# predictPoolNCP: The prediction model of non-centrality parameter In hiPOD: hierarchical Pooled Optimal Design

## Description

It is based on our simulation results.

## Usage

 `1` ```predictPoolNCP(MAF, OR, error, P, N.p, Xmean) ```

## Arguments

 `MAF` minor allele frequency `OR` odds ratio `error` sequencing error `P` number of pools (case+control) `N.p` number of individuals per pool `Xmean` average coverage per pool

Wei E. Liang

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## The function is currently defined as function(MAF, OR, error, P, N.p, Xmean){ count <- 1 while(ncp.pool.pred[[1]][count] <= MAF) count<-count+1 ncp.pool.pred.coef <- ncp.pool.pred[[2]][[count]] covariates <- c(1, MAF, log(OR), error, P, Xmean, N.p, (P^(1/3)), (Xmean^(1/3)), (N.p^(1/3)), (log(OR)^2), MAF*log(OR), Xmean*N.p, MAF*P, MAF*Xmean, MAF*N.p, log(OR)*P, log(OR)*Xmean, log(OR)*N.p, error*P, error*Xmean, error*N.p, MAF*Xmean*N.p, log(OR)*Xmean*N.p, MAF*log(OR)*P, MAF*log(OR)*Xmean, MAF*log(OR)*N.p, error*Xmean*N.p, MAF*log(OR)*Xmean*N.p) ncp.pool.predicted <- (sum(ncp.pool.pred.coef * covariates))^3 ncp.pool.predicted } ```

hiPOD documentation built on May 29, 2017, 9:10 a.m.