# FindOptPower: search for the optimal pooled design In hiPOD: hierarchical Pooled Optimal Design

## Description

Perform a grid search over potential design space, and find the predicted power and validity of the designs.

## Usage

 `1` ```FindOptPower(cost, sample.size, MAF, OR, error, costPerExp = 18915, costPerPool = 970, costPerX = 300, lower.P = 20, upper.P = 400, lower.N.p = 2, upper.N.p = 200, lower.Xmean = 4, upper.Xmean = 1280, sig.level = 0.05, Number.Grids = 100) ```

## Arguments

 `cost` cost constraint of the study `sample.size` sample size constraint of the study `MAF` assumed MAF of the variant of interest `OR` assume effect size (odds ratio) of the variant of interest `error` assume sequencing error rate `costPerExp` cost per experiment `costPerPool` cost per pool `costPerX` cost per 1X coverage `lower.P` lower bound of number of pools in potential consideration `upper.P` upper bound of number of pools in potential consideration `lower.N.p` lower bound of number of pool size in potential consideration `upper.N.p` upper bound of number of pool size in potential consideration `lower.Xmean` lower bound of number of coverage per pool in potential consideration `upper.Xmean` upper bound of number of coverage per pool in potential consideration `sig.level` significance level of the statistic test, usually 0.05 for a single test `Number.Grids` number of grids in the search space, preset as 100

## Details

Given the research question and the study constraints, this function calculates the power and validity of all the potential pooled designs.

## Value

Returns a list:

 `cost ` `sample.size ` `constraint.set ` `scenario.set ` `designs ` the potential designs, validity and power

## Author(s)

Wei E. Liang

`PlotOptPower`, `ShowOptDesign`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ######## Example 1: A simple example, with very rough grid points (only 20X20 grid points) # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ##### Find the optimal design example.1 <- FindOptPower(cost=452915, sample.size=5000, MAF=0.03, OR=2, error=0.01, upper.P=200, Number.Grids=20) ##### assign a directory to store the contour plots ##### with your own choice proj.Dir <- paste(getwd(), "/hiPOD_examples", sep="") if(!file.exists(proj.Dir)) dir.create(proj.Dir) ##### Inferences on the optimal designs PlotOptPower(example.1, save.contour=FALSE, plot.3d=FALSE) # # snapshot3d(filename = paste(proj.Dir, "/example1_3d.bmp", sep="")) ShowOptDesign(example.1, 5) ```