In CIP-RIU/hidap: High Interactive Data Analysis Platform for Plant breeding

library(knitr)

traits <- params$traits
geno <- params$geno
rep <- params$rep
block <- params$block
k <- params$k
data <- params$data

1. Model specification and data description

There are data for r nlevels(as.factor(data[, geno])) genotypes tested using an alpha (0,1) design with r nlevels(as.factor(data[, rep])) replications and r nlevels(as.factor(data[, block])) incomplete blocks. In this design each replication is a complete block for the genotypes that is splitted in several incomplete blocks. The statistical model is $$ y_{ijk} = \mu + \tau_i + \gamma_j + \rho_{k(j)} + \epsilon_{ijk} $$ where

• $y_{ijk}$ is the observed response with genotype $i$, replication $j$, incomplete block $k$.
• $\mu$ is the mean response over all genotypes and replications.
• $\tau_i$ is the effect for genotype $i$.
• $\gamma_j$ is the effect for replication $j$.
• $\rho_{k(j)}$ is the effect of the incomplete block $k$ which is nested into replication $j$.
• $\epsilon_{ijk}$ is the error term.

In this model we assume that the errors are independent and have a normal distribution with common variance, that is, $\epsilon_{ijk} \sim N(0,\sigma_{\epsilon}^2)$.

out <- NULL
for (i in 1:length(traits))
  out <- c(out, knit_expand('child_a01d.Rmd'))

r paste(knit(text = out), collapse = '\n')

CIP-RIU/hidap documentation built on April 30, 2021, 9:21 p.m.