library(knitr) # Pass arguments traits <- params$traits geno <- params$geno rep <- params$rep dfr <- params$dfr # Remove rows with missing values for factors out <- ck.fs(geno, rep, dfr) dfr <- out$dfr nmis.fac <- out$nmis.fac # Identify checks and no checks temp <- data.frame(table(dfr[, geno])) lg.ck <- temp[temp$Freq > 1, 1] lg <- temp[temp$Freq == 1, 1] # Number of checks, no checks, and replications ng.ck <- length(lg.ck) ng <- length(lg) nrep <- length(unique(dfr[, rep]))
There are data for ng
genotypes tested using an augmented block design with nrep
blocks and ng.ck
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, $\epsilon_{ij} \sim N(0,\sigma_{\epsilon}^2)$.
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.abd(traits[i], geno, rep, dfr) if (lc$nck.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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