From 34021ad1bd8e3eb477cf94b7d6240908b25bcb14 Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Tue, 14 Jan 2020 18:50:29 -0600 Subject: [PATCH] Add 'simple' prior method and release 0.9.0 --- DESCRIPTION | 2 +- R/compute_ss.R | 6 +- R/elbo_rss.R | 1 + R/estimate_residual_variance.R | 2 + R/set_R_attributes.R | 1 - R/set_X_attributes.R | 1 - R/single_effect_regression.R | 24 +- R/single_effect_regression_rss.R | 20 +- R/single_effect_regression_ss.R | 4 +- R/sparse_multiplication.R | 2 + R/susie.R | 7 +- R/susie_rss.R | 8 +- R/susie_ss.R | 4 +- R/susie_trendfilter.R | 5 +- R/susie_utils.R | 1 + R/update_each_effect.R | 2 +- R/update_each_effect_rss.R | 1 + R/update_each_effect_ss.R | 1 + README.md | 12 +- docs/LICENSE-text.html | 18 +- docs/articles/finemapping.html | 65 ++-- .../figure-html/unnamed-chunk-12-1.png | Bin 26428 -> 25464 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 26396 -> 25446 bytes .../figure-html/unnamed-chunk-15-1.png | Bin 25765 -> 24765 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 23845 -> 22850 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 28383 -> 27279 bytes .../finemapping_summary_statistics.html | 58 ++-- .../figure-html/unnamed-chunk-10-1.png | Bin 25658 -> 24632 bytes .../figure-html/unnamed-chunk-11-1.png | Bin 48279 -> 46065 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 23845 -> 22850 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 28383 -> 27279 bytes docs/articles/index.html | 18 +- docs/authors.html | 18 +- docs/index.html | 27 +- docs/pkgdown.css | 46 +-- docs/pkgdown.js | 14 +- docs/pkgdown.yml | 4 +- .../reference/SER_posterior_e_loglik_rss.html | 18 +- docs/reference/compute_MXt.html | 18 +- docs/reference/compute_Xty.html | 18 +- docs/reference/compute_ss.html | 30 +- .../reference/estimate_residual_variance.html | 18 +- .../estimate_residual_variance_ss.html | 18 +- docs/reference/index.html | 90 +---- docs/reference/loglik.html | 18 +- docs/reference/set_R_attributes.html | 24 +- docs/reference/single_effect_regression.html | 21 +- .../single_effect_regression_rss.html | 30 +- .../single_effect_regression_ss.html | 21 +- docs/reference/susie.html | 44 ++- docs/reference/susie_rss.html | 39 +-- docs/reference/susie_slim.html | 18 +- docs/reference/susie_suff_stat.html | 327 ++++++++++++++++++ docs/reference/update_each_effect.html | 18 +- docs/reference/update_each_effect_rss.html | 18 +- docs/reference/update_each_effect_ss.html | 18 +- man/SER_posterior_e_loglik_rss.Rd | 1 + man/compute_MXt.Rd | 1 + man/compute_Xty.Rd | 1 + man/compute_ss.Rd | 6 +- man/estimate_residual_variance.Rd | 1 + man/estimate_residual_variance_ss.Rd | 1 + man/loglik.Rd | 1 + man/single_effect_regression.Rd | 3 +- man/single_effect_regression_rss.Rd | 2 +- man/single_effect_regression_ss.Rd | 3 +- man/susie.Rd | 6 +- man/susie_rss.Rd | 7 +- man/susie_slim.Rd | 1 + man/susie_suff_stat.Rd | 12 +- man/update_each_effect.Rd | 1 + man/update_each_effect_rss.Rd | 1 + man/update_each_effect_ss.Rd | 1 + tests/testthat/test_prior_weights.R | 7 +- 74 files changed, 665 insertions(+), 569 deletions(-) create mode 100644 docs/reference/susie_suff_stat.html diff --git a/DESCRIPTION b/DESCRIPTION index bd2f3aae..31667906 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -10,7 +10,7 @@ Description: The package implements a simple new way to perform effects are highly sparse (e.g. <20 non-zero effects in the vector $b$), although it is also useful to more general applications. Date: 2020-01-14 -Version: 0.8.1.0571 +Version: 0.9.0 Authors@R: c(person("Gao","Wang",role=c("aut","cre"), email="gaow@uchicago.edu"), person("Yuxin","Zou",role="aut"), diff --git a/R/compute_ss.R b/R/compute_ss.R index 9b1e7cf6..4e0afe94 100644 --- a/R/compute_ss.R +++ b/R/compute_ss.R @@ -1,8 +1,8 @@ -#' @title Compute the needed summary statistics for `susie_ss` from the 'raw' data +#' @title Compute sufficient statistics from input data #' @param X an n by p matrix of covariates #' @param y an n vector #' @param standardize logical flag (default=TRUE) for whether to standardize columns of X to unit variance prior to fitting. -#' @return a list with the input for `susie_ss` +#' @return a list of sufficient statistics #' @importFrom methods as #' @export compute_ss = function(X, y, standardize = TRUE){ @@ -19,4 +19,4 @@ compute_ss = function(X, y, standardize = TRUE){ yty = sum(y^2) return(list(XtX = XtX, Xty = Xty, yty = yty, n = n)) -} +} \ No newline at end of file diff --git a/R/elbo_rss.R b/R/elbo_rss.R index 81a6a141..19639ace 100644 --- a/R/elbo_rss.R +++ b/R/elbo_rss.R @@ -65,6 +65,7 @@ get_ER2_rss = function(R,z,s){ #' @param r residuals #' @param Eb the posterior mean of b (p vector) (alpha * mu) #' @param Eb2 the posterior second moment of b (p vector) (alpha * mu2) +#' @keywords internal SER_posterior_e_loglik_rss = function(R, Sigma, r,Ez,Ez2){ eigenS = attr(Sigma,'eigenS') Dinv = 1/(eigenS$values) diff --git a/R/estimate_residual_variance.R b/R/estimate_residual_variance.R index f820834f..9eaa9723 100644 --- a/R/estimate_residual_variance.R +++ b/R/estimate_residual_variance.R @@ -2,6 +2,7 @@ #' @param X an n by p matrix of covariantes #' @param Y an n vector of data #' @param s a susie fit +#' @keywords internal estimate_residual_variance = function(X,Y,s){ n = nrow(X) return( (1/n)* get_ER2(X,Y,s) ) @@ -14,6 +15,7 @@ estimate_residual_variance = function(X,Y,s){ #' @param s a susie fit #' @param yty a scaler, Y'Y, where Y is centered to have mean 0 #' @param n sample size +#' @keywords internal estimate_residual_variance_ss = function(XtX,Xty,s,yty,n){ return( (1/n)* get_ER2_ss(XtX,Xty,s,yty) ) } diff --git a/R/set_R_attributes.R b/R/set_R_attributes.R index 08ac18fa..2d630841 100644 --- a/R/set_R_attributes.R +++ b/R/set_R_attributes.R @@ -3,7 +3,6 @@ #' @param r_tol tolerance level for eigen value check of positive semidefinite matrix of R #' @return R with attribute e.g. #' attr(R, 'eigenR') is the eigen decomposition of R. - set_R_attributes = function(R, r_tol){ if(is.null(attr(R, 'eigen'))) eigenR = eigen(R, symmetric = TRUE) diff --git a/R/set_X_attributes.R b/R/set_X_attributes.R index 164fda92..658c5db1 100644 --- a/R/set_X_attributes.R +++ b/R/set_X_attributes.R @@ -7,7 +7,6 @@ #' attr(X, 'scaled:scale') is a p vector of column standard deviations of X if scale=TRUE, a p vector of 1s otherwise. #' attr(X, 'd') is a p vector of column sums of X.standardized^2, #' where X.standardized is the matrix X centered by attr(X, 'scaled:center') and scaled by attr(X, 'scaled:scale'). - set_X_attributes = function(X, center = TRUE, scale = TRUE) { diff --git a/R/single_effect_regression.R b/R/single_effect_regression.R index e6dac12d..bac3543d 100644 --- a/R/single_effect_regression.R +++ b/R/single_effect_regression.R @@ -23,8 +23,7 @@ #' @importFrom stats uniroot #' @importFrom stats optim #' @importFrom Matrix colSums -#' -single_effect_regression = function(Y,X,V,residual_variance=1,prior_weights=NULL, optimize_V=c("none", "optim", "EM")){ +single_effect_regression = function(Y,X,V,residual_variance=1,prior_weights=NULL, optimize_V=c("none", "optim", "uniroot", "EM", "simple")){ optimize_V = match.arg(optimize_V) Xty = compute_Xty(X, Y) betahat = (1/attr(X, "d")) * Xty @@ -32,7 +31,7 @@ single_effect_regression = function(Y,X,V,residual_variance=1,prior_weights=NULL if (is.null(prior_weights)) prior_weights = rep(1/ncol(X), ncol(X)) - if(optimize_V=="optim") V=optimize_prior_variance(optimize_V, betahat, shat2, prior_weights, alpha=NULL, post_mean2=NULL) + if(optimize_V!="EM" && optimize_V!="none") V=optimize_prior_variance(optimize_V, betahat, shat2, prior_weights, alpha=NULL, post_mean2=NULL) lbf = dnorm(betahat,0,sqrt(V+shat2),log=TRUE) - dnorm(betahat,0,sqrt(shat2),log=TRUE) #log(bf) on each SNP @@ -64,14 +63,16 @@ est_V_uniroot = function(betahat, shat2, prior_weights){ } optimize_prior_variance = function(optimize_V, betahat, shat2, prior_weights, alpha=NULL, post_mean2=NULL){ - if(optimize_V=="optim"){ - lV = optim(par=log(max(c(betahat^2-shat2, 1), na.rm = TRUE)), fn=neg.loglik.logscale, betahat=betahat, shat2=shat2, prior_weights = prior_weights, method='Brent', lower = -30, upper = 15)$par - V = exp(lV) - }else if(optimize_V=="uniroot"){ - V = est_V_uniroot(betahat, shat2, prior_weights) - }else if(optimize_V=="EM"){ - V = sum(alpha*post_mean2) - }else stop('Invalid option for `optimize_V` method') + if (optimize_V != "simple") { + if(optimize_V=="optim"){ + lV = optim(par=log(max(c(betahat^2-shat2, 1), na.rm = TRUE)), fn=neg.loglik.logscale, betahat=betahat, shat2=shat2, prior_weights = prior_weights, method='Brent', lower = -30, upper = 15)$par + V = exp(lV) + }else if(optimize_V=="uniroot"){ + V = est_V_uniroot(betahat, shat2, prior_weights) + }else if(optimize_V=="EM"){ + V = sum(alpha*post_mean2) + }else stop('Invalid option for `optimize_V` method') + } if(loglik(0,betahat,shat2,prior_weights) >= loglik(V,betahat,shat2,prior_weights)) V=0 # set V exactly 0 if that beats the numerical value return(V) } @@ -81,6 +82,7 @@ optimize_prior_variance = function(optimize_V, betahat, shat2, prior_weights, al #' The log likelihood function for SER model (based on summary data betahat, shat2), as a function of prior variance V #' @importFrom Matrix colSums #' @importFrom stats dnorm +#' @keywords internal loglik = function(V,betahat,shat2,prior_weights) { lbf = dnorm(betahat,0,sqrt(V+shat2),log=TRUE) - dnorm(betahat,0,sqrt(shat2),log=TRUE) diff --git a/R/single_effect_regression_rss.R b/R/single_effect_regression_rss.R index 65c0b8f9..5ec558c6 100644 --- a/R/single_effect_regression_rss.R +++ b/R/single_effect_regression_rss.R @@ -25,13 +25,13 @@ #' \item{lbf_model}{(scalar) the loglikelihood for the total model minus the log-likelihood for the null model} #' #' -single_effect_regression_rss = function(z,Sigma,V=1,prior_weights=NULL,optimize_V=c("none", "optim", "EM")){ +single_effect_regression_rss = function(z,Sigma,V=1,prior_weights=NULL,optimize_V=c("none", "optim", "uniroot", "EM", "simple")){ p = length(z) shat2 = 1/attr(Sigma, 'RjSinvRj') if (is.null(prior_weights)) prior_weights = rep(1/p, p) - if(optimize_V=="optim"){ + if(optimize_V!="EM" && optimize_V!="none"){ V=optimize_prior_variance_rss(optimize_V, z, Sigma, prior_weights, alpha=NULL, post_mean2=NULL) } @@ -86,15 +86,17 @@ neg.loglik_z.logscale_rss = function(lV,z,Sigma,prior_weights){ } optimize_prior_variance_rss = function(optimize_V, z, Sigma, prior_weights, alpha=NULL, post_mean2=NULL){ - if(optimize_V=="optim"){ - lV = optim(par=log(max(c((z^2) - (1/attr(Sigma, 'RjSinvRj')), 1e-6), na.rm = TRUE)), + if (optimize_V != "simple") { + if(optimize_V=="optim"){ + lV = optim(par=log(max(c((z^2) - (1/attr(Sigma, 'RjSinvRj')), 1e-6), na.rm = TRUE)), fn=neg.loglik_z.logscale_rss, z=z, Sigma = Sigma, prior_weights = prior_weights, method='Brent', lower = -30, upper = 15)$par - V = exp(lV) - }else if(optimize_V=="EM"){ - V = sum(alpha*post_mean2) - }else stop('Invalid option for `optimize_V` method') + V = exp(lV) + }else if(optimize_V=="EM"){ + V = sum(alpha*post_mean2) + }else stop('Invalid option for `optimize_V` method') + } if(loglik_rss(0,z,Sigma,prior_weights) >= loglik_rss(V,z,Sigma,prior_weights)) V=0 # set V exactly 0 if that beats the numerical value return(V) -} +} \ No newline at end of file diff --git a/R/single_effect_regression_ss.R b/R/single_effect_regression_ss.R index 964ab64e..89afd026 100644 --- a/R/single_effect_regression_ss.R +++ b/R/single_effect_regression_ss.R @@ -23,14 +23,14 @@ #' @importFrom stats uniroot #' @importFrom stats optim #' -single_effect_regression_ss = function(Xty,dXtX,V=1,residual_variance=1,prior_weights=NULL,optimize_V=c("none", "optim", "EM")){ +single_effect_regression_ss = function(Xty,dXtX,V=1,residual_variance=1,prior_weights=NULL,optimize_V=c("none", "optim", "uniroot", "EM", "simple")){ optimize_V = match.arg(optimize_V) betahat = (1/dXtX) * Xty shat2 = residual_variance/dXtX if (is.null(prior_weights)) prior_weights = rep(1/length(dXtX), length(dXtX)) - if(optimize_V=="optim") V=optimize_prior_variance(optimize_V, betahat, shat2, prior_weights, alpha=NULL, post_mean2=NULL) + if(optimize_V!="EM" && optimize_V!="none") V=optimize_prior_variance(optimize_V, betahat, shat2, prior_weights, alpha=NULL, post_mean2=NULL) lbf = dnorm(betahat,0,sqrt(V+shat2),log=TRUE) - dnorm(betahat,0,sqrt(shat2),log=TRUE) #log(bf) on each SNP diff --git a/R/sparse_multiplication.R b/R/sparse_multiplication.R index 3f17dbd3..6592fa0a 100644 --- a/R/sparse_multiplication.R +++ b/R/sparse_multiplication.R @@ -24,6 +24,7 @@ compute_Xb = function(X, b){ #' @return a p vector #' @importFrom Matrix t #' @importFrom Matrix crossprod +#' @keywords internal compute_Xty = function(X, y){ cm = attr(X, 'scaled:center') csd = attr(X, 'scaled:scale') @@ -43,6 +44,7 @@ compute_Xty = function(X, y){ #' @param X an n by p unstandardized matrix with three attributes: attr(X, 'scaled:center'), attr(X, 'scaled:scale'), and attr(X, 'd') #' @return a L by n matrix #' @importFrom Matrix t +#' @keywords internal compute_MXt = function(M, X){ cm = attr(X, 'scaled:center') csd = attr(X, 'scaled:scale') diff --git a/R/susie.R b/R/susie.R index 5ceb5977..5f00127a 100644 --- a/R/susie.R +++ b/R/susie.R @@ -67,10 +67,11 @@ #' point for the optimization. If \code{estimate_prior_variance = FALSE} #' then the prior variance (for each of the \code{L} effects) is determined by the value #' supplied to \code{scaled_prior_variance}. -# #' #' @param estimate_prior_method The method used for estimating prior -#' variance. +#' variance. "simple" method only compares the loglikelihood between +#' using specified prior variance and using zero, and chose the one that +#' gives larger loglikelihood. #' #' @param s_init A previous susie fit with which to initialize. #' @@ -178,7 +179,7 @@ susie <- function(X,Y,L = min(10,ncol(X)),scaled_prior_variance = 0.2, standardize=TRUE,intercept=TRUE, estimate_residual_variance=TRUE, estimate_prior_variance = TRUE, - estimate_prior_method = c("optim","EM"), + estimate_prior_method = c("optim","EM","simple"), s_init = NULL,coverage=0.95,min_abs_corr=0.5, compute_univariate_zscore = FALSE, na.rm = FALSE, max_iter=100,tol=1e-3, diff --git a/R/susie_rss.R b/R/susie_rss.R index 26286d7f..651f86b9 100644 --- a/R/susie_rss.R +++ b/R/susie_rss.R @@ -19,7 +19,10 @@ #' @param restrict whether to restrict the resiudal variance between 0 and 1 #' @param estimate_residual_variance indicates whether to estimate residual variance #' @param estimate_prior_variance indicates whether to estimate prior -#' @param estimate_prior_method The method used for estimating prior variance, 'optim' or 'EM' +#' @param estimate_prior_method The method used for estimating prior +#' variance. "simple" method only compares the loglikelihood between +#' using specified prior variance and using zero, and chose the one that +#' gives larger loglikelihood. #' @param max_iter maximum number of iterations to perform #' @param s_init a previous susie fit with which to initialize #' @param intercept_value a value to assign to the intercept (since the intercept cannot be estimated from centered summary data). This @@ -50,7 +53,7 @@ susie_rss = function(z, R, maf=NULL, maf_thresh=0, restrict = TRUE, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, - estimate_prior_method = c("optim","EM"), + estimate_prior_method = c("optim","EM","simple"), max_iter=100,s_init = NULL, intercept_value=0, coverage=0.95, min_abs_corr=0.5, tol=1e-3, verbose=FALSE, track_fit = FALSE, check_R = TRUE, check_z = TRUE) { @@ -122,7 +125,6 @@ susie_rss = function(z, R, maf=NULL, maf_thresh=0, R = set_R_attributes(R, r_tol) # initialize susie fit s = init_setup_rss(p,L,prior_variance,residual_variance,prior_weights,null_weight,1) - if (!missing(s_init)) { s = modifyList(s, s_init) s = init_finalize_rss(s, R=R) diff --git a/R/susie_ss.R b/R/susie_ss.R index 4bd19a82..ac223bca 100644 --- a/R/susie_ss.R +++ b/R/susie_ss.R @@ -28,7 +28,7 @@ #' @param residual_variance the residual variance (defaults to variance of y) #' @param estimate_residual_variance indicates whether to estimate residual variance #' @param estimate_prior_variance indicates whether to estimate prior -#' @param estimate_prior_method The method used for estimating prior variance, 'optim' or 'EM' +#' @param estimate_prior_method The method used for estimating prior variance. #' @param r_tol tolerance level for eigen value check of positive semidefinite matrix of R. #' @param prior_weights a p vector of prior probability that each element is non-zero #' @param null_weight probability of no effect, for each single effect model @@ -85,7 +85,7 @@ susie_suff_stat = function(bhat, shat, R, n, var_y = 1, residual_variance=NULL, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, - estimate_prior_method = c("optim","EM"), + estimate_prior_method = c("optim","EM","simple"), r_tol = 1e-08, prior_weights = NULL, null_weight = NULL, standardize = TRUE, diff --git a/R/susie_trendfilter.R b/R/susie_trendfilter.R index ae6681ac..51aa70c9 100644 --- a/R/susie_trendfilter.R +++ b/R/susie_trendfilter.R @@ -69,7 +69,4 @@ susie_trendfilter = function(y, order=0, standardize=FALSE, use_mad=TRUE, ...){ #' @keywords internal estimate_mad_residual_variance = function(y){ return(0.5*(median(abs(diff(y))/0.6745)^2)) -} - - - +} \ No newline at end of file diff --git a/R/susie_utils.R b/R/susie_utils.R index 7bcd309a..0440e4b3 100644 --- a/R/susie_utils.R +++ b/R/susie_utils.R @@ -462,6 +462,7 @@ susie_get_objective <- function(res, all = FALSE, warning_tol = 1E-6) { #' @title Slim the result of fitted SuSiE model #' @param res a susie fit, the output of `susieR::susie()` +#' @keywords internal susie_slim = function(res){ list(alpha = res$alpha, niter = res$niter, V = res$V, sigma2 = res$sigma2) } diff --git a/R/update_each_effect.R b/R/update_each_effect.R index e5c452b0..beedbdbe 100644 --- a/R/update_each_effect.R +++ b/R/update_each_effect.R @@ -3,7 +3,7 @@ #' @param Y an n vector of response variable #' @param s a SuSiE fit #' @param estimate_prior_variance boolean indicating whether to estimate prior variance -#' +#' @keywords internal update_each_effect <- function (X, Y, s, estimate_prior_variance=FALSE, estimate_prior_method="optim") { if(estimate_prior_variance==FALSE) estimate_prior_method="none" diff --git a/R/update_each_effect_rss.R b/R/update_each_effect_rss.R index 8a00eb11..84524b60 100644 --- a/R/update_each_effect_rss.R +++ b/R/update_each_effect_rss.R @@ -5,6 +5,7 @@ #' @param Sigma sigma2*R + lambda I #' @param estimate_prior_variance boolean indicating whether to estimate prior variance #' @importFrom Matrix diag +#' @keywords internal update_each_effect_rss <- function (R, z, s_init, Sigma, estimate_prior_variance=FALSE,estimate_prior_method="optim") { if(estimate_prior_variance==FALSE) estimate_prior_method="none" diff --git a/R/update_each_effect_ss.R b/R/update_each_effect_ss.R index 4f9cc58c..ecfb16a2 100644 --- a/R/update_each_effect_ss.R +++ b/R/update_each_effect_ss.R @@ -5,6 +5,7 @@ #' @param estimate_prior_variance boolean indicating whether to estimate prior variance #' @param estimate_prior_method The method used for estimating prior variance, 'optim' or 'EM' #' @importFrom Matrix diag +#' @keywords internal update_each_effect_ss <- function (XtX, Xty, s_init, estimate_prior_variance=FALSE, estimate_prior_method="optim") { if(estimate_prior_variance==FALSE) estimate_prior_method="none" # Repeat for each effect to update diff --git a/README.md b/README.md index 9183182c..2e5e5570 100644 --- a/README.md +++ b/README.md @@ -45,7 +45,7 @@ To automatically retrieve and install `susieR` from this repository, with the vignettes included, run: ```R -devtools::install_github("stephenslab/susieR@0.8.0",build_vignettes = TRUE) +devtools::install_github("stephenslab/susieR@0.9.0",build_vignettes = TRUE) ``` ## Quick Start @@ -56,7 +56,7 @@ please visit https://stephenslab.github.io/susieR ## Developer notes -+ When any changes are made to `roxygen2` markup, simply run ++ When any changes are made to `roxygen2` markup, simply run `devtools::document()` to update package `NAMESPACE` and documentation files. @@ -65,12 +65,12 @@ following commands in the shell: ```bash R CMD build --resave-data --no-build-vignettes susieR - R CMD INSTALL susieR_0.8.1.0525.tar.gz - R CMD check --as-cran --ignore-vignettes susieR_0.8.1.0525.tar.gz + R CMD INSTALL susieR_0.9.0.tar.gz + R CMD check --as-cran --ignore-vignettes susieR_0.9.0.tar.gz ``` + Run `pkgdown::build_site()` to build the website. Getting `pkgdown` -to work properly can be frustrating due to numerous & fragile dependencies. +to work properly can be frustrating due to numerous & fragile dependencies. If `pkgdown` does not work for you out of the box you can use this `docker` command to run all vignettes and build the site: @@ -78,4 +78,4 @@ command to run all vignettes and build the site: docker run --rm --security-opt label:disable -t -P -w $PWD -v $PWD:$PWD \ -u $UID:${GROUPS[0]} -e HOME=/home/$USER -e USER=$USER gaow/susie \ R --slave -e "pkgdown::build_site(lazy=TRUE, examples=FALSE)" -``` +``` \ No newline at end of file diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index abe07cab..065f9414 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -46,7 +44,6 @@ - @@ -63,7 +60,7 @@ susieR - 0.8.1.545 + 0.9.0 @@ -91,7 +88,6 @@ -
@@ -127,23 +123,19 @@

License

- - - diff --git a/docs/articles/finemapping.html b/docs/articles/finemapping.html index e88d556b..acf95bb8 100644 --- a/docs/articles/finemapping.html +++ b/docs/articles/finemapping.html @@ -7,9 +7,8 @@ Fine-mapping examples • susieR - - - + + @@ -31,7 +30,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -60,14 +59,13 @@ -
@@ -60,14 +59,13 @@ -

@@ -125,7 +123,7 @@

estimate_prior_variance = TRUE)

Using summary function, we can examine the posterior inclusion probability (PIP) for each variable, and the 95% credible sets.

Here, we are the 95% credible sets.

-
summary(fitted_rss)$cs
+
summary(fitted_rss)$cs
 #   cs cs_log10bf cs_avg_r2 cs_min_r2
 # 1  2   8.415429 1.0000000 1.0000000
 # 2  1  14.908636 0.9816575 0.9634847
@@ -143,11 +141,11 @@ 

The true causal variables are colored red. The 95% CS identified are circled in different colors.

-

The result should be very similar to using the individual level data,

+

The result should be very similar to using the individual level data for this example, as the z-scores and correlation matrix (LD reference) come from the same genotype data-set.

fitted = susie(data$X, data$Y[,1], L = 10,
                 estimate_residual_variance = TRUE, 
                 estimate_prior_variance = TRUE)
-plot(fitted$pip, fitted_rss$pip, ylim=c(0,1))
+plot(fitted$pip, fitted_rss$pip, ylim=c(0,1))
&nbsp;

  @@ -158,10 +156,10 @@

Session information

Here are some details about the computing environment, including the versions of R, and the R packages, used to generate these results.

-
sessionInfo()
+
sessionInfo()
 # R version 3.5.3 (2019-03-11)
 # Platform: x86_64-pc-linux-gnu (64-bit)
-# Running under: LMDE 3 (cindy)
+# Running under: BunsenLabs GNU/Linux 8.9 (Hydrogen)
 # 
 # Matrix products: default
 # BLAS: /usr/lib/atlas-base/atlas/libblas.so.3.0
@@ -179,16 +177,17 @@ 

# [1] stats graphics grDevices utils datasets methods base # # other attached packages: -# [1] susieR_0.8.1.0545 +# [1] susieR_0.8.1.0571 # # loaded via a namespace (and not attached): -# [1] Rcpp_1.0.3 knitr_1.25 magrittr_1.5 MASS_7.3-51.1 -# [5] lattice_0.20-38 R6_2.4.1 rlang_0.4.1 stringr_1.4.0 -# [9] highr_0.8 tools_3.5.3 grid_3.5.3 xfun_0.9 -# [13] htmltools_0.3.6 yaml_2.2.0 assertthat_0.2.1 digest_0.6.22 -# [17] rprojroot_1.3-2 pkgdown_1.4.1 crayon_1.3.4 Matrix_1.2-16 -# [21] fs_1.3.0 memoise_1.1.0 evaluate_0.13 rmarkdown_1.15 -# [25] stringi_1.4.3 compiler_3.5.3 desc_1.2.0 backports_1.1.5

+# [1] Rcpp_1.0.3 knitr_1.23 xml2_1.2.0 magrittr_1.5 +# [5] roxygen2_6.1.1 MASS_7.3-51.1 lattice_0.20-38 R6_2.4.1 +# [9] rlang_0.4.2 highr_0.8 stringr_1.4.0 tools_3.5.3 +# [13] grid_3.5.3 xfun_0.7 htmltools_0.3.6 commonmark_1.7 +# [17] yaml_2.2.0 digest_0.6.23 assertthat_0.2.1 rprojroot_1.3-2 +# [21] pkgdown_1.3.0 crayon_1.3.4 Matrix_1.2-16 fs_1.2.7 +# [25] memoise_1.1.0 evaluate_0.13 rmarkdown_1.13 stringi_1.4.3 +# [29] compiler_3.5.3 desc_1.2.0 backports_1.1.5
@@ -208,20 +207,17 @@

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
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@@ -91,7 +88,6 @@ -
@@ -117,23 +113,19 @@

Vignettes

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/authors.html b/docs/authors.html index 11a713cd..f7a43870 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -46,7 +44,6 @@ - @@ -63,7 +60,7 @@ susieR - 0.8.1.545 + 0.9.0 @@ -91,7 +88,6 @@ -
@@ -128,23 +124,19 @@

Authors

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/index.html b/docs/index.html index e934d785..fed45e27 100644 --- a/docs/index.html +++ b/docs/index.html @@ -7,9 +7,8 @@ Fit Sum of Single Effects Linear Regression Model • susieR - - - + + susieR - 0.8.1.545 + 0.9.0 @@ -66,7 +65,6 @@ -
@@ -82,7 +80,7 @@

Setup

To automatically retrieve and install susieR from this repository, with the vignettes included, run:

-
devtools::install_github("stephenslab/susieR@0.8.0",build_vignettes = TRUE)
+
devtools::install_github("stephenslab/susieR@0.9.0",build_vignettes = TRUE)

@@ -93,17 +91,17 @@

Developer notes

    -
  • When any changes are made to roxygen2 markup, simply run devtools::document() to update package NAMESPACE and documentation files.

  • +
  • When any changes are made to roxygen2 markup, simply run devtools::document() to update package NAMESPACE and documentation files.

  • To install and test the development version of susieR, run the following commands in the shell:

    -
    R CMD build --resave-data --no-build-vignettes susieR
    -R CMD INSTALL susieR_0.8.1.0525.tar.gz
    -R CMD check --as-cran --ignore-vignettes susieR_0.8.1.0525.tar.gz
    +
    R CMD build --resave-data --no-build-vignettes susieR
    +R CMD INSTALL susieR_0.9.0.tar.gz
    +R CMD check --as-cran --ignore-vignettes susieR_0.9.0.tar.gz
  • Run pkgdown::build_site() to build the website. Getting pkgdown to work properly can be frustrating due to numerous & fragile dependencies. If pkgdown does not work for you out of the box you can use this docker command to run all vignettes and build the site:

-
docker run --rm --security-opt label:disable -t -P -w $PWD -v $PWD:$PWD \
-  -u $UID:${GROUPS[0]} -e HOME=/home/$USER -e USER=$USER gaow/susie \
+
docker run --rm --security-opt label:disable -t -P -w $PWD -v $PWD:$PWD \
+  -u $UID:${GROUPS[0]} -e HOME=/home/$USER -e USER=$USER gaow/susie \
   R --slave -e "pkgdown::build_site(lazy=TRUE, examples=FALSE)"
@@ -149,20 +147,17 @@

Dev status

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- diff --git a/docs/pkgdown.css b/docs/pkgdown.css index 91459581..c03fb08d 100644 --- a/docs/pkgdown.css +++ b/docs/pkgdown.css @@ -21,6 +21,8 @@ body > .container { display: flex; height: 100%; flex-direction: column; + + padding-top: 60px; } body > .container .row { @@ -100,13 +102,21 @@ a.anchor { margin-top: -40px; } +/* Static header placement on mobile devices */ +@media (max-width: 767px) { + .navbar-fixed-top { + position: absolute; + } + .navbar { + padding: 0; + } +} + + /* Sidebar --------------------------*/ #sidebar { margin-top: 30px; - position: -webkit-sticky; - position: sticky; - top: 70px; } #sidebar h2 { font-size: 1.5em; @@ -123,9 +133,6 @@ a.anchor { .orcid { height: 16px; - /* margins are required by official ORCID trademark and display guidelines */ - margin-left:4px; - margin-right:4px; vertical-align: middle; } @@ -215,19 +222,6 @@ a.sourceLine:hover { visibility: visible; } -/* headroom.js ------------------------ */ - -.headroom { - will-change: transform; - transition: transform 200ms linear; -} -.headroom--pinned { - transform: translateY(0%); -} -.headroom--unpinned { - transform: translateY(-100%); -} - /* mark.js ----------------------------*/ mark { @@ -240,17 +234,3 @@ mark { .html-widget { margin-bottom: 10px; } - -/* fontawesome ------------------------ */ - -.fab { - font-family: "Font Awesome 5 Brands" !important; -} - -/* don't display links in code chunks when printing */ -/* source: https://stackoverflow.com/a/10781533 */ -@media print { - code a:link:after, code a:visited:after { - content: ""; - } -} diff --git a/docs/pkgdown.js b/docs/pkgdown.js index 087a7622..eb7e83d2 100644 --- a/docs/pkgdown.js +++ b/docs/pkgdown.js @@ -2,12 +2,14 @@ (function($) { $(function() { - $('.navbar-fixed-top').headroom(); - - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); - }); + $("#sidebar") + .stick_in_parent({offset_top: 40}) + .on('sticky_kit:bottom', function(e) { + $(this).parent().css('position', 'static'); + }) + .on('sticky_kit:unbottom', function(e) { + $(this).parent().css('position', 'relative'); + }); $('body').scrollspy({ target: '#sidebar', diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index a5471584..5eb3648b 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -1,5 +1,5 @@ -pandoc: 1.17.2 -pkgdown: 1.4.1 +pandoc: 1.17.0.2 +pkgdown: 1.3.0 pkgdown_sha: ~ articles: finemapping: finemapping.html diff --git a/docs/reference/SER_posterior_e_loglik_rss.html b/docs/reference/SER_posterior_e_loglik_rss.html index 7175d1d1..9f038b36 100644 --- a/docs/reference/SER_posterior_e_loglik_rss.html +++ b/docs/reference/SER_posterior_e_loglik_rss.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -149,23 +145,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/compute_MXt.html b/docs/reference/compute_MXt.html index fa818cf0..307972c0 100644 --- a/docs/reference/compute_MXt.html +++ b/docs/reference/compute_MXt.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -143,23 +139,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/compute_Xty.html b/docs/reference/compute_Xty.html index f77bca19..243a0aa6 100644 --- a/docs/reference/compute_Xty.html +++ b/docs/reference/compute_Xty.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -143,23 +139,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/compute_ss.html b/docs/reference/compute_ss.html index 1815d891..b8e312f6 100644 --- a/docs/reference/compute_ss.html +++ b/docs/reference/compute_ss.html @@ -6,7 +6,7 @@ -Compute the needed summary statistics for `susie_ss` from the 'raw' data — compute_ss • susieR +Compute sufficient statistics from input data — compute_ss • susieR @@ -16,15 +16,13 @@ - - + - - - + + @@ -32,9 +30,9 @@ - + - + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,20 +91,19 @@ -
-

Compute the needed summary statistics for `susie_ss` from the 'raw' data

+

Compute sufficient statistics from input data

@@ -132,7 +128,7 @@

Arg

Value

-

a list with the input for `susie_ss`

+

a list of sufficient statistics

@@ -147,23 +143,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/estimate_residual_variance.html b/docs/reference/estimate_residual_variance.html index 377c1fe6..8e8efef8 100644 --- a/docs/reference/estimate_residual_variance.html +++ b/docs/reference/estimate_residual_variance.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -141,23 +137,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/estimate_residual_variance_ss.html b/docs/reference/estimate_residual_variance_ss.html index 9e9fe4ca..58d3237b 100644 --- a/docs/reference/estimate_residual_variance_ss.html +++ b/docs/reference/estimate_residual_variance_ss.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -149,23 +145,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/index.html b/docs/reference/index.html index 52d525b4..7f1b758c 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -46,7 +44,6 @@ - @@ -63,7 +60,7 @@ susieR - 0.8.1.545 + 0.9.0 @@ -91,7 +88,6 @@ -
@@ -117,12 +113,6 @@

SER_posterior_e_loglik_rss()

- -

posterior expected loglikelihood for a single effect regression

- -

check_projection()

@@ -141,18 +131,6 @@

compute_MXt()

- -

Computes M%*%t(standardized.X) using sparse multiplication trick

- - - -

compute_Xty()

- -

Computes t(standardized.X)%*%y using sparse multiplication trick

- -

compute_colSds()

@@ -163,25 +141,7 @@

compute_ss()

-

Compute the needed summary statistics for `susie_ss` from the 'raw' data

- - - -

estimate_residual_variance()

- -

Estimate residual variance

- - - -

estimate_residual_variance_ss()

- -

Estimate residual variance for summary statistics

- - - -

loglik()

- -

The log likelihood function for SER model (based on summary data betahat, shat2), as a function of prior variance V

+

Compute sufficient statistics from input data

@@ -238,12 +198,6 @@

susie_bhat()

- -

Summary statistics version of SuSiE on betahat, the corresponding standard error, and correlation (or covariance) matrix

- -

susie_get_cs()

@@ -323,15 +277,9 @@

susie_slim()

+

susie_suff_stat()

-

Slim the result of fitted SuSiE model

- - - -

susie_ss()

- -

Bayesian sum of single-effect (susie) linear regression using summary stat

+

Bayesian sum of single-effect (susie) linear regression using sufficient summary stat

@@ -339,24 +287,6 @@

update_each_effect()

- -

update each effect once

- - - -

update_each_effect_rss()

- -

update each effect once

- - - -

update_each_effect_ss()

- -

update each effect once

@@ -370,23 +300,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/loglik.html b/docs/reference/loglik.html index 652d33b3..e8ca088b 100644 --- a/docs/reference/loglik.html +++ b/docs/reference/loglik.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -123,23 +119,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/set_R_attributes.html b/docs/reference/set_R_attributes.html index a679227a..6f4684d4 100644 --- a/docs/reference/set_R_attributes.html +++ b/docs/reference/set_R_attributes.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -111,7 +107,7 @@

sets the attributes for the R matrix

-
set_R_attributes(R, expected_dim)
+
set_R_attributes(R, r_tol)

Arguments

@@ -121,8 +117,8 @@

Arg

- - + +

a p by p LD matrix

z

a p vector of z scores

r_tol

tolerance level for eigen value check of positive semidefinite matrix of R

@@ -144,23 +140,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/single_effect_regression.html b/docs/reference/single_effect_regression.html index 69067c70..4129a76d 100644 --- a/docs/reference/single_effect_regression.html +++ b/docs/reference/single_effect_regression.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -112,7 +108,8 @@

Bayesian single-effect linear regression of Y on X

single_effect_regression(Y, X, V, residual_variance = 1,
-  prior_weights = NULL, optimize_V = c("none", "optim", "EM"))
+ prior_weights = NULL, optimize_V = c("none", "optim", "uniroot", + "EM", "simple"))

Arguments

@@ -179,23 +176,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/single_effect_regression_rss.html b/docs/reference/single_effect_regression_rss.html index 71e2ace7..49ece2ae 100644 --- a/docs/reference/single_effect_regression_rss.html +++ b/docs/reference/single_effect_regression_rss.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -111,8 +107,8 @@

Bayesian single-effect linear regression using z scores.

-
single_effect_regression_rss(z, Sigma, V = 1, residual_variance = 1,
-  prior_weights = NULL, optimize_V = c("none", "optim", "EM"))
+
single_effect_regression_rss(z, Sigma, V = 1, prior_weights = NULL,
+  optimize_V = c("none", "optim", "uniroot", "EM", "simple"))

Arguments

@@ -129,10 +125,6 @@

Arg

- - - - @@ -158,13 +150,13 @@

Value

Details

Performs single-effect linear regression with z scores. That is, this function -fits the regression model z = Rb + e, where e is N(0,residual_variance * R) and the +fits the regression model z = Rb + e, where e is N(0,Sigma), Sigma = residual_var * R + lambda I, and the b is a p vector of effects to be estimated. The assumption is that b has exactly one non-zero element, with all elements equally likely to be non-zero. The prior on the non-zero element is N(0,var=V).

Performs sum of single-effect (susie) linear regression with z scores. The summary data required are the p by p correlation matrix R, the p vector z. The summary stats should come from the same individuals. -This function fits the regression model z = sum_l Rb_l + e, where e is N(0,residual_variance * R) and the +This function fits the regression model z = sum_l Rb_l + e, where e is N(0,Sigma) and the sum_l b_l is a p vector of effects to be estimated. The assumption is that each b_l has exactly one non-zero element, with all elements equally likely to be non-zero. The prior on the non-zero element is N(0,var=prior_variance).

@@ -184,23 +176,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/single_effect_regression_ss.html b/docs/reference/single_effect_regression_ss.html index 7240c498..3cadef82 100644 --- a/docs/reference/single_effect_regression_ss.html +++ b/docs/reference/single_effect_regression_ss.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -112,7 +108,8 @@

Bayesian single-effect linear regression of y on X.

single_effect_regression_ss(Xty, dXtX, V = 1, residual_variance = 1,
-  prior_weights = NULL, optimize_V = c("none", "optim", "EM"))
+ prior_weights=NULL, optimize_V=c("none", "optim", "uniroot", + "EM", "simple"))

Arguments

V

the prior variance

residual_variance

the residual variance

prior_weights

a p vector of prior weights

@@ -179,23 +176,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/susie.html b/docs/reference/susie.html index 46984a41..d1ead5b9 100644 --- a/docs/reference/susie.html +++ b/docs/reference/susie.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -59,7 +57,6 @@ - @@ -76,7 +73,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -104,7 +101,6 @@ -
@@ -131,11 +127,11 @@

SUm of Single Effects (SuSiE) Regression

-
susie(X, Y, L = min(10, ncol(X)), scaled_prior_variance = 0.2,
+    
susie(X, Y, L = min(10, ncol(X)), scaled_prior_variance = 0.2,
   residual_variance = NULL, prior_weights = NULL, null_weight = NULL,
   standardize = TRUE, intercept = TRUE,
   estimate_residual_variance = TRUE, estimate_prior_variance = TRUE,
-  estimate_prior_method = c("optim", "EM"), s_init = NULL,
+  estimate_prior_method = c("optim", "EM", "simple"), s_init = NULL,
   coverage = 0.95, min_abs_corr = 0.5,
   compute_univariate_zscore = FALSE, na.rm = FALSE, max_iter = 100,
   tol = 0.001, verbose = FALSE, track_fit = FALSE)
@@ -171,7 +167,7 @@

Arg

+var(Y).

@@ -222,7 +218,9 @@

Arg

+variance. "simple" method only compares the loglikelihood between +using specified prior variance and using zero, and chose the one that +gives larger loglikelihood.

@@ -330,17 +328,19 @@

R

Examples

-
-set.seed(1)
+    
# NOT RUN {
+set.seed(1)
 n = 1000
 p = 1000
-beta = rep(0,p)
+beta = rep(0,p)
 beta[1:4] = 1
-X = matrix(rnorm(n*p),nrow=n,ncol=p)
-y = X %*% beta + rnorm(n)
+X = matrix(rnorm(n*p),nrow=n,ncol=p)
+y = X %*% beta + rnorm(n)
 res = susie(X,y,L=10)
-coef(res)
-plot(y,predict(res))
+coef(res) +plot(y,predict(res)) + +# }
-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/susie_rss.html b/docs/reference/susie_rss.html index 4d5c82d1..71b1f774 100644 --- a/docs/reference/susie_rss.html +++ b/docs/reference/susie_rss.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -115,10 +111,10 @@

Bayesian sum of single-effect (SuSiE) linear regression using z scores

prior_variance = 50, residual_variance = NULL, r_tol = 1e-08, prior_weights = NULL, null_weight = NULL, restrict = TRUE, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, - estimate_prior_method = c("optim", "EM"), max_iter = 100, + estimate_prior_method = c("optim", "EM", "simple"), max_iter = 100, s_init = NULL, intercept_value = 0, coverage = 0.95, min_abs_corr = 0.5, tol = 0.001, verbose = FALSE, - track_fit = FALSE, check_input = FALSE) + track_fit = FALSE, check_R = TRUE, check_z = TRUE)

Arguments

The variance of the residual. If estimate_residual_variance = TRUE, this value simply provides the initial estimate of the residual variance. By default, it is set to -var(Y).

prior_weights
estimate_prior_method

The method used for estimating prior -variance.

s_init
@@ -181,7 +177,10 @@

Arg

- + @@ -207,7 +206,7 @@

Arg

- + @@ -218,10 +217,12 @@

Arg

- - + + + + + +
estimate_prior_method

The method used for estimating prior variance, 'optim' or 'EM'

The method used for estimating prior +variance. "simple" method only compares the loglikelihood between +using specified prior variance and using zero, and chose the one that +gives larger loglikelihood.

max_iter
tol

convergence tolerance based on alpha

convergence tolerance based on elbo

verbose

add an attribute trace to output that saves current values of all iterations

check_input

whether to skip the checks for R and z, the checks are:

-

1. Check whether R is positive semidefinite

-

2. Check whether z in space spanned by the non-zero eigenvectors of R

check_R

check whether R is positive semidefinite

check_z

check whether z in space spanned by the non-zero eigenvectors of R

@@ -260,23 +261,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/susie_slim.html b/docs/reference/susie_slim.html index 67fff7f7..91f15aee 100644 --- a/docs/reference/susie_slim.html +++ b/docs/reference/susie_slim.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -133,23 +129,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/susie_suff_stat.html b/docs/reference/susie_suff_stat.html new file mode 100644 index 00000000..9f574f5d --- /dev/null +++ b/docs/reference/susie_suff_stat.html @@ -0,0 +1,327 @@ + + + + + + + + +Bayesian sum of single-effect (susie) linear regression using sufficient summary stat — susie_suff_stat • susieR + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + + +
+ +
+
+ + +
+ +

Bayesian sum of single-effect (susie) linear regression using sufficient summary stat

+ +
+ +
susie_suff_stat(bhat, shat, R, n, var_y = 1, XtX, Xty, yty, maf = NULL,
+  maf_thresh = 0, L = 10, scaled_prior_variance = 0.2,
+  residual_variance = NULL, estimate_residual_variance = TRUE,
+  estimate_prior_variance = TRUE, estimate_prior_method = c("optim",
+  "EM", "simple"), r_tol = 1e-08, prior_weights = NULL,
+  null_weight = NULL, standardize = TRUE, max_iter = 100,
+  s_init = NULL, intercept_value = 0, coverage = 0.95,
+  min_abs_corr = 0.5, tol = 0.001, verbose = FALSE,
+  track_fit = FALSE, check_input = FALSE)
+ +

Arguments

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
bhat

a p vector of estimated effects.

shat

a p vector of corresponding standard errors.

R

a p by p symmetric and positive semidefinite matrix. It can be X'X, covariance matrix (X'X/(n-1)) or correlation matrix. +It should from the same samples used to compute `bhat` and `shat`. Using out of sample matrix may produce unreliable results.

n

sample size.

var_y

the (sample) variance of y, defined as y'y/(n-1) . If it is unknown, the coefficients (returned from `coef`) are on the standardized X, y scale.

XtX

a p by p matrix, X'X, where columns of X are centered to have mean 0

Xty

a p vector, X'y, where columns of X are centered and y is centered to have mean 0

yty

a scaler, y'y, where y is centered to have mean 0

maf

minor allele frequency; to be used along with `maf_thresh` to filter input summary statistics

maf_thresh

variants having MAF smaller than this threshold will be filtered out

L

maximum number of non-zero effects

scaled_prior_variance

the scaled prior variance (vector of length L, or scalar. In latter case gets repeated L times)

residual_variance

the residual variance (defaults to variance of y)

estimate_residual_variance

indicates whether to estimate residual variance

estimate_prior_variance

indicates whether to estimate prior

estimate_prior_method

The method used for estimating prior variance.

r_tol

tolerance level for eigen value check of positive semidefinite matrix of R.

prior_weights

a p vector of prior probability that each element is non-zero

null_weight

probability of no effect, for each single effect model

standardize

logical flag (default=TRUE) for whether to adjust XtX and Xty such that they are computed from column standardized X, prior to fitting. +Note that `scaled_prior_variance` specifies the prior on the coefficients of X *after* standardization (if performed). +If you do not standardize you may need +to think more carefully about specifying +`scaled_prior_variance`. Whatever the value of standardize, the coefficients (returned from `coef`) are for X on the original input scale.

max_iter

maximum number of iterations to perform

s_init

a previous susie fit with which to initialize

intercept_value

a value to assign to the intercept (since the intercept cannot be estimated from centered summary data). This +value will be used by coef.susie() to assign an intercept value, for consistency with the non-summary-statistic version of this function susie. +Set to NULL if you want coef.susie() not to include an intercept term (and so only return a p vector).

coverage

coverage of confident sets. Default to 0.95 for 95% credible interval.

min_abs_corr

minimum of absolute value of correlation allowed in a credible set.

tol

convergence tolerance based on alpha

verbose

if true outputs some progress messages

track_fit

add an attribute trace to output that saves current values of all iterations

check_input

whether to perform checks on XtX and Xty, the checks are:

+

1. Check whether XtX is positive semidefinite

+

2. Check whether Xty in space spanned by the non-zero eigenvectors of XtX

+ +

Value

+ +

a susie fit, which is a list with some or all of the following elements

+
alpha

an L by p matrix of posterior inclusion probabilites

+
mu

an L by p matrix of posterior means (conditional on inclusion)

+
mu2

an L by p matrix of posterior second moments (conditional on inclusion)

+
XtXr

an p vector of t(X) times fitted values, the fitted values equal to X times colSums(alpha*mu))

+
sigma2

residual variance

+
V

prior variance

+ + +

Details

+ +

Performs sum of single-effect (susie) linear regression of y on X when +only sufficient summary statistics are available. The sufficient summary data required are EITHER +the p vector bhat, the p vector shat, the p by p symmetric and positive semidefinite correlation +(or covariance) matrix R, the sample size n, the variance of y; OR +the p by p matrix X'X, the p vector X'y, the sum of squares of y (y'y) and the sample size. +The sufficient summary stats should come from the same individuals. +Both the columns of X and the vector y +should be centered to have mean 0 before +computing these summary statistics; you may also want to scale each column of X and y to have variance 1 (see examples). +This function fits the regression model y = sum_l Xb_l + e, where elements of e are iid N(0,var=residual_variance) and the +sum_l b_l is a p vector of effects to be estimated. +The assumption is that each b_l has exactly one non-zero element, with all elements +equally likely to be non-zero. The prior on the non-zero element is N(0,var=scaled_prior_variance*y'y/(n-1)).

+ + +

Examples

+
# NOT RUN {
+set.seed(1)
+n    <- 1000
+p    <- 1000
+beta <- rep(0,p)
+beta[1:4] <- 1
+X        <- matrix(rnorm(n*p),nrow=n,ncol=p)
+y        <- c(X %*% beta + rnorm(n))
+input_ss <- compute_ss(X,y,standardize = TRUE)
+ss = susieR:::univariate_regression(X, y)
+R = with(input_ss, cov2cor(XtX))
+res1      <- with(input_ss,susie_suff_stat(XtX = XtX,Xty = Xty, yty = yty,n=n))
+coef(res1)
+res2      <- with(ss,susie_suff_stat(bhat = betahat, shat = sebetahat, R = R, n=n, var_y = var(y)))
+coef(res2)
+
+# }
+
+ +
+ +
+ + +
+

Site built with pkgdown 1.3.0.

+
+
+
+ + + + + + diff --git a/docs/reference/update_each_effect.html b/docs/reference/update_each_effect.html index 7fce6102..eea7f754 100644 --- a/docs/reference/update_each_effect.html +++ b/docs/reference/update_each_effect.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -146,23 +142,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/update_each_effect_rss.html b/docs/reference/update_each_effect_rss.html index de173bf9..6feb6874 100644 --- a/docs/reference/update_each_effect_rss.html +++ b/docs/reference/update_each_effect_rss.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -150,23 +146,19 @@

Contents

-
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.3.0.

-
- - diff --git a/docs/reference/update_each_effect_ss.html b/docs/reference/update_each_effect_ss.html index 10aaad21..1cb2569e 100644 --- a/docs/reference/update_each_effect_ss.html +++ b/docs/reference/update_each_effect_ss.html @@ -16,15 +16,13 @@ - - + - - - + + @@ -49,7 +47,6 @@ - @@ -66,7 +63,7 @@ susieR - 0.8.1.545 + 0.9.0.0 @@ -94,7 +91,6 @@ -
@@ -150,23 +146,19 @@

Contents

- - - diff --git a/man/SER_posterior_e_loglik_rss.Rd b/man/SER_posterior_e_loglik_rss.Rd index 798b0903..1c2c32cc 100644 --- a/man/SER_posterior_e_loglik_rss.Rd +++ b/man/SER_posterior_e_loglik_rss.Rd @@ -20,3 +20,4 @@ SER_posterior_e_loglik_rss(R, Sigma, r, Ez, Ez2) \description{ posterior expected loglikelihood for a single effect regression } +\keyword{internal} diff --git a/man/compute_MXt.Rd b/man/compute_MXt.Rd index 6d0cee85..ddb2189b 100644 --- a/man/compute_MXt.Rd +++ b/man/compute_MXt.Rd @@ -17,3 +17,4 @@ a L by n matrix \description{ Computes M\%*\%t(standardized.X) using sparse multiplication trick } +\keyword{internal} diff --git a/man/compute_Xty.Rd b/man/compute_Xty.Rd index eb615256..9ba60ea7 100644 --- a/man/compute_Xty.Rd +++ b/man/compute_Xty.Rd @@ -17,3 +17,4 @@ a p vector \description{ Computes t(standardized.X)\%*\%y using sparse multiplication trick } +\keyword{internal} diff --git a/man/compute_ss.Rd b/man/compute_ss.Rd index 7037c4e7..34ab9a77 100644 --- a/man/compute_ss.Rd +++ b/man/compute_ss.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/compute_ss.R \name{compute_ss} \alias{compute_ss} -\title{Compute the needed summary statistics for `susie_ss` from the 'raw' data} +\title{Compute sufficient statistics from input data} \usage{ compute_ss(X, y, standardize = TRUE) } @@ -14,8 +14,8 @@ compute_ss(X, y, standardize = TRUE) \item{standardize}{logical flag (default=TRUE) for whether to standardize columns of X to unit variance prior to fitting.} } \value{ -a list with the input for `susie_ss` +a list of sufficient statistics } \description{ -Compute the needed summary statistics for `susie_ss` from the 'raw' data +Compute sufficient statistics from input data } diff --git a/man/estimate_residual_variance.Rd b/man/estimate_residual_variance.Rd index da2fb0a8..a4408e0e 100644 --- a/man/estimate_residual_variance.Rd +++ b/man/estimate_residual_variance.Rd @@ -16,3 +16,4 @@ estimate_residual_variance(X, Y, s) \description{ Estimate residual variance } +\keyword{internal} diff --git a/man/estimate_residual_variance_ss.Rd b/man/estimate_residual_variance_ss.Rd index 50451239..94a2dd93 100644 --- a/man/estimate_residual_variance_ss.Rd +++ b/man/estimate_residual_variance_ss.Rd @@ -20,3 +20,4 @@ estimate_residual_variance_ss(XtX, Xty, s, yty, n) \description{ Estimate residual variance for summary statistics } +\keyword{internal} diff --git a/man/loglik.Rd b/man/loglik.Rd index 66e07cca..a9d570f8 100644 --- a/man/loglik.Rd +++ b/man/loglik.Rd @@ -9,3 +9,4 @@ loglik(V, betahat, shat2, prior_weights) \description{ The log likelihood function for SER model (based on summary data betahat, shat2), as a function of prior variance V } +\keyword{internal} diff --git a/man/single_effect_regression.Rd b/man/single_effect_regression.Rd index 232e10f2..7a94e30d 100644 --- a/man/single_effect_regression.Rd +++ b/man/single_effect_regression.Rd @@ -5,7 +5,8 @@ \title{Bayesian single-effect linear regression of Y on X} \usage{ single_effect_regression(Y, X, V, residual_variance = 1, - prior_weights = NULL, optimize_V = c("none", "optim", "EM")) + prior_weights = NULL, optimize_V = c("none", "optim", "uniroot", + "EM", "simple")) } \arguments{ \item{Y}{an n vector} diff --git a/man/single_effect_regression_rss.Rd b/man/single_effect_regression_rss.Rd index 3a69924c..82a75bce 100644 --- a/man/single_effect_regression_rss.Rd +++ b/man/single_effect_regression_rss.Rd @@ -5,7 +5,7 @@ \title{Bayesian single-effect linear regression using z scores.} \usage{ single_effect_regression_rss(z, Sigma, V = 1, prior_weights = NULL, - optimize_V = c("none", "optim", "EM")) + optimize_V = c("none", "optim", "uniroot", "EM", "simple")) } \arguments{ \item{z}{a p vector} diff --git a/man/single_effect_regression_ss.Rd b/man/single_effect_regression_ss.Rd index 5ca1eb13..d2228f56 100644 --- a/man/single_effect_regression_ss.Rd +++ b/man/single_effect_regression_ss.Rd @@ -5,7 +5,8 @@ \title{Bayesian single-effect linear regression of y on X.} \usage{ single_effect_regression_ss(Xty, dXtX, V = 1, residual_variance = 1, - prior_weights = NULL, optimize_V = c("none", "optim", "EM")) + prior_weights = NULL, optimize_V = c("none", "optim", "uniroot", + "EM", "simple")) } \arguments{ \item{Xty}{a p vector} diff --git a/man/susie.Rd b/man/susie.Rd index 3a86b461..211a8852 100644 --- a/man/susie.Rd +++ b/man/susie.Rd @@ -8,7 +8,7 @@ susie(X, Y, L = min(10, ncol(X)), scaled_prior_variance = 0.2, residual_variance = NULL, prior_weights = NULL, null_weight = NULL, standardize = TRUE, intercept = TRUE, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, - estimate_prior_method = c("optim", "EM"), s_init = NULL, + estimate_prior_method = c("optim", "EM", "simple"), s_init = NULL, coverage = 0.95, min_abs_corr = 0.5, compute_univariate_zscore = FALSE, na.rm = FALSE, max_iter = 100, tol = 0.001, verbose = FALSE, track_fit = FALSE) @@ -69,7 +69,9 @@ then the prior variance (for each of the \code{L} effects) is determined by the supplied to \code{scaled_prior_variance}.} \item{estimate_prior_method}{The method used for estimating prior -variance.} +variance. "simple" method only compares the loglikelihood between +using specified prior variance and using zero, and chose the one that +gives larger loglikelihood.} \item{s_init}{A previous susie fit with which to initialize.} diff --git a/man/susie_rss.Rd b/man/susie_rss.Rd index c0d4f827..9e5bcca6 100644 --- a/man/susie_rss.Rd +++ b/man/susie_rss.Rd @@ -8,7 +8,7 @@ susie_rss(z, R, maf = NULL, maf_thresh = 0, L = 10, lambda = 0, prior_variance = 50, residual_variance = NULL, r_tol = 1e-08, prior_weights = NULL, null_weight = NULL, restrict = TRUE, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, - estimate_prior_method = c("optim", "EM"), max_iter = 100, + estimate_prior_method = c("optim", "EM", "simple"), max_iter = 100, s_init = NULL, intercept_value = 0, coverage = 0.95, min_abs_corr = 0.5, tol = 0.001, verbose = FALSE, track_fit = FALSE, check_R = TRUE, check_z = TRUE) @@ -42,7 +42,10 @@ susie_rss(z, R, maf = NULL, maf_thresh = 0, L = 10, lambda = 0, \item{estimate_prior_variance}{indicates whether to estimate prior} -\item{estimate_prior_method}{The method used for estimating prior variance, 'optim' or 'EM'} +\item{estimate_prior_method}{The method used for estimating prior +variance. "simple" method only compares the loglikelihood between +using specified prior variance and using zero, and chose the one that +gives larger loglikelihood.} \item{max_iter}{maximum number of iterations to perform} diff --git a/man/susie_slim.Rd b/man/susie_slim.Rd index 26fea4ee..625aa7e2 100644 --- a/man/susie_slim.Rd +++ b/man/susie_slim.Rd @@ -12,3 +12,4 @@ susie_slim(res) \description{ Slim the result of fitted SuSiE model } +\keyword{internal} diff --git a/man/susie_suff_stat.Rd b/man/susie_suff_stat.Rd index 649726fd..5ee08778 100644 --- a/man/susie_suff_stat.Rd +++ b/man/susie_suff_stat.Rd @@ -8,11 +8,11 @@ susie_suff_stat(bhat, shat, R, n, var_y = 1, XtX, Xty, yty, maf = NULL, maf_thresh = 0, L = 10, scaled_prior_variance = 0.2, residual_variance = NULL, estimate_residual_variance = TRUE, estimate_prior_variance = TRUE, estimate_prior_method = c("optim", - "EM"), r_tol = 1e-08, prior_weights = NULL, null_weight = NULL, - standardize = TRUE, max_iter = 100, s_init = NULL, - intercept_value = 0, coverage = 0.95, min_abs_corr = 0.5, - tol = 0.001, verbose = FALSE, track_fit = FALSE, - check_input = FALSE) + "EM", "simple"), r_tol = 1e-08, prior_weights = NULL, + null_weight = NULL, standardize = TRUE, max_iter = 100, + s_init = NULL, intercept_value = 0, coverage = 0.95, + min_abs_corr = 0.5, tol = 0.001, verbose = FALSE, + track_fit = FALSE, check_input = FALSE) } \arguments{ \item{bhat}{a p vector of estimated effects.} @@ -46,7 +46,7 @@ It should from the same samples used to compute `bhat` and `shat`. Using out of \item{estimate_prior_variance}{indicates whether to estimate prior} -\item{estimate_prior_method}{The method used for estimating prior variance, 'optim' or 'EM'} +\item{estimate_prior_method}{The method used for estimating prior variance.} \item{r_tol}{tolerance level for eigen value check of positive semidefinite matrix of R.} diff --git a/man/update_each_effect.Rd b/man/update_each_effect.Rd index 2db58f4b..89875ddf 100644 --- a/man/update_each_effect.Rd +++ b/man/update_each_effect.Rd @@ -19,3 +19,4 @@ update_each_effect(X, Y, s, estimate_prior_variance = FALSE, \description{ update each effect once } +\keyword{internal} diff --git a/man/update_each_effect_rss.Rd b/man/update_each_effect_rss.Rd index 9436b23f..aa67032d 100644 --- a/man/update_each_effect_rss.Rd +++ b/man/update_each_effect_rss.Rd @@ -21,3 +21,4 @@ update_each_effect_rss(R, z, s_init, Sigma, \description{ update each effect once } +\keyword{internal} diff --git a/man/update_each_effect_ss.Rd b/man/update_each_effect_ss.Rd index d5b8d169..db10d05e 100644 --- a/man/update_each_effect_ss.Rd +++ b/man/update_each_effect_ss.Rd @@ -21,3 +21,4 @@ update_each_effect_ss(XtX, Xty, s_init, estimate_prior_variance = FALSE, \description{ update each effect once } +\keyword{internal} diff --git a/tests/testthat/test_prior_weights.R b/tests/testthat/test_prior_weights.R index 6b4f7e2b..9a540fb1 100644 --- a/tests/testthat/test_prior_weights.R +++ b/tests/testthat/test_prior_weights.R @@ -27,8 +27,9 @@ test_that("Sufficient stat (beta): prior weights specification agrees with defau test_that("RSS: prior weights specification agrees with default", with(simulate(200,500), { ss = univariate_regression(X, y) R = cor(X) + set.seed(9) # otherwise it might fail because the two results will be slighly different res1 = susie_rss(z = ss$betahat/ss$sebetahat, R = R, estimate_prior_variance = TRUE, check_z = FALSE) - res2 = susie_rss(z = ss$betahat/ss$sebetahat, R = R, estimate_prior_variance = TRUE, - prior_weights = rep(1/ncol(R), ncol(R)), check_z = FALSE) + res2 = susie_rss(z = ss$betahat/ss$sebetahat, R = R, estimate_prior_variance = TRUE, check_z = FALSE, + prior_weights = rep(1/ncol(R), ncol(R))) expect_equal_susie_suff_stat(res1,res2) -})) +})) \ No newline at end of file