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

library(knitr)

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

lc <- check.abd(traits[1], geno, rep, data)

1. Model specification and data description

There are data for r lc$nt.new genotypes tested using an augmented block design with r lc$nr blocks and r lc$nt.check checks in each block. The statistical model is $$ y_{ij} = \mu + \tau_i + \beta_j + \epsilon_{ij} $$ where

$y_{ij}$ is the observed response with genotype $i$ and block $j$.
$\mu$ is the mean response over all genotypes and blocks.
$\tau_i$ is the effect for genotype $i$.
$\beta_j$ is the effect for block $j$.
$\epsilon_{ij}$ is the error term.

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

out <- NULL

for (i in 1:length(traits)) {

  lc <- check.abd(traits[i], geno, rep, data)

  if (lc$nt.check.2 > 1) {
    out <- c(out, knit_expand('child_abd.Rmd'))
  } else {
    out <- c(out, knit_expand('child_abd_fail.Rmd'))
  }
}