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

vars <- params$vars
rowf <- params$rowf
colf <- params$colf
rep <- params$rep
dfr <- params$dfr

# Check factors structure

out <- ck.fs(dfr, c(rowf, colf), rep)
nrf <- out$nl[[1]]
ncf <- out$nl[[2]]
nrep <- out$nrep
dfr <- out$dfr
nmis.fac <- out$nmis.fac

# Define internal variables

rowf <- dfr[, rowf]
colf <- dfr[, colf]
rep <- dfr[, rep]

1. Model specification and data description

There are data for a strip plot design with r nrf level for the row factor, r ncf levels for the column factor, and r nrep replications. In this design each replication is a complete block for the levels of each factor. The statistical model is $$ y_{ijk} = \mu + \alpha_i + \beta_j + \gamma_k + (\alpha\beta){ij} + (\alpha\gamma){ik} + (\beta\gamma){jk} + (\alpha\beta\gamma){ijk} $$ where

In this model $(\alpha\gamma){ik}$, $(\beta\gamma){jk}$, and $(\alpha\beta\gamma)_{ijk}$ are the errors used to test the row factor, the column factor, and the row and column factors interaction respectively.

r if (nmis.fac == 1) paste("Note: There is", nmis.fac, "data row with missing values for classifications factors. This row has been deleted.") r if (nmis.fac > 1) paste("Note: There are", nmis.fac, "data rows with missing values for classifications factors. These rows have been deleted.")

out <- NULL
for (i in 1:length(vars))
  out <- c(out, knit_expand('child_strd.Rmd'))

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



reyzaguirre/pepa documentation built on March 29, 2025, 9:56 p.m.