batchS: Run batch analysis for single trait in R.

In yzhlinscau/AAFMM: Augment Applications in Fitting Mixed Models

Description

batchS This function carries out batch analysis for single trait for mixed models in R.

Usage

batchS(data,type,FMod,RMod=NULL,EMod=NULL,
                 geneticM=NULL,SpM=NULL,pformula=NULL)

Arguments

data	aim dataset
type	Index to specify which package for analysis.
FMod	Fixed mode,should be 'y~1+fixed.factors'.
RMod	Randomed mode, should be '~random.factors'.
EMod	Error mode for asreml, details see example.
geneticM	Randomed terms for tree model in breedR, details see example.
SpM	Spatial error terms in breedR, details see example.
pformula	formula for h2 (or corr).

Details

Mixed models batch analysis for single trait.

Value

the result is returned directly.

Author(s)

Yuanzhen Lin <[e-mail]>

References

AAFMM website:https://github.com/yzhlinscau/AAFMM

Examples

library(AAFMM)
library(tidyr)
library(plyr)
library(dplyr)

#### running examples for batchS()
## 00 data
data(butron.maize,package='agridat')
#str(butron.maize)


set.seed(2018)
butron.maize$x<-rnorm(245,mean=10,sd=4)
df<-tidyr::gather(butron.maize,key=Trait,y,c(-1:-4))
#str(df)


## 01 nlme package
library(nlme) # V3.1-131

Fixed.Mod1<- y ~ 1+env
Ran.Mod1<- ~1|male/female

nlme.rs<-plyr::ddply(df,'Trait', 
               function(dat) batchS(data=dat,type='nlme',
                                         FMod=Fixed.Mod1,
                                         RMod=Ran.Mod1))


## 02 lme4 package
library(lme4)  # V1.1-17

Fixed.Mod2<- y ~ 1+env+(1|male)+(1|female)

lme4.rs<-plyr::ddply(df,'Trait', 
               function(dat) batchS(data=dat,type='lme4',
                                         FMod=Fixed.Mod2))

## Not run: 
## 03 breedR package
library(breedR) # V0.12-1

Fixed.Mod3<- y ~ 1+env
Ran.Mod3<- ~ male+female

breedR.rs<-plyr::ddply(df,'Trait', 
                 function(dat) batchS(data=dat,type='breedR',
                                           FMod=Fixed.Mod3,
                                           RMod=Ran.Mod3))


## 04 asreml package
library(asreml) #V3.0

Fixed.Mod4<- y ~ 1+env
Ran.Mod4<- ~male+female

asreml.rs<-plyr::ddply(df,'Trait', 
                 function(dat) batchS(data=dat,type='asreml',
                                           FMod=Fixed.Mod4,
                                           RMod=Ran.Mod4))


#### special for breedR
library(breedR)

data(douglas)
S3<-subset(douglas,site=='s3')
#summary(S3);str(S3)

S3a<-dplyr::filter(S3,is.na(dad)) # hs
S3a<-transform(S3a,Mum=factor(mum))
S3a<-droplevels(S3a)
names(S3a)[7:8]<-c('x1','y1')

df<-tidyr::gather(S3a,key=Trait,y,c(-1:-8,-12,-14:-16))
#str(df)


# for parent model
fixed = y ~ 1+orig
random1=~Mum+block
pformula1=h2~4*V1/(V1+V3)

mm<-plyr::ddply(df,'Trait',
       function(dat) batchS(data=dat,type='breedR',
                             FMod=fixed,RMod=random1,
                             pformula=pformula1)
                             )
#result                             
mm                      

# for tree model
random2=~block
genetic=list(model = 'add_animal',
             pedigree = S3a[,1:3],
             id = 'self')
pformula2=h2~V2/(V2+V3)

mm1<-plyr::ddply(df,'Trait',
       function(dat) batchS(data=dat,type='breedR',
                             FMod=fixed,RMod=random2,
                             geneticM=genetic,
                             pformula=pformula2)
                             )

#result                             
mm1

## End(Not run)
 
 

yzhlinscau/AAFMM documentation built on May 4, 2019, 4:17 a.m.