create.gpData {synbreed} | R Documentation |
This function combines all raw data sources in a single, unified data object
of class gpData
. This is a list
with elements for phenotypic,
genotypic, marker map, pedigree and further covariate data. All elements are
optional.
create.gpData( pheno = NULL, geno = NULL, map = NULL, pedigree = NULL, family = NULL, covar = NULL, reorderMap = TRUE, map.unit = "cM", repeated = NULL, modCovar = NULL, na.string = "NA", cores = 1 )
pheno |
|
geno |
|
map |
|
pedigree |
Object of class |
family |
|
covar |
|
reorderMap |
|
map.unit |
|
repeated |
This column is used to identify the replications of the
phenotypic values. The unique values become the names of the third dimension
of the pheno object in the |
modCovar |
|
na.string |
|
cores |
|
The class gpData
is designed to provide a unified framework for data
related to genomic prediction analysis. Every data source can be omitted. In
this case, the corresponding argument must be NULL
. By default
(argument reorderMap
), markers in geno
are ordered by their
position in map
. Individuals are ordered in alphabetical order.
An object of class gpData
can contain different subsets of
individuals or markers in the elements pheno
, geno
and
pedigree
. In this case the id
in covar
comprises all
individuals that either appear in pheno
, geno
and
pedigree
. Two additional columns in covar
named
phenotyped
and genotyped
are automatically generated to
identify individuals that appear in the corresponding gpData
object.
Object of class gpData
which is a list
with the
following elements
covar |
|
pheno |
|
geno |
|
pedigree |
object of class |
map |
|
phenoCovars |
|
info |
|
In case of missing row names or column names in one item, information is substituted from other elements (assuming the same order of individuals/markers) and a warning specifying the assumptions is returned. Please check them carefully.
Valentin Wimmer and Hans-Juergen Auinger with contributions be Peter VandeHaar
codeGeno
, summary.gpData
,
gpData2data.frame
set.seed(123) # 9 plants with 2 traits n <- 9 # only for n > 6 pheno <- data.frame(Yield = rnorm(n, 200, 5), Height = rnorm(n, 100, 1)) rownames(pheno) <- letters[1:n] # marker matrix geno <- matrix(sample(c("AA", "AB", "BB", NA), size = n * 12, replace = TRUE, prob = c(0.6, 0.2, 0.1, 0.1) ), nrow = n) rownames(geno) <- letters[n:1] colnames(geno) <- paste("M", 1:12, sep = "") # genetic map # one SNP is not mapped (M5) and will therefore be removed map <- data.frame(chr = rep(1:3, each = 4), pos = rep(1:12)) map <- map[-5, ] rownames(map) <- paste("M", c(1:4, 6:12), sep = "") # simulate pedigree ped <- simul.pedigree(3, c(3, 3, n - 6)) # combine in one object gp <- create.gpData(pheno, geno, map, ped) summary(gp) # 9 plants with 2 traits , 3 replications n <- 9 # pheno <- data.frame( ID = rep(letters[1:n], 3), rep = rep(1:3, each = n), Yield = rnorm(3 * n, 200, 5), Height = rnorm(3 * n, 100, 1) ) # combine in one object gp2 <- create.gpData(pheno, geno, map, repeated = "rep") summary(gp2)