library(knitr) traits <- params$traits geno <- params$geno rep <- params$rep block <- params$block k <- params$k data <- params$data
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
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')
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