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$dfr
nla <- out$nl[1]
nlb <- out$nl[2]
nrep <- out$nrep
nmis.fac <- out$nmis.fac

dfr[, factors[1]] <- as.character(dfr[, factors[1]])
dfr[, factors[2]] <- as.character(dfr[, factors[2]])
dfr[, rep] <- as.character(dfr[, rep])

1. Model specification and data description

The data frame has two factors with r nla and r nlb levels. The experimental design is a randomized complete block design with r nrep blocks. The statistical model is $$ y_{ijk} = \mu + \alpha_i + \beta_j + (\alpha\beta){ij} + \gamma_k + \epsilon{ijk} $$ where

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(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'))
  }
}

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



CIP-RIU/hidap documentation built on April 30, 2021, 9:21 p.m.