HIest-package: Functions for estimating hybrid indices (ancestry and...

In HIest: Hybrid index estimation

Description

Uses likelihood to estimate ancestry, heterozygosity, and genomic cline parameters. Also evaluates simple hybrid classifications (parentals, F1, F2, backcrosses).

Details

Package:	HIest
Type:	Package
Version:	1.0
Date:	2012-02-13
License:	GPL 3.0
LazyLoad:	yes

Author(s)

Benjamin M. Fitzpatrick Maintainer: <[e-mail]>

References

Fitzpatrick, B. M. 2008. Hybrid dysfunction: Population genetic and quantitative genetic perspectives. American Naturalist 171:491-198.

Fitzpatrick, B. M. 2012. Estimating ancestry and heterozygosity of hybrids using molecular markers. BMC Evolutionary Biology 12:131. http://www.biomedcentral.com/1471-2148/12/131

Fitzpatrick, B. M. Alternative forms for genomic clines (in review)

Lynch, M. 1991. The genetic interpretation of inbreeding depression and outbreeding depression. Evolution 45:622-629.

Examples

	## Not run: 
data(Bluestone)

######################
# Fit genomic clines #
######################

data(Bluestone)
BS.fit <- Cline.fit(Bluestone[,1:12],model=c("logit.logistic","Barton"))
Cline.plot(BS.fit)

########################################
# Estimate ancestry and heterozygosity #
########################################

Bluestone <- replace(Bluestone,is.na(Bluestone),-9)

# parental allele frequencies (assumed diagnostic)
BS.P <- data.frame(Locus=names(Bluestone),Allele="BTS",P1=1,P2=0)

# estimate ancestry and heterozygosity
BS.est <-HIC(Bluestone)

# calculate likelihoods for early generation hybrid classes
BS.class <- HIclass(Bluestone,BS.P,type="allele.count")

# compare classification with maximum likelihood estimates
BS.test <- HItest(BS.class,BS.est,thresholds=c(2,2))

table(BS.test$c1)
# all 41 are TRUE, meaning the best classification is at least 2 log-likelihood units 
# better than the next best

table(BS.test$c2)
# 2 are TRUE, meaning the MLE S and H are within 2 log-likelihood units of the best
# classification, i.e., the simple classification is rejected in all but 2 cases

table(BS.test$Best.class,BS.test$c2)
# individuals were classified as F2-like (class 3) or backcross to CTS (class 4), but
# only two of the F2's were credible 

BS.test[BS.test$c2,]
# in only one case was the F2 classification a better fit (based on AIC) than the
# continuous model.

# equivalent to the AIC criterion:
BS.test <- HItest(BS.class,BS.est,thresholds=c(2,1))

#########################
# three-way hybrid zone #
#########################

# for example: make each parental, F1, F2, and backcross
G <- rbind(
rep(1,12),rep(1,12),
rep(2,12),rep(2,12),
rep(3,12),rep(3,12),
rep(1,12),rep(2,12),
rep(1:2,each=6),rep(1:2,6),
rep(1,12),rep(1:2,6),
rep(2,12),rep(1:2,6),
rep(1,12),rep(3,12),
rep(c(1,3),each=6),rep(c(1,3),6),
rep(1,12),rep(c(1,3),6),
rep(3,12),rep(c(1,3),6),
rep(2,12),rep(3,12),
rep(2:3,each=6),rep(2:3,6),
rep(3,12),rep(2:3,6),
rep(2,12),rep(2:3,6)
)

# 12 diagnostic markers
P <- data.frame(Locus=rep(1:12,each=3), allele=rep(1:3,12), P1=rep(c(1,0,0),12), 
P2=rep(c(0,1,0),12), P3=rep(c(0,0,1),12))

# find MLE with simulated annealing ... takes a few minutes with default iterations
# Est <- threeway(G,P,method="SANN",surf=FALSE)

# shortcut for diagnostic markers
Est <- HIC3(G,P)
CL <- thirdclass(G,P)

## End(Not run)

Example output

Loading required package: nnet

 2  NA's were omitted

 2  NA's were omitted

 1  NA's were omitted

 1  NA's were omitted

 1  NA's were omitted

 1  NA's were omitted

 1  NA's were omitted

 1  NA's were omitted
 * * * * * * * * *
 10 * * * * * * * * *
 20 * * * * * * * * *
 30 * * * * * * * * *
 40 *
TRUE 
  41 

FALSE  TRUE 
   39     2 
          
           FALSE TRUE
  class011     3    0
  class121    36    2
           S         H Best.class  LL.class LLD.class    LL.max       dAIC   c1
21 0.4112903 0.4677419   class121 -65.84898  2675.184 -63.85097  1.9960226 TRUE
27 0.4758065 0.4354839   class121 -67.23528  3900.188 -66.58898 -0.7073996 TRUE
     c2
21 TRUE
27 TRUE
 * * * * * * * * *
 10 * * * * *

HIest documentation built on May 2, 2019, 10:21 a.m.