Description Usage Arguments Value Examples
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.
1 2 |
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 |
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 |
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);
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