library(knitr)

# Pass arguments

traits <- params$traits
geno <- params$geno
rep <- params$rep
dfr <- params$dfr

# Remove rows with missing values for factors

out <- ck.fs(geno, rep, dfr)
dfr <- out$dfr
nmis.fac <- out$nmis.fac

# Identify checks and no checks

temp <- data.frame(table(dfr[, geno]))
lg.ck <- temp[temp$Freq > 1, 1]
lg <- temp[temp$Freq == 1, 1]

# Number of checks, no checks, and replications

ng.ck <- length(lg.ck)
ng <- length(lg)
nrep <- length(unique(dfr[, rep]))

1. Model specification and data description

There are data for ng genotypes tested using an augmented block design with nrep blocks and ng.ck checks in each block. The statistical model is $$ y_{ij} = \mu + \tau_i + \beta_j + \epsilon_{ij} $$ where

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.abd(traits[i], geno, rep, dfr)

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

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



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