gvc_pvar: Phenotypic Variance

In myaseen208/gvcR: Genotypic Variance Components

Description

gvc_pvar computes phenotypic 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).

Usage

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

## Default S3 method:
gvc_pvar(.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

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
pvar1 <-
  gvc_pvar(
          .data  = df1
         , .y    = Response
         , .rep  = Rep
         , .gen  = Variety
         , .env  = Env
          )
pvar1

library(eda4treeR)
data(DataExam6.2)
pvar2 <-
  gvc_pvar(
          .data  = DataExam6.2
         , .y    = Dbh.mean
         , .rep  = Replication
         , .gen  = Family
         , .env  = Province
         )
pvar2

myaseen208/gvcR documentation built on May 29, 2019, 3:17 p.m.