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The blockerT function takes a dataframe that contains columns indicating x and y coordinates and use them to create transversal blocking with different numbers or row width. For example, a square field with no blocks after applying the function will have 2 new columns (forward and backwards) for the transversal blocking desired (angle degree and #rows specified).
blockerT(dat, d= 60, nr= 5, rows="ROW",
ranges="RANGE", by=NULL,
shiftF=0, shiftB=0)
a dataframe with 2 obligatory columns; rows and ranges which can have different names and can be matched with the following arguments.
angle degree used to draw the transversal blocks.
number of rows width of the block.
the name of the numeric column that indicates the x coordinate direction in the field.
the name of the numeric column that indicates the y coordinate other direction in the field.
optional argument to indicate the name of the column of the dataframe x that indicates the environments so the field is filled by environment.
an integer value indicating the number of rows or ranges to shift the block. If positive, the blocking is shifted to the right. If negative, the blocking is shifted to the right.
an integer value indicating the number of rows or ranges to shift the block. If positive, the blocking is shifted to the right. If negative, the blocking is shifted to the right.
a new dataframe with a 2 new columns for transversal blocking forward and backwards.
Covarrubias-Pazaran G (2016) Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6): doi:10.1371/journal.pone.0156744
Fikret Isik. 2009. Analysis of Diallel Mating Designs. North Carolina State University, Raleigh, USA.
# NOT RUN {
####=========================================####
#### For CRAN time limitations most lines in the
#### examples are silenced with one '#' mark,
#### remove them and run the examples using
#### command + shift + C |OR| control + shift + C
####=========================================####
data(CPdata)
#### look at the data
head(CPpheno)
#### fill the design
gg <- fill.design(x=CPpheno, rows="Row",ranges="Col")
head(gg)
#### apply the postblocking
gg2 <- blockerT(dat=gg, d= 60, nr= 5, rows="Col",ranges="Row")
head(gg2)
#### see the new blocking
# lattice::levelplot(TBLOCKB~Row*Col|FIELDINST, data=gg2)
# lattice::levelplot(TBLOCKF~Row*Col|FIELDINST, data=gg2)
#### now you can use them in your mixed models
# }
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