Title: | Genotypic Variance Components |
---|---|
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). |
Authors: | Muhammad Yaseen [aut, cre], Sami Ullah [aut, ctb] |
Maintainer: | Muhammad Yaseen <[email protected]> |
License: | GPL-3 |
Version: | 0.4.0 |
Built: | 2024-10-31 02:49:24 UTC |
Source: | https://github.com/myaseen208/gvcr |
The 'gvc' class calculates genotypic variance, phenotypic variance, and broad-sense heritability from replicated data.
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.
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.
new()
Initialize the 'gvc' class with the data and variable names.
gvc$new(.data, .y, .x = NULL, .rep, .gen, .env)
.data
A 'data.frame' containing the data for analysis.
.y
The response variable.
.x
The covariate (optional).
.rep
The replicate factor.
.gen
The genotype factor.
.env
The environmental factor.
An instance of the 'gvc' class.
calculate_gvar()
Calculate genetic variance.
gvc$calculate_gvar()
A list with the genetic variance ('gvar').
calculate_pvar()
Calculate phenotypic variance.
gvc$calculate_pvar()
A list with the phenotypic variance ('pvar').
calculate_herit()
Calculate broad-sense heritability.
gvc$calculate_herit()
A list with the heritability ('h2').
clone()
The objects of this class are cloneable with this method.
gvc$clone(deep = FALSE)
deep
Whether to make a deep clone.
df1 <- data.frame( Response = c( rnorm(48, mean = 15000, sd = 500) , rnorm(48, mean = 5000, sd = 500) , rnorm(48, mean = 1000, sd = 500) ) , Rep = as.factor(rep(1:3, each = 48)) , Variety = gl(n = 4, k = 4, length = 144, labels = letters[1:4]) , Env = gl(n = 3, k = 16, length = 144, labels = letters[1:3]) ) # Create an instance of the class gvc1 <- gvc$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$calculate_herit()
df1 <- data.frame( Response = c( rnorm(48, mean = 15000, sd = 500) , rnorm(48, mean = 5000, sd = 500) , rnorm(48, mean = 1000, sd = 500) ) , Rep = as.factor(rep(1:3, each = 48)) , Variety = gl(n = 4, k = 4, length = 144, labels = letters[1:4]) , Env = gl(n = 3, k = 16, length = 144, labels = letters[1:3]) ) # Create an instance of the class gvc1 <- gvc$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()
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).
Sami Ullah ([email protected])
Muhammad Yaseen ([email protected])
R.K. Singh and B.D.Chaudhary Biometrical Methods in Quantitative Genetic Analysis. Kalyani Publishers, New Delhi
Williams, E.R., Matheson, A.C. and Harwood, C.E. (2002).Experimental Design and Analysis for Tree Improvement. CSIRO Publishing.