In AGROFIMS/hagrofims: Shiny App main application for the Agronomy Field Information Management System (AgroFIMS)

library(knitr)

traits <- params$traits
geno <- params$geno
env <- params$env
rep <- params$rep
data <- params$data
maxp <- params$maxp

data[, geno] <- as.character(data[, geno])
data[, env] <- as.character(data[, env])
data[, rep] <- as.character(data[, rep])

1. Model specification and data description

The data frame has 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

• $y_{ijk}$ is the observed response with genotype $i$, environment $j$, and block $k$ nested in environment $j$.
• $\mu$ is the mean response over all genotypes, environments and blocks.
• $\alpha_i$ is the effect for genotype $i$.
• $\beta_j$ is the effect for environment $j$.
• $(\alpha\beta)_{ij}$ is the interaction effect between genotype $i$ and environment $j$.
• $\gamma_{k(j)}$ is the effect of block $k$ nested in environment $j$.
• $\epsilon_{ijk}$ is the error term.

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)) {
  lc <- check.2f(traits[i], geno, env, rep, data)
  if (lc$c1 == 1 & lc$c2 == 1 & lc$c3 == 1 & lc$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')

AGROFIMS/hagrofims documentation built on May 6, 2020, 7:43 p.m.