Package: trialSizing 0.1.0

Willyan Bandeira

trialSizing: Tools for Experimental Design Sizing

Sizes field experiments from uniformity-trial data, following the relationship between the coefficient of variation and plot size. Checks a trial for the spatial structure the sizing methods assume (semivariogram, Moran's I, kriged field map), summarises the coefficient of variation over every plot shape the grid admits, and estimates the optimal plot size by the modified maximum curvature method of Meier and Lessman (1971), by the linear response plateau (LRP) and quadratic response plateau (QRP) models, and by the closed form of Paranaiba, Ferreira and Morais (2009), which can be compared side by side. From the coefficient of variation at the optimum it derives the number of replications needed to detect a given difference between treatment means, as in Cargnelutti Filho and others (2014). Every method returns standardised diagnostic statistics, optional bootstrap uncertainty for the breakpoint, and publication-style plots.

Authors:Willyan Bandeira [aut, cre], Leonardo Pradebon [aut], Ivan Carvalho [aut], Murilo Loro [aut]

trialSizing_0.1.0.tar.gz
trialSizing_0.1.0.zip(r-4.7-any)trialSizing_0.1.0.zip(r-4.6-any)trialSizing_0.1.0.zip(r-4.5-any)
trialSizing_0.1.0.tgz(r-4.6-any)trialSizing_0.1.0.tgz(r-4.5-any)
trialSizing_0.1.0.tar.gz(r-4.7-any)trialSizing_0.1.0.tar.gz(r-4.6-any)
trialSizing_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
trialSizing/json (API)

# Install 'trialSizing' in R:
install.packages('trialSizing', repos = c('https://willyanjnr.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/willyanjnr/trialsizing/issues

Pkgdown/docs site:https://willyanjnr.github.io

Datasets:

On CRAN:

Conda:

6.01 score 2 stars 23 scripts 219 downloads 8 exports 17 dependencies

Last updated from:175b5b28dd. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK176
source / vignettesOK339
linux-releaseOK208
macos-releaseOK183
macos-oldrelOK284
windows-develOK173
windows-releaseOK120
windows-oldrelOK161
wasm-releaseOK86

Exports:calc_cv_shapescalc_paranaibacalc_replicatescheck_trialcompare_methodsfit_lrpfit_mcmfit_qrp

Dependencies:clicpp11farverggplot2gluegtableisobandlabelinglifecycleR6RColorBrewerrlangS7scalesvctrsviridisLitewithr

Linear Response Plateau (LRP)
Theory | How the breakpoint is estimated | References | Data | Basic use | Plot | Several trials at once | Competing breakpoints | Reproducing a fit from another package | Deciding between basins | Fine-tuning | Warnings worth heeding | Where to go next
Rendered fromlrp.Rmdusingknitr::rmarkdown

Last update: 2026-07-27
Started: 2026-07-23

Number of replications
Theory | Why this needs iteration | References | Basic use | Design, significance level | The full workflow | Plot | Reading the results | Where to go next
Rendered fromreplicates.Rmdusingknitr::rmarkdown

Last update: 2026-07-27
Started: 2026-07-23

Quadratic Response Plateau (QRP)
Theory | How the breakpoint is estimated | References | Data | Basic use | Plot | Several trials at once | Fine-tuning | Warnings worth heeding | Comparing with the other methods
Rendered fromqrp.Rmdusingknitr::rmarkdown

Last update: 2026-07-27
Started: 2026-07-23

Checking a uniformity trial
Why check first | Data | Running the check | Reading the variogram | The field map | Several trials at once | When the grid is awkward | Where this fits | References
Rendered fromcheck_trial.Rmdusingknitr::rmarkdown

Last update: 2026-07-27
Started: 2026-07-24

Comparing the methods
Why compare | From a CV table | From the raw grid | Which statistics are comparable | Weighting | Intervals: the point of the exercise | Several trials at once | Choosing the methods | Where this fits | References
Rendered fromcompare.Rmdusingknitr::rmarkdown

Last update: 2026-07-27
Started: 2026-07-24

CV by plot shape
The step before the models | Theory | Shapes are not interchangeable at equal area | Fewer plots, less information | Data and basic use | Same area, different orientation | n falls as the plot grows | Feeding the fitters | Several trials at once | Long-format data | Where this fits | References
Rendered fromcv_shapes.Rmdusingknitr::rmarkdown

Last update: 2026-07-27
Started: 2026-07-24

Modified Maximum Curvature (MCM)
Theory | Two ways to estimate a and b | References | Data | Basic use | Choosing the estimator | Plot | Several trials at once | Warnings worth heeding | Where to go next
Rendered frommcm.Rmdusingknitr::rmarkdown

Last update: 2026-07-27
Started: 2026-07-23

The Paranaiba method
Theory | How rho is estimated | References | Data | Basic use | Direction of the walk | Three ways to supply the data | Plot | When results are flagged invalid | How it compares
Rendered fromparanaiba.Rmdusingknitr::rmarkdown

Last update: 2026-07-27
Started: 2026-07-23

Validation on simulated fields
The idea | The generator | Recovering the autocorrelation | Response of the CV-curve methods | What this does and does not show | Extending this to your own scenarios
Rendered fromvalidation.Rmdusingknitr::rmarkdown

Last update: 2026-07-27
Started: 2026-07-25

Readme and manuals

Help Manual

Help pageTopics
CV by plot shape from a uniformity-trial gridcalc_cv_shapes
Optimal plot size by the Paranaiba methodcalc_paranaiba
Estimate the optimal number of replicationscalc_replicates
Check a uniformity trial before sizing plotscheck_trial
Compare the plot-size methods on the same trialcompare_methods
Fit the Linear Response Plateau (LRP) model by grid searchfit_lrp
Fit the Modified Maximum Curvature (MCM) plot-size modelfit_mcm
Fit the Quadratic Response Plateau (QRP) model by grid searchfit_qrp
Plot an LRP fit (publication style)plot.lrp_fit
Plot LRP fits for several trials (paginated grid)plot.lrp_multi
Plot an MCM fit (publication style)plot.mcm_fit
Plot MCM fits for several trials (paginated grid)plot.mcm_multi
Plot Paranaiba estimates by trialplot.paranaiba_fit
Plot a QRP fit (publication style)plot.qrp_fit
Plot QRP fits for several trials (paginated grid)plot.qrp_multi
Plot the number of replicationsplot.replicates_fit
Field map of a checked uniformity trialplot.trial_check
Predictions from an LRP fitpredict.lrp_fit
Predictions from an MCM fitpredict.mcm_fit
Predictions from a QRP fitpredict.qrp_fit
Print an LRP fitprint.lrp_fit
Print LRP fits for several trialsprint.lrp_multi
Print a method comparisonprint.method_comparison
Print a trial checkprint.trial_check
Summarize an LRP fitsummary.lrp_fit
Summarize LRP fits for several trialssummary.lrp_multi
Summarize a method comparisonsummary.method_comparison
Summarize a trial checksummary.trial_check
Simulated uniformity trialuniformity_trial