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).
gvc_pvar(.data, .y, .x = NULL, .rep, .gen, .env) # S3 method for default gvc_pvar(.data, .y, .x = NULL, .rep, .gen, .env)
.data | data.frame |
---|---|
.y | Response |
.x | Covariate by default NULL |
.rep | Repliction |
.gen | gentypic Factor |
.env | Environmental Factor |
Phenotypic Variance
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.
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#> $pvar #> [1] 319911.6 #>library(eda4treeR) data(DataExam6.2) pvar2 <- gvc_pvar( .data = DataExam6.2 , .y = Dbh.mean , .rep = Replication , .gen = Family , .env = Province ) pvar2#> $pvar #> [1] NA #>