prunePed | R Documentation |
This function removes individuals who are either not themselves or not ancestors to phenotyped individuals
prunePed(pedigree, phenotyped, ...)
## Default S3 method:
prunePed(pedigree, phenotyped, ...)
## S3 method for class 'numPed'
prunePed(pedigree, phenotyped, ...)
pedigree |
An object, where the first 3 columns correspond to: ID, Dam, & Sire. See details. |
phenotyped |
A vector indicating which individuals in the pedigree have phenotypic information available. |
... |
Arguments to be passed to methods |
Often mixed effect models run much faster when extraneous information is removed before running the model. This is particularly so when reducing the number of random effects associated with a relationship matrix constructed from a pedigree.
NOTE: more columns than just a pedigree can be passed in the pedigree
argument.
Missing parents (e.g., base population) should be denoted by either 'NA', '0', or '*'.
This function is very similar to (and the code is heavily borrowed from) a
function of the same name in the MCMCglmm
package by Jarrod Hadfield.
The pedigree object (can have more columns than just ID, Dam, and Sire), where the ID column contains an ID for all individuals who are actually phenotyped or are an ancestor to an individual with a phenotype (and are thus informative for estimating parameters in the base population).
prepPed
# Make a pedigree (with sex) from the warcolak dataset
warcolak_ped <- warcolak[, 1:4]
# Reduce the number of individuals that have a phenotype for "trait1" in
#the warcolak dataset
t1phenotyped <- warcolak
t1phenotyped[sample(seq.int(nrow(warcolak)), 1500, replace = FALSE), "trait1"] <- NA
t1phenotyped <- t1phenotyped[which(!is.na(t1phenotyped$trait1)), ]
# The following will give a pedigree with only individuals that have a
# phenotype for "trait1" OR are an ancestor to a phenotyped individual.
pruned_warcolak_ped <- prunePed(warcolak_ped, phenotyped = t1phenotyped$ID)
# Now compare the sizes (note, pruned_warcolak_ped retained its column indicating sex.
dim(warcolak_ped)
dim(pruned_warcolak_ped)
# We could have kept all of the data associated with individuals who had phenotypic
# information on "trait1" by instead specifying
pruned_fullt1_warcolak_ped <- prunePed(warcolak, phenotyped = t1phenotyped$ID)
dim(pruned_fullt1_warcolak_ped) #<-- compare number of columns with above
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