bad_ash: Main Adaptive Shrinkage function
In dcgerard/sabotash: Sabotage the results of ashr

Description Usage Arguments Details Value See Also Examples

Takes vectors of estimates (betahat) and their standard errors (sebetahat), together with degrees of freedom (df) and applies shrinkage to them, using Empirical Bayes methods, to compute shrunk estimates for beta.

1 2	bad_ash(betahat, sebetahat, mixcompdist = c("uniform", "halfuniform", "normal", "+uniform", "-uniform"), df = NULL, ...)

`betahat`	a p vector of estimates
`sebetahat`	a p vector of corresponding standard errors
`mixcompdist`	distribution of components in mixture ("uniform","halfuniform" or "normal"; "+uniform" or "-uniform"), the default is "uniform". If you believe your effects may be asymmetric, use "halfuniform". If you want to allow only positive/negative effects use "+uniform"/"-uniform". The use of "normal" is permitted only if df=NULL.
`df`	appropriate degrees of freedom for (t) distribution of betahat/sebetahat, default is NULL which is actually treated as infinity (Gaussian)
`...`	Further arguments to be passed to `ash.workhorse`.

This function is actually just a simple wrapper that passes its parameters to ash.workhorse which provides more documented options for advanced use. See readme for more details.

ash returns an object of class "ash", a list with some or all of the following elements (determined by outputlevel)

`fitted_g`	fitted mixture
`loglik`	log P(D\|fitted_g)
`logLR`	log[P(D\|fitted_g)/P(D\|beta==0)]
`result`	A dataframe whose columns are

NegativeProb: A vector of posterior probability that beta is negative
PositiveProb: A vector of posterior probability that beta is positive
lfsr: A vector of estimated local false sign rate
lfdr: A vector of estimated local false discovery rate
qvalue: A vector of q values
svalue: A vector of s values
PosteriorMean: A vector consisting the posterior mean of beta from the mixture
PosteriorSD: A vector consisting the corresponding posterior standard deviation

`call`	a call in which all of the specified arguments are specified by their full names
`data`	a list containing details of the data and models used (mostly for internal use)
`fit_details`	a list containing results of mixture optimization, and matrix of component log-likelihoods used in this optimization

ash.workhorse for complete specification of ash function

ashci for computation of credible intervals after getting the ash object return by ash()

beta = c(rep(0,100),rnorm(100))
sebetahat = abs(rnorm(200,0,1))
betahat = rnorm(200,beta,sebetahat)
beta.ash = ash(betahat, sebetahat)
names(beta.ash)
head(beta.ash$result) # the main dataframe of results
graphics::plot(betahat,beta.ash$result$PosteriorMean,xlim=c(-4,4),ylim=c(-4,4))

CIMatrix=ashci(beta.ash,level=0.95)
print(CIMatrix)

#Illustrating the non-zero mode feature
betahat=betahat+5
beta.ash = ash(betahat, sebetahat)
graphics::plot(betahat,beta.ash$result$PosteriorMean)
betan.ash=ash(betahat, sebetahat,mode=5)
graphics::plot(betahat, betan.ash$result$PosteriorMean)
summary(betan.ash)