mash: Apply mash method to data

In mashr: Multivariate Adaptive Shrinkage

Apply mash method to data

Description

Apply mash method to data

Usage

mash(
  data,
  Ulist = NULL,
  gridmult = sqrt(2),
  grid = NULL,
  normalizeU = TRUE,
  usepointmass = TRUE,
  g = NULL,
  fixg = FALSE,
  prior = c("nullbiased", "uniform"),
  nullweight = 10,
  optmethod = c("mixSQP", "mixIP", "mixEM", "cxxMixSquarem"),
  control = list(),
  verbose = TRUE,
  add.mem.profile = FALSE,
  algorithm.version = c("Rcpp", "R"),
  pi_thresh = 1e-10,
  A = NULL,
  posterior_samples = 0,
  seed = 123,
  outputlevel = 2,
  output_lfdr = FALSE
)

Arguments

data	a mash data object containing the Bhat matrix, standard errors, alpha value; created using mash_set_data or mash_set_data_contrast
Ulist	a list of covariance matrices to use (see normalizeU for rescaling these matrices)
gridmult	scalar indicating factor by which adjacent grid values should differ; close to 1 for fine grid
grid	vector of grid values to use (scaling factors omega in paper)
normalizeU	whether or not to normalize the U covariances to have maximum of 1 on diagonal
usepointmass	whether to include a point mass at 0, corresponding to null in every condition
g	the value of g obtained from a previous mash fit - an alternative to supplying Ulist, grid and usepointmass
fixg	if g is supplied, allows the mixture proportions to be fixed rather than estimated; e.g., useful for fitting mash to test data after fitting it to training data
prior	indicates what penalty to use on the likelihood, if any
nullweight	scalar, the weight put on the prior under “nullbiased” specification, see “prior”.
optmethod	name of optimization method to use
control	A list of control parameters passed to optmethod.
verbose	If TRUE, print progress to R console.
add.mem.profile	If TRUE, print memory usage to R console (requires R library 'profmem').
algorithm.version	Indicates whether to use R or Rcpp version
pi_thresh	threshold below which mixture components are ignored in computing posterior summaries (to speed calculations by ignoring negligible components)
A	the linear transformation matrix, Q x R matrix. This is used to compute the posterior for Ab.
posterior_samples	the number of samples to be drawn from the posterior distribution of each effect.
seed	A random number seed to use when sampling from the posteriors. It is used when posterior_samples > 0.
outputlevel	controls amount of computation / output; 1: output only estimated mixture component proportions, 2: and posterior estimates, 3: and posterior covariance matrices, 4: and likelihood matrices
output_lfdr	If output_lfdr = TRUE, output local false discovery rate estimates. The lfdr tends to be sensitive to mis-estimated covariance matrices, and generally we do not recommend using them; we recommend using the local false sign rate (lfsr) instead, which is always returned, even when output_lfdr = TRUE.

Value

a list with elements result, loglik and fitted_g

Examples

Bhat     = matrix(rnorm(100),ncol=5) # create some simulated data
Shat     = matrix(rep(1,100),ncol=5)
data     = mash_set_data(Bhat,Shat, alpha=1)
U.c      = cov_canonical(data)
res.mash = mash(data,U.c)

# Run mash with penalty exponent on null term equal to 100.
# See "False disovery rates: a new deal" (M. Stephens 2017),
# supplementary material S.2.5 for more details.
set.seed(1)
simdata = simple_sims(500,5,1)
data    = mash_set_data(simdata$Bhat,simdata$Shat)
U.c     = cov_canonical(data)
res0    = mash(data,U.c)
res1    = mash(data,U.c,prior = "nullbiased",nullweight = 101)
plot(res0$fitted_g$pi,res1$fitted_g$pi,pch = 20)
abline(a = 0,b = 1,col = "skyblue",lty = "dashed")

mashr documentation built on Oct. 18, 2023, 5:08 p.m.