library(knitr) # Pass arguments traits <- params$traits geno <- params$geno rep <- params$rep block <- params$block k <- params$k dfr <- params$dfr # Check factors structure out <- ck.fs(c(geno, block), rep, dfr) ng <- out$nl[[1]] nrep <- out$nrep nib <- out$nl[[2]] dfr <- out$dfr nmis.fac <- out$nmis.fac # Define internal variables block <- dfr[, block] geno <- dfr[, geno] rep <- dfr[, rep]
There are data for r ng
genotypes tested using an alpha (0,1) design with r nrep
replications and r nib
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, $\epsilon_{ijk} \sim N(0,\sigma_{\epsilon}^2)$.
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)) out <- c(out, knit_expand('child_a01d.Rmd'))
r paste(knit(text = out), collapse = '\n')
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