GP.optim {BayesianPlatformDesignTimeTrend} R Documentation

## GP.optim: optimiser to give the next cutoff for evaluation

### Description

A function to predict the next cutoff value for evaluation.

### Usage

GP.optim(
x,
y,
errorrate = 0.05,
confidence.level = 0.95,
grid.length = 5000,
change.scale = FALSE,
noise = T,
grid.min,
grid.max
)


### Arguments

 x A numeric vector of cutoff data y A numeric vector of error rate data errorrate A numeric value. The error rate we want to achieve. Error rate here means type I error rate or family-wise error rate. Default is 0.05. confidence.level A numeric value indicating the confidence level of estimate. Default is 0.95 grid.length A numeric value indicating the grid resolution. Default is 5000. change.scale A logic value indicating whether we want to change scale when doing Gaussian process. Default is FALSE. noise A logic value indicating whether the input x is noisy. Default is TRUE. grid.min A numeric value indicating the lower bound of the grid for screening. grid.max A numeric value indicating the upper bound of the grid for screening.

### Value

A list including the next cutoff value for evaluation next.cutoff and a list of predictions for screening grid.

Ziyan Wang

### References

Surrogates: Gaussian process modeling, design, and optimization for the applied sciences. CRC press. Gramacy, R.B., 2020. Bayesian optimization for adaptive experimental design: A review. IEEE access, 8, 13937-13948. Greenhill, S., Rana, S., Gupta, S., Vellanki, P., & Venkatesh, S. (2020).

### Examples

x = c(7.123968, 6.449631, 1.984406,
3.507463, 4.972510, 2.925768,
5.816682, 4.367796,
7.349160, 1.113648)
y = c(0.0396, 0.0450,
0.5116, 0.2172,
0.1040, 0.3058,
0.0592, 0.1384,
0.0296, 0.7936)
grid.min=1
grid.max=8
GP.res=GP.optim(x=x, y=y, errorrate = 0.1, grid.min = grid.min, grid.max = grid.max)
GP.res\$next.cutoff


[Package BayesianPlatformDesignTimeTrend version 1.1.3 Index]