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13 changes: 7 additions & 6 deletions README.Rmd
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Expand Up @@ -51,7 +51,8 @@ devtools::install_github("tylermorganwall/skpr")
* `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.
* `skprGUI()` and `skprGUIbrowser()` opens up the GUI in either R Studio or an external browser.
* `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:

Expand All @@ -60,7 +61,7 @@ If addition, the package offers two functions to generate common plots related t

##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()` or `skprGUIbrowser()` to begin. Screenshots:
`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>

Expand Down Expand Up @@ -98,11 +99,11 @@ eval_design_mc(design, effectsize=1.5)
#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.
#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()
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)
```
164 changes: 97 additions & 67 deletions README.md
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Expand Up @@ -17,17 +17,17 @@ Status](https://travis-ci.org/tylermorganwall/skpr.svg?branch=master)](https://t
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.
- 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

Expand All @@ -42,36 +42,38 @@ 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.
- `skprGUI()` and `skprGUIbrowser()` opens up the GUI in either R
Studio or an external browser.
- `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.
- `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()` or
`skprGUIbrowser()` to begin. Screenshots:
`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>

Expand Down Expand Up @@ -138,9 +140,12 @@ eval_design(design)
#> 8 beansize1 parameter.power 0.5593966
#> 9 beansize2 parameter.power 0.5593966
#> ============Evaluation Info============
#> • Alpha = 0.05 • Trials = 12 • Blocked = FALSE
#> • Evaluating Model = ~temp + type + beansize
#> • Anticipated Coefficients = c(1, 1, 1, 1, -1)
#> * Alpha = 0.05 * Trials = 12 * Blocked = FALSE
#> * Evaluating Model = ~temp + type + beansize
#> * Anticipated Coefficients = c(1, 1, 1, 1, -1)
#> * Contrasts = `contr.sum`
#> * Parameter Analysis Method = `lm(...)`
#> * Effect Analysis Method = `car::Anova(fit, type = "III")`

#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.
Expand All @@ -156,9 +161,12 @@ eval_design_mc(design, effectsize=1.5)
#> 8 beansize1 parameter.power.mc 0.359
#> 9 beansize2 parameter.power.mc 0.354
#> ===========Evaluation Info============
#> • Alpha = 0.05 • Trials = 12 • Blocked = FALSE
#> • Evaluating Model = ~temp + type + beansize
#> • Anticipated Coefficients = c(0.750, 0.750, 0.750, 0.750, -0.750)
#> * Alpha = 0.05 * Trials = 12 * Blocked = FALSE
#> * Evaluating Model = ~temp + type + beansize
#> * Anticipated Coefficients = c(0.750, 0.750, 0.750, 0.750, -0.750)
#> * Contrasts = `contr.sum`
#> * Parameter Analysis Method = `lm(...)`
#> * Effect Analysis Method = `car::Anova(fit, type = "III")`

#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.
Expand All @@ -174,33 +182,55 @@ eval_design_mc(design, nsim=5000, glmfamily = "poisson", effectsize=c(2,6))
#> 8 beansize1 parameter.power.mc 0.8842
#> 9 beansize2 parameter.power.mc 0.7052
#> ============Evaluation Info============
#> • Alpha = 0.05 • Trials = 12 • Blocked = FALSE
#> • Evaluating Model = ~temp + type + beansize
#> • Anticipated Coefficients = c(1.242, 0.549, 0.549, 0.549, -0.549)

#skpr was designed to operate with the pipe (%>%) in mind.
#> * Alpha = 0.05 * Trials = 12 * Blocked = FALSE
#> * Evaluating Model = ~temp + type + beansize
#> * Anticipated Coefficients = c(1.242, 0.549, 0.549, 0.549, -0.549)
#> * Contrasts = `contr.sum`
#> * Parameter Analysis Method = `glm(..., family = "poisson")`
#> * Effect Analysis Method = `car::Anova(fit, type = "III")`

#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()
#> parameter type power
#> 1 (Intercept) effect.power.mc 0.912
#> 2 temp effect.power.mc 0.927
#> 3 type effect.power.mc 0.997
#> 4 beansize effect.power.mc 0.935
#> 5 I(temp^2) effect.power.mc 0.637
#> 6 type:beansize effect.power.mc 0.913
#> 7 (Intercept) parameter.power.mc 0.912
#> 8 temp parameter.power.mc 0.927
#> 9 type1 parameter.power.mc 0.997
#> 10 beansize1 parameter.power.mc 0.917
#> 11 beansize2 parameter.power.mc 0.913
#> 12 I(temp^2) parameter.power.mc 0.637
#> 13 type1:beansize1 parameter.power.mc 0.899
#> 14 type1:beansize2 parameter.power.mc 0.902
#> ==============Evaluation Info==============
#> • Alpha = 0.05 • Trials = 24 • Blocked = FALSE
#> • Evaluating Model = ~temp + type + beansize + type:beansize + I(temp^2)
#> • Anticipated Coefficients = c(1, 1, 1, 1, -1, 1, 1, -1)
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)
#> parameter type power anticoef alpha glmfamily trials
#> 1 (Intercept) effect.power.mc 0.912 NA 0.05 gaussian 24
#> 2 temp effect.power.mc 0.927 NA 0.05 gaussian 24
#> 3 type effect.power.mc 0.997 NA 0.05 gaussian 24
#> 4 beansize effect.power.mc 0.935 NA 0.05 gaussian 24
#> 5 I(temp^2) effect.power.mc 0.637 NA 0.05 gaussian 24
#> 6 type:beansize effect.power.mc 0.913 NA 0.05 gaussian 24
#> 7 (Intercept) parameter.power.mc 0.912 1 0.05 gaussian 24
#> 8 temp parameter.power.mc 0.927 1 0.05 gaussian 24
#> 9 type1 parameter.power.mc 0.997 1 0.05 gaussian 24
#> 10 beansize1 parameter.power.mc 0.917 1 0.05 gaussian 24
#> 11 beansize2 parameter.power.mc 0.913 -1 0.05 gaussian 24
#> 12 I(temp^2) parameter.power.mc 0.637 1 0.05 gaussian 24
#> 13 type1:beansize1 parameter.power.mc 0.899 1 0.05 gaussian 24
#> 14 type1:beansize2 parameter.power.mc 0.902 -1 0.05 gaussian 24
#> nsim blocking error_adjusted_alpha power_lcb power_ucb
#> 1 1000 FALSE 0.05 0.8927052 0.9288249
#> 2 1000 FALSE 0.05 0.9090858 0.9423464
#> 3 1000 FALSE 0.05 0.9912580 0.9993809
#> 4 1000 FALSE 0.05 0.9178989 0.9494797
#> 5 1000 FALSE 0.05 0.6063275 0.6668632
#> 6 1000 FALSE 0.05 0.8937921 0.9297315
#> 7 1000 FALSE 0.05 0.8927052 0.9288249
#> 8 1000 FALSE 0.05 0.9090858 0.9423464
#> 9 1000 FALSE 0.05 0.9912580 0.9993809
#> 10 1000 FALSE 0.05 0.8981467 0.9333511
#> 11 1000 FALSE 0.05 0.8937921 0.9297315
#> 12 1000 FALSE 0.05 0.6063275 0.6668632
#> 13 1000 FALSE 0.05 0.8786332 0.9169799
#> 14 1000 FALSE 0.05 0.8818715 0.9197225
#> =========================================================Evaluation Info==========================================================
#> * Alpha = 0.05 * Trials = 24 * Blocked = FALSE
#> * Evaluating Model = ~temp + type + beansize + type:beansize + I(temp^2)
#> * Anticipated Coefficients = c(1, 1, 1, 1, -1, 1, 1, -1)
#> * Contrasts = `contr.sum`
#> * Parameter Analysis Method = `lm(...)`
#> * Effect Analysis Method = `car::Anova(fit, type = "III")`
#> * MC Power CI Confidence = 95%
```

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