DD {lmDiallel}

Dominant Deviation effect

Description

DD effect to fit Hayman2 model with lm function

Usage

DD(P1, P2, type = "fix", data)

Arguments

P1

a variable for the first parent

P2

a variable for the second parent

type

a variable for model selection. May be "fix" (fixed model) or "random" (random model).

data

a 'data.frame' where to look for explanatory variables

Details

a design matrix of n-1 parentals

Value

A design matrix for the DD effect

Author(s)

Andrea Onofri, Niccolo' Terzaroli , Luigi Russi

References

Onofri, A., Terzaroli, N. & Russi, L. Linear models for diallel crosses: a review with R functions. Theor Appl Genet (2020). https://doi.org/10.1007/s00122-020-03716-8

Examples

data("hayman54")
contrasts(hayman54$Block) <- "contr.sum"
dMod <- lm(Ftime ~ Block + GCA(Par1, Par2) + MDD(Par1, Par2) +
             DD(Par1, Par2) + SCA(Par1, Par2) +
             RGCA(Par1, Par2) + RSCA(Par1, Par2), data = hayman54)
summary(dMod)