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]
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')
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