synbreed (version 0.12-6)

gpData2data.frame:

Description

Create a data.frame out of phenotypic and genotypic data in object of class gpData by merging datasets using the common id. The shared data set could either include individuals with phenotypes and genotypes (default) or additional unphenotyped or ungenotyped individuals. In the latter cases, the missing observations are filled by NA's.

Usage

gpData2data.frame(gpData,trait=1,onlyPheno=FALSE,all.pheno=FALSE,
                  all.geno=FALSE,repl=NULL,phenoCovars=TRUE,...)

Arguments

gpData

object of class gpData

trait

numeric or character. A vector with the names or numbers of the trait that should be extracted from pheno. Default is 1.

onlyPheno

scalar logical. Only return phenotypic data.

all.pheno

scalar logical. Include all individuals with phenotypes in the data.frame and fill the genotypic data with NA.

all.geno

scalar logical. Include all individuals with genotypes in the data.frame and fill the phenotypic data with NA.

repl

character or numeric. A vector which contains names or numbers of replication that should be drawn from the phenotypic values and covariates. Default is NULL, i.e. all values are used.

phenoCovars

logical. If TRUE, columns with the phenotypic covariables are attached from element phenoCovars to the data.frame. Only required for repeated measurements.

...

further arguments to be used in function reshape. The argument times could be useful to rename the levels of the grouping variable (such as locations or environments).

Value

A data.frame with the individuals names in the first column, the phenotypes in the next column(s) and the marker genotypes in subsequent columns.

Details

Argument all.geno can be used to predict the genetic value of individuals without phenotypic records using the BGLR package. Here, the genetic value of individuals with NA as phenotype is predicted by the marker profile. For multiple measures, phenotypic data in object gpData is arranged with replicates in an array. With gpData2data.frame this could be reshaped to "long" format with multiple observations in one column. In this case, one column for the phenotype and 2 additional columns for the id and the levels of the grouping variable (such as replications, years of locations in multi-environment trials) are added.

See Also

create.gpData, reshape

Examples

# example data with unrepeated observations
set.seed(311)

# simulating genotypic and phenotypic data
pheno <- data.frame(Yield = rnorm(12,100,5),Height=rnorm(12,100,1))
rownames(pheno) <- letters[4:15]
geno <- matrix(sample(c("A","A/B","B",NA),size=120,replace=TRUE,
prob=c(0.6,0.2,0.1,0.1)),nrow=10)
rownames(geno) <-  letters[1:10]
colnames(geno) <- paste("M",1:12,sep="")
# different subset of individuals in pheno and geno

# create 'gpData' object
gp <- create.gpData(pheno=pheno,geno=geno)
summary(gp)
gp$covar

# as data.frame with individuals with genotypes and phenotypes
gpData2data.frame(gp,trait=1:2)
# as data.frame with all individuals with phenotypes
gpData2data.frame(gp,1:2,all.pheno=TRUE)
# as data.frame with all individuals with genotypes
gpData2data.frame(gp,1:2,all.geno=TRUE)

# example with repeated observations
set.seed(311)

# simulating genotypic and phenotypic data
pheno <- data.frame(ID = letters[1:10], Trait = c(rnorm(10,1,2),rnorm(10,2,0.2),
                    rbeta(10,2,4)), repl = rep(1:3, each=10))
geno <- matrix(rep(c(1,0,2),10),nrow=10)
colnames(geno) <- c("M1","M2","M3")
rownames(geno) <-  letters[1:10]

# create 'gpData' object
gp <- create.gpData(pheno=pheno,geno=geno, repeated="repl")

# reshape of phenotypic data and merge of genotypic data,
# levels of grouping variable loc are named "a", "b" and "c"
gpData2data.frame(gp,onlyPheno=FALSE,times=letters[1:3])