Type: Package
Title: Genotypic Variance Components
Version: 0.4.0
Maintainer: Muhammad Yaseen <myaseen208@gmail.com>
Description: Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).
Depends: R (≥ 3.2.3)
Imports: dplyr, eda4treeR, lme4, magrittr, rlang, stats, supernova, tibble, R6
License: GPL-3
URL: https://myaseen208.com/gvcR/ https://github.com/MYaseen208/gvcR
BugReports: https://github.com/myaseen208/gvcR/issues
RoxygenNote: 7.3.2
Encoding: UTF-8
Suggests: testthat
NeedsCompilation: no
Packaged: 2024-09-30 21:23:22 UTC; myaseen208
Author: Muhammad Yaseen [aut, cre], Sami Ullah [aut, ctb]
Repository: CRAN
Date/Publication: 2024-10-01 06:50:02 UTC

Genotypic Variance

Description

The 'gvc' class calculates genotypic variance, phenotypic variance, and broad-sense heritability from replicated data.

Details

The 'gvc' class uses methods explained by Burton, G. W. & Devane, E. H. (1953) and Allard, R.W. (2010). It includes methods for calculating genetic variance, phenotypic variance, and heritability.

Public fields

data

A 'tibble' containing the data for analysis.

y

The name of the response variable.

x

The name of the covariate (optional).

rep

The name of the replicate factor.

gen

The name of the genotype factor.

env

The name of the environmental factor.

Methods

Public methods

Method new()

Initialize the 'gvc' class with the data and variable names.

Usage
gvc$new(.data, .y, .x = NULL, .rep, .gen, .env)
Arguments
.data

A 'data.frame' containing the data for analysis.

.y

The response variable.

.x

The covariate (optional).

.rep

The replicate factor.

.gen

The genotype factor.

.env

The environmental factor.

Returns

An instance of the 'gvc' class.

Method calculate_gvar()

Calculate genetic variance.

Usage
gvc$calculate_gvar()
Returns

A list with the genetic variance ('gvar').

Method calculate_pvar()

Calculate phenotypic variance.

Usage
gvc$calculate_pvar()
Returns

A list with the phenotypic variance ('pvar').

Method calculate_herit()

Calculate broad-sense heritability.

Usage
gvc$calculate_herit()
Returns

A list with the heritability ('h2').

Method clone()

The objects of this class are cloneable with this method.

Usage
gvc$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

df1 <- data.frame(
           Response = c(
                          rnorm(48, mean = 15000, sd = 500)
                        , rnorm(48, mean =  5000, sd = 500)
                        , rnorm(48, mean =  1000, sd = 500)
                       )
        , Rep      = as.factor(rep(1:3, each = 48))
        , Variety  = gl(n = 4, k =  4, length = 144, labels = letters[1:4])
        , Env      = gl(n = 3, k = 16, length = 144, labels = letters[1:3])
        )

# Create an instance of the class
gvc1 <- gvc$new(
           .data = df1
         , .y    = Response
         , .rep  = Rep
         , .gen  = Variety
         , .env  = Env
         )

# Calculate genetic variance (gvar)
gvc1$calculate_gvar()

# Calculate phenotypic variance (pvar)
gvc1$calculate_pvar()

# Calculate heritability (h2)
gvc1$calculate_herit()

Genotypic Variance Components

Description

Functionalities to compute model based genetic components i.e genotypic, phenotypic variances and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).

Author(s)

  1. Sami Ullah (samiullahuos@gmail.com)

  2. Muhammad Yaseen (myaseen208@gmail.com)

References

  1. R.K. Singh and B.D.Chaudhary Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi

  1. Williams, E.R., Matheson, A.C. and Harwood, C.E. (2002).Experimental Design and Analysis for Tree Improvement. CSIRO Publishing.