Package {BKBreed}

BKBreed: Colourful Biometrical Analysis for Plant Breeding and Genetics

Description

A compact, colour-first toolkit for analysing plant-breeding and genetics field experiments: RBD, Factorial RBD and Augmented Alpha-Lattice designs; genetic variability, correlation and path analysis; Mahalanobis D-square divergence; and Eberhart-Russell / AMMI stability. Every analysis returns a tidy object and a publication-ready ggplot2 figure via bk_plot.

Author(s)

Praveen Kumar B. K. bkpraveenars@gmail.com

See Also

bk_rbd, bk_frbd, bk_augmented, bk_variability, bk_correlation, bk_path, bk_diversity, bk_stability, bk_plot.

Augmented Alpha-Lattice / Augmented RCBD analysis

Description

Analysis of augmented designs. Block effects are estimated by intra-block least squares from replicated checks; every genotype mean is adjusted and classical Federer standard errors are reported.

Usage

bk_augmented(data, trait, gen, block, rep = NULL, checks = NULL, alpha = 0.05)

Arguments

Value

An object of class bk_augmented with adjusted means, block effects, error MS and Federer critical differences.

Examples

d <- bk_data("augmented")
res <- bk_augmented(d, "grain_yield", "genotype", "block", "rep",
                    checks = c("CHK-1","CHK-2","CHK-3","CHK-4"))
res
bk_plot(res)

Genotypic and phenotypic correlation

Description

Genotypic and phenotypic correlation coefficients among traits, via analysis of covariance.

Usage

bk_correlation(data, traits, gen, rep)

Arguments

Value

An object of class bk_correlation with matrices rg and rp.

Examples

cr <- bk_correlation(bk_data("rbd"),
        c("grain_yield","plant_height","tillers","test_weight"),
        "genotype", "rep")
cr
bk_plot(cr)

Load a bundled BKBreed example dataset

Description

Convenience loader for the demonstration datasets shipped with the package.

Usage

bk_data(name = c("rbd", "frbd", "augmented", "mlt", "lxt", "diallel"))

Arguments

Value

A data frame.

Examples

head(bk_data("rbd"))

Griffing diallel combining-ability analysis (Method 2, Model I)

Description

Griffing's (1956) diallel analysis for a half diallel of parents plus one set of F1 crosses without reciprocals (Method 2), with genotypes as fixed effects (Model I). Standard errors are computed exactly by propagating the per-entry error variance through the linear GCA/SCA estimators.

Usage

bk_diallel(data, trait, parent1, parent2, rep, alpha = 0.05)

Arguments

Value

An object of class bk_diallel with the combining-ability ANOVA, parental GCA effects, the SCA matrix, exact standard errors, Baker's predictability ratio and variance components.

See Also

bk_lxt, bk_plot

Examples

res <- bk_diallel(bk_data("diallel"), trait = "grain_yield",
                  parent1 = "parent1", parent2 = "parent2", rep = "rep")
res
bk_plot(res)
bk_plot(res, type = "sca")

Mahalanobis D-square genetic divergence

Description

Computes Mahalanobis D-square distances among genotypes using the pooled error covariance, then groups them by Tocher and Ward clustering.

Usage

bk_diversity(data, traits, gen, rep,
             method = c("tocher", "hierarchical"), clusters = NULL)

Arguments

Value

An object of class bk_diversity with the D2 matrix, cluster memberships and cluster means.

Examples

dv <- bk_diversity(bk_data("rbd"),
        c("grain_yield","plant_height","tillers","test_weight"),
        "genotype", "rep")
dv
bk_plot(dv)

Two-factor Factorial RBD analysis

Description

ANOVA for a two-factor factorial in a randomised block design, with main-effect and interaction means and factor-specific critical differences.

Usage

bk_frbd(data, trait, factorA, factorB, rep, alpha = 0.05)

Arguments

Value

An object of class bk_frbd.

Examples

res <- bk_frbd(bk_data("frbd"), "grain_yield", "nitrogen", "variety", "rep")
res
bk_plot(res)

Line x Tester combining-ability analysis

Description

Kempthorne's Line x Tester analysis for l \times t crosses in a randomised block design. Partitions crosses into lines, testers and line x tester; estimates GCA and SCA effects; and derives additive and dominance variances (assuming inbred parents, F = 1).

Usage

bk_lxt(data, trait, line, tester, rep, alpha = 0.05)

Arguments

Value

An object of class bk_lxt with the combining-ability ANOVA, GCA effects for lines and testers, SCA effects, proportional contributions and variance components.

See Also

bk_plot

Examples

res <- bk_lxt(bk_data("lxt"), trait = "grain_yield",
              line = "line", tester = "tester", rep = "rep")
res
bk_plot(res)
bk_plot(res, type = "sca")

BKBreed colour palettes

Description

A curated set of high-contrast palettes used across all BKBreed figures.

Usage

bk_palette(name = c("field", "spectrum", "canopy", "sunrise", "earth"),
          n = NULL, reverse = FALSE)

Arguments

Value

A character vector of hex colours.

Examples

bk_palette("sunrise", 5)
bk_palette("spectrum", 11)

Path-coefficient analysis

Description

Partitions the correlation of each causal trait with a target trait into direct and indirect effects (Wright's path analysis).

Usage

bk_path(data, traits, dependent, gen, rep, type = c("genotypic", "phenotypic"))

Arguments

Value

An object of class bk_path with direct/indirect effects and the residual.

Examples

pa <- bk_path(bk_data("rbd"),
        c("plant_height","tillers","panicle_len","test_weight"),
        "grain_yield", "genotype", "rep")
pa
bk_plot(pa)

Draw the signature figure for a BKBreed result

Description

Generic entry point returning the publication-ready figure for whichever analysis produced x.

Usage

bk_plot(x, ...)

Arguments

Value

A ggplot2 object.

Examples

bk_plot(bk_rbd(bk_data("rbd"), "grain_yield", "genotype", "rep"))

Randomised Block Design (RBD) analysis

Description

One-call ANOVA for a randomised complete block design with SE, critical difference, CV and compact-letter groupings.

Usage

bk_rbd(data, trait, gen, rep, alpha = 0.05)

Arguments

Value

An object of class bk_rbd with elements anova, means, cv, sem, sed and cd.

See Also

bk_plot, bk_frbd

Examples

res <- bk_rbd(bk_data("rbd"), "grain_yield", "genotype", "rep")
res
bk_plot(res)

GxE stability analysis (Eberhart-Russell + AMMI)

Description

Multi-location trial analysis: combined ANOVA, Eberhart-Russell regression (bi, S2di) and an AMMI decomposition with IPCA scores for biplots.

Usage

bk_stability(data, trait, gen, env, rep)

Arguments

Value

An object of class bk_stability with combined ANOVA, Eberhart-Russell parameters and AMMI scores.

Examples

st <- bk_stability(bk_data("mlt"), "grain_yield",
                   "genotype", "environment", "rep")
st
bk_plot(st)
bk_plot(st, type = "er")

Genetic variability parameters

Description

Estimates GCV, PCV, ECV, broad-sense heritability, genetic advance and genetic advance as percent of mean for one or more traits.

Usage

bk_variability(data, traits, gen, rep, k = 2.063)

Arguments

Value

An object of class bk_variability (a data frame with metadata).

Examples

v <- bk_variability(bk_data("rbd"),
       c("grain_yield","plant_height","tillers","test_weight"),
       "genotype", "rep")
v
bk_plot(v)

Discrete BKBreed colour and fill scales

Description

Discrete colour and fill scales built on the BKBreed palettes.

Usage

scale_colour_bk(pal_name = "sunrise", reverse = FALSE, ...)

scale_color_bk(pal_name = "sunrise", reverse = FALSE, ...)

scale_fill_bk(pal_name = "sunrise", reverse = FALSE, ...)

Arguments

Value

A ggplot2 scale.

Examples

library(ggplot2)
ggplot(iris, aes(Sepal.Length, Sepal.Width, colour = Species)) +
  geom_point() + scale_colour_bk("sunrise")

A clean, high-contrast ggplot2 theme

Description

The BKBreed figure theme.

Usage

theme_bk(base_size = 12, base_family = "", grid = TRUE)

Arguments

Value

A ggplot2 theme object.

Examples

library(ggplot2)
ggplot(mtcars, aes(wt, mpg)) + geom_point() + theme_bk()