addMisclass function

Misclassifies marker data in objects of class autoMarker or autoCross

Marker data are misclassified at a specified rate for objects of class simAutoMarkers or simAutoCross. The rate may be specified either as a proportion of missing at random or a proportion of columns and rows with specified proportions of missings.

addMisclass(x, misclass = 0, bands.missed=0, parents = FALSE,
parent.cols = c(1, 2), seed)

Arguments

• x: object of class simAutoMarkers or simAutoCross, or a matrix with dominant markers scored as 0 or 1
• misclass: proportion misclassified specified as for na.proportion (Default: 0)
• bands.missed: proportion of bands that are not scored when they are actually present. Note this is applied to correctly specified markers after markers are misclassified (Default: 0)
• parents: if TRUE then misclassify parental alleles, otherwise misclassify offspring marker alleles
• parent.cols: for object of simAutoClass the columns containg parental markers
• seed: random number generator (RNG) state for random number which will be set at start to reproduce results

Returns

returns object of class simAutoMarkers or simAutoCross, or a matrix with dominant markers scored as 0 or 1 with extra components - misclass.info: list with components

 * `proportion`numeric proportion misclassified
 * `index`indicates which markers were set as misclassified
 * `bands.proportion`numeric proportion marker bands missed
 * `bands.index`indicates which markers bands were missed
 * `call`matches arguments when function called
 * `time.generated`time/date when misclassifieds added
 * `seed`seed for random number generation

Author(s)

Peter Baker p.baker1@uq.edu.au

See Also

addMissing add missing markers at random, sim.autoMarkers simulate autopolyploid markers, sim.autoCross simulate autopolyploid markers for a cross

Examples

## simulate autopolyploid markers
p1 <- sim.autoCross(4, dose.proportion=c(0.7,0.3), n.markers=20, n.indiv=10)
p2 <- sim.autoCross(4, dose.proportion=list(p01=c(0.7,0.3),p10=c(0.7,0.3),p11=c(
0.6,0.2,0.2)))

## add misclassified for a whopping 20% of markers
print(addMisclass(p1, 0.2, parents=TRUE), row=1:20)
addMisclass(p2, 0.1)