# nebula.train: Nonparametric empirical Bayes classifier using latent... In ssa: Simultaneous Signal Analysis

## Description

Nonparametric empirical Bayes classifier using latent annotations: wrapper function; training

## Usage

 ```1 2``` ```nebula.train(pi0, pi1, n0, n1, T = NULL, I = NULL, 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 `T` p x 1 vector of chi-square test statistics `I` p x 1 vector of binary indicators `d` if a single number, G0 and G1 are estimated on d x d x d grids; if a three-component vector (d0,d1,dt), G0 and G1 are estimated on d0 x d1 x dt grids `maxit` maximum number of EM iterations `tol` error tolerance `verbose` TRUE to print the error attained by each EM iteration

## Value

 `type` 1=given neither T nor I; 2=given T but not I; 3=not given T but given I; 4=given both T and I `nebula` trained classifier

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```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])); set.seed(1); lam <- rep(0,p); lam[I==1] <- rchisq(sum(I==1),1,50); ## ncps ## 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 T <- rchisq(p,1,lam); ## chi-square statistics nebula1 <- nebula.train(colMeans(X0)/2,colMeans(X1)/2,n0,n1,d=c(10,15)); nebula2 <- nebula.train(colMeans(X0)/2,colMeans(X1)/2,n0,n1,T=T,d=c(10,15,20)); nebula3 <- nebula.train(colMeans(X0)/2,colMeans(X1)/2,n0,n1,I=I,d=c(10,15)); nebula4 <- nebula.train(colMeans(X0)/2,colMeans(X1)/2,n0,n1,T=T,I=I,d=c(10,15,20)); ```

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