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https://github.com/tylermorganwall/skpr

Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user.
https://github.com/tylermorganwall/skpr

design-of-experiments linear-models linear-regression monte-carlo optimal-designs power r rstats split-plot-designs survival-analysis

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Generates and evaluates D, I, A, Alias, E, T, G, and custom optimal designs. Supports generation and evaluation of mixture and split/split-split/N-split plot designs. Includes parametric and Monte Carlo power evaluation functions. Provides a framework to evaluate power using functions provided in other packages or written by the user.

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README 

        
---

output: 

  github_document:

  html_preview: false

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```



# skpr 



[![Travis-CI Build Status](https://travis-ci.org/tylermorganwall/skpr.svg?branch=master)](https://travis-ci.org/tylermorganwall/skpr)

[![CRAN_Status_Badge]( http://www.r-pkg.org/badges/version-ago/skpr)](https://cran.r-project.org/package=skpr)

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## Overview



**skpr** is an open source design of experiments suite for generating and evaluating optimal designs in R. Here is a sampling of what skpr offers: 



* Generates and evaluates D, I, A, Alias, E, T, and G optimal designs, as well as user-defined custom optimality criteria. 

* Supports generation and evaluation of split/split-split/.../N-split plot designs. 

* Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. 

* Provides an extensible framework for the user to evaluate Monte Carlo power using their own libraries. 

* Includes a Shiny graphical user interface, skprGUI, that auto-generates the R code used to create and evaluate the design to improve ease-of-use and enhance reproducibility.



## Installation



```{r, eval=FALSE}

# To install:

install.packages("skpr")



# To install the latest version from Github:

# install.packages("devtools")

devtools::install_github("tylermorganwall/skpr")

```



## Functions



* `gen_design()` generates optimal designs from a candidate set, given a model and the desired number of runs.

* `eval_design()` evaluates power parametrically for linear models, for normal and split-plot designs.

* `eval_design_mc()` evaluates power with a Monte Carlo simulation, for linear and generalized linear models. This function also supports calculating power for split-plot designs using REML. 

* `eval_design_survival_mc()` evaluates power with a Monte Carlo simulation, allowing the user to specify a point at which the data is censored.

* `eval_design_custom_mc()` allows the user to import their own libraries and use the Monte Carlo framework provided by skpr to calculate power.

* `calculate_power_curves()` provides an interface to automate the generation and evaluation of designs to create power versus sample size and effect size curves.

* `skprGUI()` opens up the GUI in either RStudio or an external browser.



If addition, the package offers two functions to generate common plots related to designs:



* `plot_correlations()` generates a color map of correlations between variables.

* `plot_fds()` generates the fraction of design space plot for a given design.



##skprGUI

 

`skprGUI()` provides an graphical user interface to access all of the main features of skpr. An interactive tutorial is provided to familiarize the user with the available functionality. Type `skprGUI()` to begin. Screenshots:






## Usage



```{r, include=FALSE}

set.seed(2)

```



```{r example, message = FALSE}

library(skpr)



#Generate a candidate set of all potential design points to be considered in the experiment

#The hypothetical experiment is determining what affects the caffeine content in coffee

candidate_set = expand.grid(temp = c(80,90,100), 

                            type = c("Kona","Java"),

                            beansize = c("Large","Medium","Small"))

candidate_set



#Generate the design (default D-optimal)

design = gen_design(candidateset = candidate_set, 

                    model = ~temp + type + beansize,

                    trials=12)

design



#Evaluate power for the design with an allowable type-I error of 5% (default)

eval_design(design)



#Evaluate power for the design using a Monte Carlo simulation. 

#Here, we set the effect size (here, the signal-to-noise ratio) to 1.5.

eval_design_mc(design, effectsize=1.5)



#Evaluate power for the design using a Monte Carlo simulation, for a non-normal response. 

#Here, we also increase the number of simululations to improve the precision of the results.

eval_design_mc(design, nsim=5000, glmfamily = "poisson", effectsize=c(2,6))



#skpr was designed to operate with the pipe (|>) in mind. 

#Here is an example of an entire design of experiments analysis in three lines:



expand.grid(temp = c(80,90,100), type = c("Kona","Java"), beansize = c("Large","Medium","Small")) |>

  gen_design(model = ~temp + type + beansize + beansize:type + I(temp^2), trials=24, optimality="I") |>

  eval_design_mc(detailedoutput = TRUE)



```