# designUniformRandom: Uniform Design Generator In SPOT: Sequential Parameter Optimization Toolbox

## Description

Create a simple experimental design based on uniform random sampling.

## Usage

 `1` ```designUniformRandom(x = NULL, lower, upper, control = list()) ```

## Arguments

 `x` optional data.frame x to be part of the design `lower` vector with lower boundary of the design variables (in case of categorical parameters, please map the respective factor to a set of contiguous integers, e.g., with lower = 1 and upper = number of levels) `upper` vector with upper boundary of the design variables (in case of categorical parameters, please map the respective factor to a set of contiguous integers, e.g., with lower = 1 and upper = number of levels) `control` list of controls: `size` number of design points `types` this specifies the data type for each design parameter, as a vector of either "numeric","integer","factor". (here, this only affects rounding) `replicates` integer for replications of each design point. E.g., if replications is two, every design point will occur twice in the resulting matrix.

## Value

matrix `design`
- `design` has `length(lower)` columns and `(size + nrow(x))*control\$replicates` rows. All values should be within `lower <= design <= upper`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```set.seed(1) #set RNG seed to make examples reproducible design <- designUniformRandom(,1,2) #simple, 1-D case design design <- designUniformRandom(,1,2,control=list(replicates=3)) #with replications design design <- designUniformRandom(,c(-1,-2,1,0),c(1,4,9,1), control=list(size=5, types=c("numeric","integer","factor","factor"))) design x <- designUniformRandom(,c(1,-10),c(2,10),control=list(size=5)) x2 <- designUniformRandom(x,c(1,-10),c(2,10),control=list(size=5)) plot(x2) points(x, pch=19) ```

SPOT documentation built on Oct. 23, 2021, 1:06 a.m.