In CIP-RIU/hidap: High Interactive Data Analysis Platform for Plant breeding

library(knitr)

# Pass arguments

traits <- params$traits
factors <- params$factors
rep <- params$rep
design <- params$design
dfr <- params$dfr
maxp <- params$maxp

# Check factors structure

out <- ck.fs(factors, rep, dfr)
dfr <- out$dfr
nf <- out$nf
nl <- out$nl
nrep <- out$nrep
nmis.fac <- out$nmis.fac

# Texts for levels

text.levels <- out$nl[1]
for (i in 2:(nf - 1))
  text.levels <- paste0(text.levels, ', ', nl[i])
text.levels <- paste0(text.levels, ', and ', nl[nf])

# Text for design

if (design == 'crd')
  text.design <- 'completely randomized design'
if (design == 'rcbd')
  text.design <- 'randomized complete block design'

# Everything as character

for (i in 1:nf) 
  dfr[, factors[i]] <- as.character(dfr[, factors[i]])
dfr[, rep] <- as.character(dfr[, rep])

1. Model specification and data description

The data frame has data for a full factorial with r nf factors with r text.levels levels. The experimental design is a r text.design with r nrep replications. In this model we assume that the errors are independent and have a normal distribution with common variance.

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], factors, rep, dfr)
  if (lc$nt.0 == 0 & lc$nrep > 1 & lc$nt.mult == 0 & lc$pmis <= maxp) {
    out <- c(out, knit_expand('child_f.Rmd'))
  } else {
    out <- c(out, knit_expand('child_f_fail.Rmd'))
  }
}