It relates to the difference between the average yield of selfed parents and the average yield of crosses. DD effect to fit Hayman2 model with <code>lm</code> function

 ~diallel 

 ~genetic effects 

Several service functions to be used to analyse datasets obtained from diallel experiments within the frame of linear models in R, as described in Onofri et al (2020) <DOI:10.1007/s00122-020-03716-8>.

Andrea Onofri

lmDiallel

Linear Fixed/Mixed Effects Models for Diallel Crosses

Niccolo Terzaroli

MDD function

<dl> <dt>P1</dt>
<dd><code>a variable for the first parent</code></dd> <dt>P2</dt>
<dd><code>a variable for the second parent</code></dd> <dt>type</dt>
<dd><code>a variable for the model</code></dd> <dt>data</dt>
<dd><code>a 'data.frame' where to look for explanatory variables</code></dd></dl>

Arguments

Andrea Onofri, Niccolo' Terzaroli, Luigi Russi

Author

Mean Dominance Deviation effect — MDD

<dl>

 <dt>P1</dt>
<dd><code>a variable for the first parent</code></dd>

 <dt>P2</dt>
<dd><code>a variable for the second parent</code></dd>

 <dt>type</dt>
<dd><code>a variable for the model</code></dd>

 <dt>data</dt>
<dd><code>a 'data.frame' where to look for explanatory variables</code></dd>

</dl>

MDD: Mean Dominance Deviation effect

Description

Usage

Value

Arguments

Author

References

Examples