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README.Rmd
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README.Rmd
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---
output:
github_document:
html_preview: false
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# skpr <img src="man/figures/skprlogo.png" align="right" />
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[](https://travis-ci.org/tylermorganwall/skpr)
[](https://cran.r-project.org/package=skpr)
[](https://github.com/tylermorganwall/skpr/actions/workflows/R-CMD-check.yaml)
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<img src="man/figures/vidguigif.gif" ></img>
## 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:
<img src="man/figures/skprGUIcomp.png" align="center"></img>
## 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)
```