In reyzaguirre/pepa: Package for the Execution of Pre Cooked Analysis

library(knitr)
opts_chunk$set(echo = FALSE, comment = NA)

# Pass arguments

dfr <- params$dfr
vars <- params$vars
trt <- params$trt
env <- params$env
rep <- params$rep
maxp <- params$maxp

# Check factors structure

out <- ck.fs(dfr, c(trt, env), rep)
dfr <- out$dfr
nl <- out$nl
nrep <- out$nrep
nmis.fac <- out$nmis.fac

# Everything as character

dfr[, trt] <- as.character(dfr[, trt])
dfr[, env] <- as.character(dfr[, env])
dfr[, rep] <- as.character(dfr[, rep])

1. Model specification and data description

The data frame has r nl[2] environments and r nl[1] treatments. In each environment the treatments 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

$y_{ijk}$ is the observed response with treatment $i$, environment $j$, and block $k$ nested in environment $j$.
$\mu$ is the mean response over all treatments, environments and blocks.
$\alpha_i$ is the effect for treatment $i$.
$\beta_j$ is the effect for environment $j$.
$(\alpha\beta)_{ij}$ is the interaction effect between treatment $i$ 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, $\epsilon_{ijk} \sim N(0,\sigma_{\epsilon}^2)$.

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(vars)) {
  lc <- ck.f(dfr, vars[i], c(trt, env), rep)
  if (lc$nt.0 == 0 & lc$nrep > 1 & lc$nt.mult == 0 & lc$pmis <= maxp) {
    out <- c(out, knit_expand('child_met_agro.Rmd'))
  } else {
    out <- c(out, knit_expand('child_met_agro_fail.Rmd'))
  }
}