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

library(knitr)

# Pass arguments

traits <- params$traits
geno <- params$geno
dfr <- params$dfr
maxp <- params$maxp

# Check factors structure

out <- ck.fs(geno, NULL, dfr)
ng <- out$nl[[1]]
dfr <- out$dfr
nmis.fac <- out$nmis.fac

1. Model specification and data description

There are data from r ng genotypes, evaluated using a completely randomized design. The statistical model is $$ y_{ij} = \mu + \tau_i + \epsilon_{ij} $$ where

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

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

r if (nmis.fac == 1) paste("Note: There is", nmis.fac, "row with missing values for classifications factors. This row has been deleted.") r if (nmis.fac > 1) paste("Note: There are", nmis.fac, "rows with missing values for classifications factors. These rows have been deleted.")

out <- NULL
for (i in 1:length(traits)) {
  lc <- ck.crd(traits[i], geno, dfr)
  if (lc$ng.0 == 0 & lc$nrep > 1 & lc$ng > 2) {
    out <- c(out, knit_expand('child_crd.Rmd'))
  } else {
    out <- c(out, knit_expand('child_crd_fail.Rmd'))
  }
}