From 4ca15ee150a12b53b8fa3562104e8bfda0027553 Mon Sep 17 00:00:00 2001 From: "David A. Knowles" Date: Tue, 5 Sep 2017 15:36:25 -0700 Subject: [PATCH] Rebuilt C++ --- leafcutter/man/differential_splicing.Rd | 5 ++++- leafcutter/man/dirichlet_multinomial_anova_mc.Rd | 7 +++++-- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/leafcutter/man/differential_splicing.Rd b/leafcutter/man/differential_splicing.Rd index 1d20156..1147694 100644 --- a/leafcutter/man/differential_splicing.Rd +++ b/leafcutter/man/differential_splicing.Rd @@ -6,7 +6,8 @@ \usage{ differential_splicing(counts, x, confounders = NULL, max_cluster_size = 10, min_samples_per_intron = 5, min_samples_per_group = 4, - min_coverage = 20, timeout = 10, robust = F, debug = F) + min_coverage = 20, timeout = 10, robust = F, debug = F, + init = "smart", ...) } \arguments{ \item{counts}{An [introns] x [samples] matrix of counts. The rownames must be of the form chr:start:end:cluid. If the counts file comes from the leafcutter clustering code this should be the case already.} @@ -28,6 +29,8 @@ differential_splicing(counts, x, confounders = NULL, max_cluster_size = 10, \item{robust}{Whether to use the robust model (explicitly models outliers). Generally not required/recommended for differential splicing.} \item{debug}{If true writes more output} + +\item{init}{One of 'smart' (default) or 'random'. If 'random' you can pass an additional arg "seed" for reproducibility.} } \value{ A per cluster list of results. Clusters that were not tested will be represented by a string saying why. diff --git a/leafcutter/man/dirichlet_multinomial_anova_mc.Rd b/leafcutter/man/dirichlet_multinomial_anova_mc.Rd index 2f07d34..c985fef 100644 --- a/leafcutter/man/dirichlet_multinomial_anova_mc.Rd +++ b/leafcutter/man/dirichlet_multinomial_anova_mc.Rd @@ -6,7 +6,8 @@ \usage{ dirichlet_multinomial_anova_mc(xFull, xNull, y, concShape = 1.0001, concRate = 1e-04, robust = T, outlier_prior_a = 1.01, - outlier_prior_b = 100, fit_null = NULL, debug = F, ...) + outlier_prior_b = 100, fit_null = NULL, debug = F, init = "smart", + ...) } \arguments{ \item{xFull}{[samples] x [covariates] matrix for the alternative model} @@ -29,7 +30,9 @@ dirichlet_multinomial_anova_mc(xFull, xNull, y, concShape = 1.0001, \item{debug}{Whether to give verbose output from rstan.} -\item{...}{will be passed on the rstan::optimizing, so can be used for example to set the algorithm used (default is LBFGS).} +\item{init}{Can be one of {"smart", "random"}. smart uses an method of moments estimator to get a reasonable initialization. The seed for "random" can be set through the ... arguments passed to rstan::optimizing.} + +\item{...}{will be passed on the rstan::optimizing, so can be used for example to set the algorithm used (default is LBFGS) or the random seed if random initialization is requested.} } \description{ Dirichlet multinomial GLM likelihood ratio test for a single cluster