library(knitr) # Pass arguments traits <- params$traits geno <- params$geno env <- params$env rep <- params$rep dfr <- params$dfr maxp <- params$maxp # Check factors structure out <- ck.fs(c(geno, env), rep, dfr) dfr <- out$dfr nl <- out$nl nrep <- out$nrep nmis.fac <- out$nmis.fac # Everything as character dfr[, geno] <- as.character(dfr[, geno]) dfr[, env] <- as.character(dfr[, env]) dfr[, rep] <- as.character(dfr[, rep])
The data frame has r nl[2]
environments and r nl[1]
genotypes. In each environment the genotypes were evaluated using a randomized complete block design with r nrep
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, $\epsilon_{ijk} \sim N(0,\sigma_{\epsilon}^2)$.
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.f(traits[i], c(geno, env), rep, dfr) if (lc$nt.0 == 0 & lc$nrep > 1 & lc$nt.mult == 0 & lc$pmis <= maxp) { out <- c(out, knit_expand('child_met.Rmd')) } else { out <- c(out, knit_expand('child_met_fail.Rmd')) } }
r paste(knit(text = out), collapse = '\n')
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