test.segRatio: Classic tests for assessing marker dosage in autopolyploids

In polySegratio: Simulate and test marker dosage for dominant markers in autopolyploids

Description

Perform chi–squared tests or binomial CIs to obtain expected marker dosage in autopolyploids

Usage

test.segRatio(seg.ratio, ploidy.level = 4,
  type.parents = c("heterogeneous", "homozygous"),
  method = c("chi.squared", "binomial"), alpha = 0.05, expected.ratio)

Arguments

seg.ratio	object of class segRatio containing segregation proportions
ploidy.level	the number of homologous chromosomes, either as numeric or as a character string
type.parents	"heterogeneous" if parental markers are 0,1 or "homozygous" if parental markers are both 1
method	specify which method ‘chi.squared’ or ‘binomial’
alpha	significance level for tests/CIs
expected.ratio	vector of expected segregation proportions Default: determined by using function expected.segRatio given the ploidy.level

Value

Returns object of class testSegRatio with components

probability	matrix of probabilities under the test for each dosage where columns are doses and rows are markers
dosage	vector of allocated dosages where allocation unique otherwise NA
allocated	matrix of 0's and 1's where 1 indicates dosage allocation where columns are doses and rows are markers
alpha	alpha level for significance test or CI construction
expected.ratios	expected segregation ratios under null hypotheses
call	call to test.segRatio

Author(s)

Peter Baker [e-mail]

References

• K Mather(1951) The measurement of linkage in heredity. Methuen London

• Ripol, M I et al(1999) Statistical aspects of genetic mapping in autopolyploids. Gene 235 31–41

See Also

segregationRatios for computing segregation ratios and segRatio, expected.segRatio

Examples

  ## simulated data
  a <- sim.autoMarkers(ploidy = 8, c(0.7,0.2,0.09,0.01))
  print(a)

  ## summarise chi-squared test vs true
  ac <- test.segRatio(a$seg.ratios, ploidy=8, method="chi.squared")
  print(addmargins(table(a$true.doses$dosage, ac$dosage, exclude=NULL)))

  ## summarise binomial CI vs true
  ab <- test.segRatio(a$seg.ratios, ploidy=8, method="bin")
  print(addmargins(table(a$true.doses$dosage, ab$dosage, exclude=NULL)))

polySegratio documentation built on May 2, 2019, 6:09 p.m.