data(huasahuasi, package = "agricolae")
YIELD <- huasahuasi$YIELD
Block <- factor(YIELD$block) # replications
A <- factor(YIELD$trt) # row (horizontal strip) factor
B <- factor(YIELD$clon) # column (vertical strip) factor
AB <- factor(paste(A, B, sep = ":")) # A x B cell
y <- YIELD$y1da # response variable
strip_anovaTable <- agricolae::strip.plot(Block, A, B, y)
strip_result <- as.data.frame(strip_anovaTable$ANOVA)
strip_result
# Rows: 1 = Block, 2 = A, 3 = Ea, 4 = B, 5 = Eb, 6 = A:B, 7 = Ec
strip_anovaTable$Ea; strip_anovaTable$gl.a # row-factor error
strip_anovaTable$Eb; strip_anovaTable$gl.b # column-factor error
strip_anovaTable$Ec; strip_anovaTable$gl.c # interaction error
# Partial Cohen's f for A, B and A:B (rows 2, 3 and 4 of the fitted model)
model_lm <- lm(y ~ Block + A + B + A:B + Block:A + Block:B)
effectsize::cohens_f(model_lm)
Computational Provenance & Reproducibility Record Strip Plot Design · 2.0.0 · DOI 10.5281/zenodo.22202872
This Computational Provenance Record documents the statistical computing environment, software dependencies, computational provenance, and bibliographic references associated with the RAISINS Strip Plot Design module. It is intended to support computational reproducibility and software transparency. Detailed statistical methodology, mathematical derivations, and user guidance are provided separately in the official module documentation.
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1 Module Metadata
| Parameter | Specification |
|---|---|
| Module | Strip Plot Design |
| Module Version | 2.0.0 |
| DOI | 10.5281/zenodo.22202872 |
| Document Type | Computational workflow |
| Statistical Engine | R |
| R Version | 4.5.2 |
| Reproducibility | Execution Environment: GCP · renv-locked |
2 Statistical Dependency Manifest
| Package | Version | Repository | Core Statistical Functions |
|---|---|---|---|
| stats | 4.5.2 |
Base R | lm(), residuals(), shapiro.test(), ks.test(), sd(), var() |
| agricolae | 1.3-7 |
CRAN | strip.plot(), LSD.test(), HSD.test(), duncan.test() |
| effectsize | 1.0.3 |
CRAN | cohens_f() |
| phia | 0.3-2 |
CRAN | interactionMeans() |
| gtools | 3.9.5 |
CRAN | mixedsort() |
| dplyr | 1.2.1 |
CRAN | group_by(), summarise(), select() |
3 Statistical Function Registry
| Analytical Role | Primary Function(s) |
|---|---|
| Strip-plot (split-block) ANOVA | agricolae::strip.plot(Block, A, B, y) |
| Effect size (per term) | effectsize::cohens_f() on stats::lm(y ~ Block + A + B + A:B + Block:A + Block:B) |
| Post-hoc mean separation | agricolae::LSD.test() (default), agricolae::HSD.test(), agricolae::duncan.test() |
| Grouping-letter ordering | gtools::mixedsort() |
| Normality diagnostics (residuals) | stats::shapiro.test(), nortest::ad.test(), stats::ks.test() |
| Interaction means | phia::interactionMeans() |
4 Default Methods & Parameters
| Analysis | Step / Parameter | Default Method / Value |
|---|---|---|
| Strip-plot ANOVA ( agricolae::strip.plot()) |
Model | y ~ Block + A + Ea + B + Eb + A:B + Ec |
| Effect size ( effectsize::cohens_f()) |
Term effect size | Partial Cohen's f, taken from lm(y ~ Block + A + B + A:B + Block:A + Block:B) and reported for A, B and A×B |
| Level of significance | Significance level (α) | 0.05 default (user-selectable: 0.05, 0.01) |
| Mean comparison / post-hoc ( agricolae) |
Default method | LSD - agricolae::LSD.test() (alternatives: Tukey HSD HSD.test(); DMRT duncan.test()) |
| Grouping display | Group labels ordered with gtools::mixedsort() |
|
| Standard errors & CV | SEM | sqrt(E / r), where E is that term's error term (Ea for Row factor, Eb (for column factor) or Ec)(for row column interaction) |
| SED | sqrt(2 × E / r) |
|
| Coefficient of variation | Reported separately for each factors and interaction factor - cv(a), cv(b) and cv(c) | |
| Normality diagnostics (on model residuals) |
Default test | Shapiro–Wilk shapiro.test() (3 ≤ n ≤ 5000); Anderson–Darling nortest::ad.test() (n ≥ 7) and Kolmogorov–Smirnov ks.test() (n ≥ 2) also available. Residuals are taken from lm(y ~ A + B + A:B) |
| Data transformations (optional, per variable) |
Logarithmic | log10(x); if any value ≤ 0, log10(x - min(x) + 1) |
| Square-root | sqrt(x); if any value = 0, sqrt(x + 0.5); blocked on negative values |
|
| Arcsine | asin(sqrt(x)) on proportions in [0, 1]; 0 and 1 replaced by 1/(4n) and 1 - 1/(4n); blocked outside [0, 1] |
5 R Code for Key Analytical Steps
The code blocks below demonstrate the exact computation behind each reported result using huasahuasi, a strip-plot field trial from the agricolae package: 3 blocks × 3 row treatments (trt) × 5 column treatments (clon) = 45 plots, the fully balanced strip plot layout.
5.1 Strip-Plot ANOVA and Effect Size
5.2 Post-hoc Mean Comparison (LSD default; Tukey / DMRT alternatives)
strip_anovaTable <- agricolae::strip.plot(Block, A, B, y)
# Least Significant Difference (default).
# OUT
strip_out <- agricolae::LSD.test(y, A, strip_anovaTable$gl.a, strip_anovaTable$Ea,
alpha = 0.05)
strip_outB <- agricolae::LSD.test(y, B, strip_anovaTable$gl.b, strip_anovaTable$Eb,
alpha = 0.05)
strip_outAB <- agricolae::LSD.test(y, AB, strip_anovaTable$gl.c, strip_anovaTable$Ec,
alpha = 0.05)
# Grouping letters, ordered naturally
strip_out$groups[gtools::mixedsort(rownames(strip_out$groups)), ]
# statistics: MSerror, Df, Mean, CV, t.value, LSD -> CV is column 4, CD is column 6
strip_stat <- strip_out$statistics
strip_stat
# Alternatives selectable in the app (same DFerror / MSerror / alpha arguments):
# Tukey HSD -> agricolae::HSD.test(y, A, strip_anovaTable$gl.a, strip_anovaTable$Ea,
# alpha = strip_alpha) # MSD is column 5
# DMRT -> agricolae::duncan.test(y, A, strip_anovaTable$gl.a, strip_anovaTable$Ea,
# alpha = strip_alpha)$duncan
# Standard errors from that term's error stratum (r = replications per level)
strip_r <- strip_out$means[1, 3]
strip_SEM <- sqrt(strip_stat[1, 1] / strip_r)
strip_SED <- sqrt((2 * strip_stat[1, 1]) / strip_r)5.3 Interaction Means
# Adjusted cell means and their standard errors behind the interaction plot
lm1 <- lm(y ~ A + B + A:B)
IM <- phia::interactionMeans(lm1)
IM
plot(IM)Explore the entire Strip Plot Design module in preview mode using our demo datasets. To submit suggestions or report a workflow issue, please use the discussion section below, or visit the official RAISINS website.
6 RAISINS Native Statistical Framework
RAISINS uses R for all its statistical computations. Every package used to generate major results is listed and demonstrated with examples, so results can be reproduced independently. These results are then organized and formatted on the RAISINS website along with visualisation to make them easier to use and interpret. RAISINS also has its own custom-built statistical tools for managing workflows, validating results, and generating reports. Details of these are not fully covered here, they’re shared with outside researchers only on request, and are subject to licensing terms.
7 Package References
R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
de Mendiburu, F. (2023). agricolae: Statistical Procedures for Agricultural Research (R package version 1.3-7). https://doi.org/10.32614/CRAN.package.agricolae
Ben-Shachar, M. S., Lüdecke, D., & Makowski, D. (2020). effectsize: Estimation of Effect Size Indices and Standardized Parameters (R package version 1.0.3). https://doi.org/10.32614/CRAN.package.effectsize
Gross, J., & Ligges, U. (2015). nortest: Tests for Normality (R package version 1.0-4). https://doi.org/10.32614/CRAN.package.nortest
De Rosario-Martinez, H. (2015). phia: Post-Hoc Interaction Analysis (R package version 0.3-2). https://doi.org/10.32614/CRAN.package.phia
Peterson, B. G., & Carl, P. (2020). PerformanceAnalytics: Econometric Tools for Performance and Risk Analysis (R package version 2.1.0). https://doi.org/10.32614/CRAN.package.PerformanceAnalytics
Warnes, G. R., Bolker, B., & Lumley, T. (2023). gtools: Various R Programming Tools (R package version 3.9.5). https://doi.org/10.32614/CRAN.package.gtools
Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. (2025). dplyr: A Grammar of Data Manipulation (R package version 1.2.1). https://doi.org/10.32614/CRAN.package.dplyr
Wickham, H., Vaughan, D., & Girlich, M. (2025). tidyr: Tidy Messy Data (R package version 1.3.2). https://doi.org/10.32614/CRAN.package.tidyr