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

Description Usage Arguments Value Examples

Assumes that chi-square test statistics for each SNP are available from another study. Treats the true control and case minor allele frequencies and the chi-square non-centrality parameters as random triples from a bivariate prior distribution G, and estimates the optimal Bayesian classifier given G. Nonparametric maximum likelihood is used as a plug-in estimator for G.

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

`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
`d`	if a single number, G is estimated on a d x d x d grid; if a three-component vector (d0,d1,dt), G is estimated on a d0 x d1 x dt grid
`maxit`	maximum number of EM iterations
`tol`	error tolerance
`verbose`	TRUE to print the error attained by each EM iteration

`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
`Lam`	grid points for estimating the distribution of the non-centrality parameter
`D0`	conditional density matrix for controls
`D1`	conditional density matrix for cases
`DT`	conditional density matrix for test statistics
`g`	estimated mixing probability mass function
`P`	proportion of cases

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),1,25); ## 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
nebula <- nebula.chisq.train(colMeans(X0)/2,colMeans(X1)/2,n0,n1,T,d=c(20,25,30));
par(mfrow=c(1,3));
contour(nebula$Pi0,nebula$Pi1,apply(nebula$g,c(1,2),sum));
points(pi0,pi1);
contour(nebula$Pi0,nebula$Lam,apply(nebula$g,c(1,3),sum));
points(pi0,lam);
contour(nebula$Pi1,nebula$Lam,apply(nebula$g,c(2,3),sum));
points(pi1,lam);