Package index

Pairwise Comparison Criteria

Compute Tukey HSD, LSD, or Bonferroni comparison criteria from ASReml-R V4 predicted values, and visualise the results as a dot plot, half-criterion error bars, compact letter display, or pairwise heatmap.

compare()

Multiple Comparison Criteria for ASReml-R Predicted Values

plot_compare()

Plot Output from compare()

as_plotly()

Convert a pc_interactive Object to a Plotly Widget

Wald Tests on Fixed-Effect Contrasts

Test user-specified linear contrasts of predicted values using Wald chi-squared or approximate F statistics, with optional multiplicity adjustment. Forest-plot visualisation included.

waldTest() waldTest.asreml()

Wald Tests for Fixed-Effect Contrasts Using Predicted Values

plot_waldTest()

Forest Plot for Wald Test Results

Random Regression

Decompose variety BLUPs into efficiency and responsiveness components via G-matrix conditioning (multivariate random regression).

randomRegress()

Multivariate Random Regression of Treatment BLUPs Within Environments

plot_randomRegress()

Plot Output from randomRegress()

Fixed Regression

OLS regression of treatment BLUEs across environments (multivariate fixed-effects regression).

fixedRegress()

Multivariate Fixed-Effects Regression of Treatment BLUEs Within Groups

plot_fixedRegress()

Plot Output from fixedRegress()

Factor Analytic Selection Tools

FAST and interaction class (iClass) approaches derived from Factor Analytic mixed models.

fast()

Factor Analytic Selection Tools: FAST and iClass Analysis

BLUP Accuracy

Compute model-based BLUP accuracy (Mrode) and Cullis H2 from ASReml-R V4 mixed models, with six plot types for single-model summaries and head-to-head model comparisons.

accuracy()

Model-Based BLUP Accuracy for ASReml-R V4 Multi-Environment Trials

plot_accuracy()

Plot accuracy results from accuracy()

pc_add()

Add a ggplot2 Layer to a pc_interactive Object

Field Trial Utilities

Simulate balanced or unbalanced multi-environment trial data with a realistic genetic covariance structure, visualise the simulated design and GEI surface, and extract or pad field trial layouts from partially observed grids.

simTrialData()

Simulate Multi-Environment Plant Breeding Trial Data

plot_simTrialData()

Plot simulated trial data from simTrialData()

padTrial()

Extract and Pad a Sub-Trial from a Field Trial Layout

plot_padTrial()

Before/After Field Layout Plot for padTrial() Results