# neb.train: Nonparametric empirical Bayes classifier without annotations;... In ssa: Simultaneous Signal Analysis

## Description

Treats the control and case minor allele frequencies as random tuples from a bivariate prior distribution G and then estimates the optimal Bayesian classifier given G. Nonparametric maximum likelihood is used as a plug-in estimator for G.

## Usage

 ```1 2``` ```neb.train(pi0, pi1, n0, n1, d = 25, maxit = 200, tol = 1e-04, verbose = FALSE) ```

## Arguments

 `pi0, pi1` p x 1 vectors of control and case minor allele frequencies, respectively; IMPORTANT: must be relative to the same allele in both cases and controls `n0, n1` number of controls and number of cases, respectively `d` if a single number, G is estimated on a d x d grid; if a two-component vector (d0,d1), G is estimated on a d0 x d1 grid `maxit` maximum number of EM iterations `tol` error tolerance `verbose` TRUE to print the error attained by each EM iteration

## Value

 `Pi0` grid points for estimating the distribution of the control minor allele frequencies `Pi1` grid points for estimating the distribution of the case minor allele frequencies `D0` conditional density matrix for controls `D1` conditional density matrix for cases `g` estimated mixing probability mass function `P` proportion of cases

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```p <- 1000; ## number of snps I <- rep(0,p); I[1:10] <- 1; ## which snps are causal set.seed(1); pi0 <- runif(p,0.1,0.5); ## control minor allele frequencies set.seed(1); ors <- runif(sum(I),-1,1); ## odds ratios pi1 <- pi0; pi1[I==1] <- expit(ors+logit(pi0[I==1])); ## training data n0 <- 100; ## number of controls X0 <- t(replicate(n0,rbinom(p,2,pi0))); ## controls n1 <- 50; ## number of cases X1 <- t(replicate(n1,rbinom(p,2,pi1))); ## cases neb <- neb.train(colMeans(X0)/2,colMeans(X1)/2,n0,n1,d=c(20,25)); contour(neb\$Pi0,neb\$Pi1,neb\$g); points(pi0,pi1); ```

ssa documentation built on May 1, 2019, 10:27 p.m.