Description Usage Arguments Value Author(s) References Examples
gvc_gvar computes genotypic 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).
1 |
y |
Response |
x |
Covariate by default NULL |
rep |
Repliction |
geno |
Genotypic Factor |
env |
Environmental Factor |
data |
data.frame |
Genotypic Variance
Sami Ullah (samiullahuos@gmail.com)
Muhammad Yaseen (myaseen208@gmail.com)
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | 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)
# Genotypic Variance
gvar <-
gvc_gvar(
y = Response
, rep = Rep
, geno = Variety
, env = Env
, data = df1
)
gvar
library(eda4treeR)
data(DataExam6.2)
gvar <-
gvc_gvar(
y = Dbh.mean
, rep = Replication
, geno = Family
, env = Province
, data = DataExam6.2
)
gvar
|
boundary (singular) fit: see ?isSingular
$gvar
[1] 0
$gvar
[1] 0.3513914
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