Summarise Sequoia Output or Pedigree
Number of assigned parents and grandparents and sibship sizes, split by genotyped, dummy, and 'observed'.
SummarySeq( SeqList = NULL, Pedigree = NULL, DumPrefix = c("F0", "M0"), SNPd = NULL, Plot = TRUE, Panels = "all" )
SeqList |
the list returned by |
Pedigree |
dataframe, pedigree with the first three columns being id - dam - sire. Column names are ignored, as are additional columns. |
DumPrefix |
character vector of length 2 with prefixes for dummy dams
(mothers) and sires (fathers). Will be read from |
SNPd |
character vector with ids of SNP genotyped individuals. Only when
|
Plot |
show barplots and histograms of the results, as well as of the parental LLRs, Mendelian errors, and agepriors, if present. |
Panels |
character vector with panel(s) to plot. Choose from 'all', 'G.parents' (parents of genotyped individuals), 'D.parents' (parents of dummy individuals), 'sibships' (distribution of sibship sizes), 'LLR' (log10-likelihood ratio parent/otherwise related), 'OH' (count of opposite homozygote SNPs). |
A list with the following elements:
PedSummary |
a 2-column matrix with basic summary statistics, similar
to what used to be returned by Pedantics' |
ParentCount |
a 2x3x2x4 array with the number of assigned parents, split by D1: genotyped vs dummy individuals; D2: female, male and unknown-sex individuals; D3: dams vs sires; D4: genotyped, dummy, observed vs no parent |
GPCount |
a 2x4x4 array with the number of assigned grandparents, split by D1: genotyped vs dummy individuals; D2 Maternal grandmother (MGM), maternal grandfather (MGF), paternal grandmother (PGM), paternal grandfather (PGF); D3: genotyped, dummy, observed vs no grandparent |
SibSize |
a list with as first element a table of maternal sibship sizes, and as second element a table of paternal sibship sizes. Each table is a matrix with a number of rows equal to the maximum sibship size, and 3 columns, splitting by the type of parent: genotyped, dummy, or observed. |
sequoia
for pedigree reconstruction and links to other
functions.
data(SimGeno_example, LH_HSg5, package="sequoia") SeqOUT <- sequoia(GenoM = SimGeno_example, LifeHistData = LH_HSg5, Module="ped") Ped_example <- SeqOUT[["Pedigree"]] Ped_example$dam[1:20] <- paste0("Mum", 1:20) # some fake field mums PedSum1 <- SummarySeq(SeqOUT, Pedigree=Ped_example, Panels="G.parents") summary(PedSum1) PedSum1$PedSummary
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