library(knitr) traits <- params$traits geno <- params$geno env <- params$env rep <- params$rep data <- params$data maxp <- params$maxp
There is data from r nlevels(as.factor(data[, env]))
environments and r nlevels(as.factor(data[, geno]))
genotypes. In each environment the genotypes were evaluated using a randomized complete block design with r nlevels(as.factor(data[, rep]))
blocks. The statistical model is
$$
y_{ijk} = \mu + \alpha_i + \beta_j + (\alpha\beta){ij} + \gamma{k(j)} + \epsilon_{ijk}
$$
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_met.Rmd'))
r paste(knit(text = out), collapse = '\n')
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