Installation
You can install:
- the latest release from CRAN with
install.packages('ggDoE')
- the development version from GitHub with
if (!require("pak")) install.packages("pak")
pak::pak("toledo60/ggDoE")
Overview
With ggDoE you’ll be able to generate common plots used in Design of Experiments with ggplot2.
The following plots are currently available:
- Alias Matrix
- Box-Cox Transformation
- Lambda Plot
- Boxplots
- Regression Diagnostic Plots
- Half-Normal Plot
- Interaction Effects Plot for a Factorial Design
- Main Effects Plot for a Factorial Design
- Contour Plots for Response Surface Methodology
- Pareto Plot
- Two Dimensional Projections of a Latin Hypercube Design
The following datasets/designs are included in ggDoE as tibbles:
adapted_epitaxial: Adapted epitaxial layer experiment obtain from the book
“Experiments: Planning, Analysis, and Optimization, 2nd Edition”original_epitaxial: Original epitaxial layer experiment obtain from the book
“Experiments: Planning, Analysis, and Optimization, 2nd Edition”pulp_experiment: Reflectance Data, Pulp Experiment obtain from the book
“Experiments: Planning, Analysis, and Optimization, 2nd Edition”girder_experiment: Girder experiment obtain from the book
“Experiments: Planning, Analysis, and Optimization, 2nd Edition”aliased_design: D-efficient minimal aliasing design obtained from the article
“Efficient Designs With Minimal Aliasing by Bradley Jones and Christopher J. Nachtsheim”
Citation
If you want to cite this package in a scientific journal or in any other context, run the following code in your R
console
citation('ggDoE')
Warning in citation("ggDoE"): could not determine year for 'ggDoE' from package
DESCRIPTION file
To cite package 'ggDoE' in publications use:
Toledo Luna J (????). _ggDoE: Modern Graphs for Design of Experiments
with 'ggplot2'_. R package version 0.8, <https://ggdoe.netlify.app>.
A BibTeX entry for LaTeX users is
@Manual{,
title = {ggDoE: Modern Graphs for Design of Experiments with 'ggplot2'},
author = {Jose {Toledo Luna}},
note = {R package version 0.8},
url = {https://ggdoe.netlify.app},
}
Contributing to the package
I welcome feedback, suggestions, issues, and contributions! Check out the CONTRIBUTING file for more details.
Examples of Plots
Alias Matrix
Correlation matrix plot to visualize the Alias matrix
alias_matrix(design=aliased_design)
Box-Cox Transformation
model <- lm(s2 ~ (A+B+C+D),data = adapted_epitaxial)
boxcox_transform(model,lambda = seq(-5,5,0.2))
Lambda Plot
Obtain the trace plot of the t-statistics after applying Boxcox transformation across a specified sequence of lambda values
model <- lm(s2 ~ (A+B+C)^2,data=original_epitaxial)
lambda_plot(model)
lambda_plot(model, lambda = seq(0,2,by=0.1))
Boxplots
data <- ToothGrowth
data$dose <- factor(data$dose,levels = c(0.5, 1, 2),
labels = c("D0.5", "D1", "D2"))
gg_boxplots(data,y = 'len',x = 'dose')
gg_boxplots(data,y = 'len',x = 'dose',
group_var = 'supp',
color_palette = 'viridis',
jitter_points = TRUE)
Regression Diagnostic Plots
- Residual vs. Fitted Values
- Normal-QQ plot
- Scale-Location plot
- Residual vs. Leverage
- Cook’s Distance
- Collinearity
The default plots are 1-4
Half-Normal Plot
model <- lm(ybar ~ (A+B+C+D)^4,data=adapted_epitaxial)
half_normal(model)
half_normal(model,method='Zahn',alpha=0.1,
ref_line=TRUE,label_active=TRUE,
margin_errors=TRUE)
Interaction Effects Plot
Interaction effects plot between two factors in a factorial design
interaction_effects(adapted_epitaxial,response = 'ybar',
exclude_vars = c('s2','lns2'))
interaction_effects(adapted_epitaxial,response = 'ybar',
exclude_vars = c('A','s2','lns2'),
n_columns=3)
Main Effects Plots
Main effect plots for each factor in a factorial design
main_effects(original_epitaxial,
response='s2',
exclude_vars = c('A','ybar','lns2'),
color_palette = 'viridis',
n_columns=3)
Contour Plots
contour plot(s) that display the fitted surface for an rsm object involving two or more numerical predictors
Pareto Plot
Pareto plot of effects with cutoff values for the margin of error (ME) and simultaneous margin of error (SME)
model <- lm(lns2 ~ (A+B+C+D)^4,data=original_epitaxial)
pareto_plot(model)
pareto_plot(model,method='Zahn',alpha=0.1)
Two Dimensional Projections
This function will output all two dimensional projections from a Latin hypercube design
set.seed(10)
X <- lhs::randomLHS(n=10, k=4)
pair_plots(X,n_columns=3,grid = TRUE)