library(knitr) traits <- params$traits geno <- params$geno rep <- params$rep data <- params$data lc <- check.abd(traits[1], geno, rep, data)
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
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')) } }
r paste(knit(text = out), collapse = '\n')
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