gvc_pvar {gvcR}

Phenotypic Variance

Description

gvc_pvar computes phenotypic variances 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).

Usage

gvc_pvar(y, x = NULL, rep, geno, env, data)

Arguments

y

Response

x

Covariate by default NULL

rep

Repliction

geno

Genotypic Factor

env

Environmental Factor

data

data.frame

Value

Phenotypic Variance

Author(s)

1. Sami Ullah ([e-mail])

2. Muhammad Yaseen ([e-mail])

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.

Examples

set.seed(12345)
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])
df1      <- data.frame(Response, Rep, Variety, Env)

#' # Penotypic Variance
pvar <-
  gvc_pvar(
            y    = Response
          , rep  = Rep
          , geno = Variety
          , env  = Env
          , data = df1
          )
pvar

library(eda4treeR)
data(DataExam6.2)
pvar <-
  gvc_pvar(
           y    = Dbh.mean
         , rep  = Replication
         , geno = Family
         , env  = Province
         , data = DataExam6.2
         )
pvar