2.358521e-108 5.080982e-109 2.461812e-109
#>  [901] 3.188443e-108 2.938238e-109 9.845558e-109 5.931645e-109 1.009875e-108
#>  [906] 2.598845e-108 4.687850e-109 6.478374e-109 2.753338e-109 3.020194e-109
#>  [911] 2.152606e-109 3.691877e-109 3.310370e-109 8.628700e-109 3.193387e-107
#>  [916] 1.958258e-109 2.127249e-109 1.968740e-109 2.626454e-109 3.560532e-109
#>  [921] 3.735653e-109 3.137502e-109 7.664552e-109 2.062430e-109 2.084918e-109
#>  [926] 1.986064e-109 1.968421e-109 1.987204e-109 2.124803e-109 1.660186e-108
#>  [931] 3.919469e-109 5.350128e-109 6.616113e-109 1.852939e-107 3.211698e-109
#>  [936] 1.969113e-109 3.543570e-109 3.376059e-109 4.236960e-109 6.179872e-109
#>  [941] 2.080003e-109 6.318994e-109 2.001440e-109 2.985715e-109 5.111849e-108
#>  [946] 5.268317e-109 1.810856e-108 2.378112e-109 1.959128e-109 4.160533e-109
#>  [951] 2.188552e-109 5.527226e-109 2.459867e-109 2.852813e-109 2.663325e-109
#>  [956] 2.018614e-109 2.516195e-109 1.958339e-109 1.792844e-108 5.845693e-109
#>  [961] 1.141639e-108 3.808687e-109 3.825767e-109 6.412281e-109 1.985275e-108
#>  [966] 1.977347e-109 2.991622e-107 1.983360e-109 2.056256e-109 2.100788e-109
#>  [971] 2.181566e-109 2.123325e-109 2.011820e-109 1.837987e-108 7.487454e-109
#>  [976] 3.379462e-109 1.003802e-107 1.969026e-109 2.841476e-109 1.191829e-108
#>  [981] 2.472081e-109 7.668895e-109 2.149806e-109 1.112323e-108 2.351742e-109
#>  [986] 1.992252e-109 2.784875e-109 2.299434e-109 3.544914e-109 7.131832e-109
#>  [991] 2.857363e-109 3.446853e-109 5.966288e-108 1.165153e-108 1.963730e-109
#>  [996] 1.038567e-107 2.361689e-109 3.951653e-109 2.898299e-109 3.462102e-108
susie_get_niter(s)
#> [1] 5
susie_get_pip(s)
#>    [1] 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>   [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>   [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [334] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [371] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [408] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [445] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [482] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [519] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [556] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [593] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [630] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [667] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [704] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [741] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [778] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [815] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [852] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [889] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [926] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>  [963] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [1000] 0
susie_get_lfsr(s)
#>  [1] 0.0 0.0 0.0 0.0 0.2 0.2 0.2 0.2 0.2 0.2