wmerge {wgaim} | R Documentation |
Merge phenotypic data with genotypic data from an object
of class "interval
"
wmerge(geno, pheno, by = NULL, ...)
geno |
genotypic data from an object of class
"interval " (see read.interval |
pheno |
phenotypic data which may be give as a data.frame
or a file |
by |
character string defining the name of the column(s) with which to merge the phenotypic data with the genotypic data |
... |
arguments passed to read.table if pheno is
a filename |
The ...
argument actually passes extra arguments to
asreml.read.table
which in turn passes identical arguments to
read.table
. Therefore if header
or col.names
is
set, names of columns with a capital letter are converted to a factor
(see asreml.read.table
.)
This function provides a fail safe mechanism with which to merge
large scale genotypic data with phenotypic data. It is important that
both data sets contain a matching column named with the argument
"by
".
As the phenotypic data is inherently larger in size than the genotypic
data the merging process has been simplified to ensure that the correct
combined data set is returned. Any names of the genotypic "by
"
column that do not appear in the names of the phenotypic "by
" column
are dropped from the genotypic data as there is no phenotypic
information for these uniquely named rows. Any names of the phenotypic "by
"
column that do not appear in the names of the genotypic "by
"
column induce a row of NA's to be placed in the genotype data. This is
to ensure that the phenotypic data (which may include columns containing
important design information) remains intact.
a list contaning identical components as returned by
read.interval
or possibly read.cross
plus the
additional following components
pheno.dat |
A copy of the phenotypic data used in the merge |
full.data |
A copy of the full data, i.e the genotypic data merged
with the phenotypic data using the matching column named by the
argument "by " |
Julian Taylor, Simon Diffey, Ari Verbyla and Brian Cullis
# read in data data(zinc, package = "wgaim") data(raccas, package = "wgaim") # subset linkage map and merge genotypic with phenotypic raccasS <- subset(raccas, chr = c("1A1", "2D1", "4D2", "6A1")) raccasM <- wmerge(raccasS, zinc, by = "id")