gentypic Variance

Source: R/gvc_gvar.R

gvc_gvar computes gentypic variances for given traits of different gentypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) and Allard, R.W. (2010).

gvc_gvar(.data, .y, .x = NULL, .rep, .gen, .env)

# S3 method for default
gvc_gvar(.data, .y, .x = NULL, .rep, .gen, .env)

Arguments

.data

data.frame
.y

Response
.x

Covariate by default NULL
.rep

Repliction
.gen

gentypic Factor
.env

Environmental Factor

Value

gentypic Variance

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)

# gentypic Variance
gvar1 <-
 gvc_gvar(
          .data  = df1
         , .y    = Response
         , .rep  = Rep
         , .gen  = Variety
         , .env  = Env
         )
gvar1
#> $gvar
#> [1] 3.34096e-12
#> 

library(eda4treeR)
data(DataExam6.2)
gvar2 <-
  gvc_gvar(
          .data  = DataExam6.2
         , .y    = Dbh.mean
         , .rep  = Replication
         , .gen  = Family
         , .env  = Province
         )
gvar2
#> $gvar
#> [1] 0.3513914
#>