Methods for Adaptive Shrinkage, using Empirical Bayes
Adaptive Shrinkage
Performs adaptive shrinkage on Poisson data
Credible Interval Computation for the ash object
ashr
Compute loglikelihood for data from ash fit
Compute loglikelihood ratio for data from ash fit
Generic function of calculating the overall mean of the mixture
Generic function of calculating the overall standard deviation of the ...
Compute loglikelihood for data under null that all beta are 0
Compute vector of loglikelihood for data under null that all beta are ...
Compute vector of loglikelihood for data from ash fit
Compute vector of loglikelihood ratio for data from ash fit
cdf method for ash object
cdf_conv
cdf_post
Generic function of computing the cdf for each component
comp_cdf_conv.normalmix
comp_cdf_conv
cdf of convolution of each component of a unif mixture
comp_cdf_post
Generic function of calculating the component densities of the mixture
comp_dens_conv.normalmix
comp_dens_conv
density of convolution of each component of a unif mixture
comp_mean.normalmix
Generic function of calculating the first moment of components of the ...
comp_mean.tnormalmix
Generic function of calculating the second moment of components of the...
comp_postmean
comp_postmean2
comp_postprob
comp_postsd
comp_sd.normalmix
Generic function to extract the standard deviations of components of t...
comp_sd.normalmix
Function to compute the local false sign rate
Brief description of function.
Find density at y, a generic function
dens_conv
The log-F distribution
Estimate mixture proportions of a mixture g given noisy (error-prone) ...
gen_etruncFUN
Density method for ash object
Return lfsr from an ash object
Sample from posterior
Constructor for igmix class
Likelihood object for Binomial error distribution
Likelihood object for logF error distribution
Likelihood object for normal error distribution
Likelihood object for normal mixture error distribution
Likelihood object for Poisson error distribution
Likelihood object for t error distribution
log_comp_dens_conv.normalmix
log_comp_dens_conv
log density of convolution of each component of a unif mixture
loglik_conv.default
loglik_conv
mixcdf.default
mixcdf
Estimate mixture proportions of a mixture model by EM algorithm
Estimate mixture proportions of a mixture model by Interior Point meth...
Generic function of calculating the overall second moment of the mixtu...
Generic function of extracting the mixture proportions
Estimate mixture proportions of a mixture model using mix-SQP algorith...
Estimate posterior distribution on mixture proportions of a mixture mo...
second moment of truncated Beta distribution
second moment of truncated gamma distribution
Expected Squared Value of Truncated Normal
my_e2trunct
mean of truncated Beta distribution
mean of truncated gamma distribution
my_etrunclogf
Expected Value of Truncated Normal
my_etrunct
Variance of Truncated Normal
ncomp.default
ncomp
Constructor for normalmix class
pcdf_post
The log-F distribution
Plot method for ash object
Diagnostic plots for ash object
Generic function to extract which components of mixture are point mass...
post_sample.normalmix
post_sample
post_sample.unimix
Compute Posterior
postmean
postmean2
postsd
Print method for ash object
prune
Function to compute q values from local false discovery rates
Takes raw data and sets up data object for use by ash
Summary method for ash object
Constructor for tnormalmix class
Constructor for unimix class
vcdf_post
Estimate mixture proportions of a mixture model by EM algorithm (weigh...
The R package 'ashr' implements an Empirical Bayes approach for large-scale hypothesis testing and false discovery rate (FDR) estimation based on the methods proposed in M. Stephens, 2016, "False discovery rates: a new deal", <DOI:10.1093/biostatistics/kxw041>. These methods can be applied whenever two sets of summary statistics---estimated effects and standard errors---are available, just as 'qvalue' can be applied to previously computed p-values. Two main interfaces are provided: ash(), which is more user-friendly; and ash.workhorse(), which has more options and is geared toward advanced users. The ash() and ash.workhorse() also provides a flexible modeling interface that can accommodate a variety of likelihoods (e.g., normal, Poisson) and mixture priors (e.g., uniform, normal).