Description Usage Arguments Details Value Note Author(s) See Also Examples
View source: R/create.gpData.r
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
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
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# 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)
|
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