Nothing
`print.simAutoMarkers` <-
function(x, ..., row.index=c(1:min(10,nrow(x$markers))),
col.index=c(1:min(10,ncol(x$markers))) ){
## Description: print object of class simAutoMarkers
## Arguments:
## x: object of class simAutoMarkers
## row.index: which rows to print
## col.index: which columns to print
cat("Autopolyploid dominant markers generated at",x$time.generated,
"\nwith call:\n")
print(x$call)
cat("\nPloidy level is:",x$ploidy.level,"(", x$E.segRatio$ploidy.name,")\n")
cat("Parents were set as", x$type.parents, "for the markers\n")
cat("Theoretical segregation proportions:\n")
print(unlist(x$E.segRatio))
cat("\nProportions in each dosage class:\n")
print(x$dose.proportion)
cat("No. of markers generated from multinomial distribution:\n")
print(x$true.doses$table.doses)
cat("\nData were generated for",x$n.individuals,"individuals with",
x$n.markers,"markers\nA subset is:\n")
print(cbind(x$markers[row.index,col.index],
cbind(r=x$seg.ratio$r[row.index], n=x$seg.ratio$n[row.index],
ratio=x$seg.ratio$seg.ratio[row.index],
dose=x$true.doses$names[row.index])), quote=FALSE, ...)
## print(x$markers[row.index,col.index])
##cat("Segregation ratios:")
##print(x$seg.ratio, row.index)
## if missclassified
if (length(x$misclass.info$proportion) != 0) {
if (x$misclass$proportion != 0) {
cat("Marker data were misclassified for",100*x$misclass$proportion,
"% markers at random\n")
}
}
## if missing values
if (length(x$na.proportion) != 0) {
if (mode(x$na.proportion$na.proportion) == "numeric") {
if (x$na.proportion$na.proportion != 0) {
cat("Missing data generated for",100*x$na.proportion$na.proportion,
"% markers at random\n")
}
} else {
if (mode(x$na.proportion$na.proportion) == "list") {
cat("Missing values generated for:\n")
cat("Markers:",100*x$na.proportion$na.proportion$marker[1],
"% markers had",
100*x$na.proportion$na.proportion$marker[2],"missing at random\n")
cat("Individuals:",100*x$na.proportion$na.proportion$indiv[1],
"% individuals had",
100*x$na.proportion$na.proportion$indiv[2],"missing at random\n")
}
}
}
## if overdispersed beta-binomial used
if(x$overdispersion$overdispersion){
cat("Overdispersion parameters:\nShape1\n")
print(x$overdispersion$shape1)
cat("Shape2\n")
print(x$overdispersion$shape2)
}
}
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