simul.phenotype: Simulation of a field trial with single trait
In synbreed: Framework for the Analysis of Genomic Prediction Data using R

Description Usage Arguments Details Value Author(s) See Also Examples

Simulates observations from a field trial using an animal model. The field trial consists of multiple locations and randomized complete block design within locations. A single quantitative trait is simulated according to the model Trait ~ id(A) + block + loc + e.

1 2	simul.phenotype(pedigree = NULL, A = NULL, mu = 100, vc = NULL, Nloc = 1, Nrepl = 1)

`pedigree`	object of class "pedigree"
`A`	object of class "relationshipMatrix"
`mu`	`numeric`; Overall mean of the trait.
`vc`	list containing the variance components. `vc` consists of elements `sigma2e`, `sigma2a`, `sigma2l`, `sigma2b` with the variance components of the residual, the additive genetic effect, the location effect and the block effect.
`Nloc`	`numeric`. Number of locations in the field trial.
`Nrepl`	`Numeric`. Number of complete blocks within location.

Either pedigree or A must be specified. If pedigree is given, pedigree information is used to set up numerator relationship matrix with function kinship. If unrelated individuals should be used for simulation, use identity matrix for A. True breeding values for N individuals is simulated according to following distribution

tbv = chol(A)*sigma2a*rnorm(N,0,1)

Observations are simulated according to

If no location or block effects should appear, use sigma2l=0 and/or sigma2b=0.

A data.frame with containing the simulated values for trait and the following variables

`ID`	Factor identifying the individuals. Names are extracted from `pedigree` or rownames of `A`
`Loc`	Factor for Location
`Block`	Factor for Block within Location
`Trait`	Trait observations
`TBV`	Simulated values for true breeding values of individuals

Results are sorted for locations within individuals.

Valentin Wimmer

simul.pedigree

## Not run: 
ped <- simul.pedigree(gener=5)
varcom <- list(sigma2e=25,sigma2a=36,sigma2l=9,sigma2b=4)
# field trial with 3 locations and 2 blocks within locations
data.simul <- simul.phenotype(ped,mu=10,vc=varcom,Nloc=3,Nrepl=2)
head(data.simul)
# analysis of variance
anova(lm(Trait~ID+Loc+Loc:Block,data=data.simul))

## End(Not run)

   ID Loc Block    Trait      TBV
1 ID1   1     1 15.34203 4.903431
2 ID1   1     2 14.28618 4.903431
3 ID1   2     1 15.45593 4.903431
4 ID1   2     2 15.03316 4.903431
5 ID1   3     1 23.67578 4.903431
6 ID1   3     2 18.38217 4.903431
Analysis of Variance Table

Response: Trait
          Df Sum Sq Mean Sq F value    Pr(>F)    
ID        19 3267.2  171.96  7.8347 2.015e-12 ***
Loc        2 1069.7  534.87 24.3698 2.864e-09 ***
Loc:Block  3  234.4   78.13  3.5596   0.01715 *  
Residuals 95 2085.1   21.95                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1