posterior.mean | R Documentation |
Compute the posterior mean under spike-and-slab Gaussian prior
posterior.mean(z, sigma, p, mu, sigma.prior, deriv = 0)
z |
a vector of z-scores |
sigma |
a vector of standard deviations of |
p |
prior: mixture proportion |
mu |
prior: mean |
sigma.prior |
prior: standard deviation |
deriv |
compute the posterior mean ( |
Similar to fit.mixture.model
, this function assumes that z is distributed as N(γ, 1) and γ follows a Gaussian mixture model. The function computes the posterior mean E[γ|z].
a vector
require(mr.raps) data(lipid.cad) data <- subset(lipid.cad, lipid == "hdl" & restrict & gwas.selection == "teslovich_2010" & gwas.outcome == "cardiogramplusc4d_1000genome") z <- data$beta.exposure / data$se.exposure prior.param <- fit.mixture.model(z) z.seq <- seq(-5, 5, 0.1) gamma.hat <- posterior.mean(z.seq, 1, prior.param$p, prior.param$mu, prior.param$sigma) gamma.hat.deriv <- posterior.mean(z.seq, 1, prior.param$p, prior.param$mu, prior.param$sigma, deriv = 1) par(mfrow = c(1, 2)) plot(z.seq, gamma.hat, type = "l") plot(z.seq, gamma.hat.deriv, type = "l")
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