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

library(knitr)

traits <- params$traits
A <- params$A
B <- params$B
rep <- params$rep
design <- params$design
data <- params$data
maxp <- params$maxp

data[, A] <- as.character(data[, A])
data[, B] <- as.character(data[, B])
data[, rep] <- as.character(data[, rep])

1. Model specification and data description

The data frame has two factors with r nlevels(as.factor(data[, A])) and r nlevels(as.factor(data[, B])) levels. The experimental design is a randomized complete block design with r nlevels(as.factor(data[, rep])) blocks. The statistical model is $$ y_{ijk} = \mu + \alpha_i + \beta_j + (\alpha\beta){ij} + \gamma_k + \epsilon{ijk} $$ where

• $y_{ijk}$ is the observed response with level $i$ of factor A, level $j$ of factor B, and block $k$.
• $\mu$ is the mean response over all levels of factor A, factor B, and blocks.
• $\alpha_i$ is the effect for level $i$ of factor A.
• $\beta_j$ is the effect for level $j$ of factor B.
• $(\alpha\beta)_{ij}$ is the interaction effect between level $i$ of factor A and level $j$ of factor B.
• $\gamma_k$ is the effect of block $k$.
• $\epsilon_{ijk}$ 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], A, B, rep, data)
  if (lc$c1 == 1 & lc$c2 == 1 & lc$c3 == 1 & lc$pmis <= maxp)
    out <- c(out, knit_expand('child_2f.Rmd'))
  else
    out <- c(out, knit_expand('child_2f_fail.Rmd'))
}

r paste(knit(text = out), collapse = '\n')

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