Description Usage Arguments Details Value Author(s) References See Also Examples
HItest
compares the best fit of six early generation diploid hybrid genotypes (parental, F1, F2, backcross) to the maximum likelihood genotype decribed by ancestry (S) and interclass heterozygosity (H).
1 |
class |
Output from |
MLE |
Output from |
thresholds |
Criteria for classification. The first criterion ( |
As a quick-and-dirty rule of thumb, one might accept a putative classification as credible if the log-likelihood of the best-fit class was over 2 units greater than the log-likelihood of the second best-fit class AND within 2 units of the maximum log-likelihood. The first criterion is based on the approximate equivalence of a 2 x log-likelihood interval to a 95 percent confidence interval for some distributions (Hudson 1971; Hillborn and Mangel 1997). The second is based on the conventional penalty of two log-likelihood units for an additional estimated parameter in model selection (Edwards 1972; Burnaham and Anderson 2004). The classification model can be viewed as having one free parameter (once the best-fit class is set to "chosen", the other five are constrained to "not chosen"), while the continuous model has two (S and H). This approach has the disadvantage of effectively treating the classification as a null model, which is not biologically justified.
A better approach might be to accept the classification only if its AIC is lower than the AIC of the MLE, i.e., if dAIC
is negative (Fitzpatrick 2012). Note that dAIC
cannot be less than -2 (the case where the MLE is identical to the expectation for a class).
A data frame with one row per individual. Columns are:
S |
The maximim likelihood estimate of the ancestry index from |
H |
The maximum likelihood estimate of the interclass heterozygosity from |
Best.class |
The class with the highest likelihood of the six from |
LL.class |
The log-likelihood of the data for the best-fit class from |
LLD.class |
The difference in log-likelihood between the best and second-best fit class from |
LL.max |
The maximum log-likelihood from |
dAIC |
The difference in AIC between the continuous model MLE (2 estimated parameters) and the best-fit class (1 estimated parameter). |
c1 |
Logical: |
c2 |
Logical: |
Ben Fitzpatrick
Burnham, K. P., and D. R. Anderson. 2004. Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods and Research 33:261-304.
Edwards, A. W. F. 1972. Likelihood. Cambridge University Press, Cambridge.
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
Hilborn, R., and M. Mangel. 1997. The ecological detective: Confronting models with data. Princeton University Press, New Jersey.
Hudson, D. J. 1971. Interval estimation from the likelihood function. Journal of the Royal Statistical Society, Series B 33: 256-262.
Lynch, M. 1991. The genetic interpretation of inbreeding depression and outbreeding depression. Evolution 45:622-629.
HIest
for maximum likelihood estimation of S and H, HIsurf
for a likelihood surface, HIclass
for likelihoods of early generation hybrid classes, HILL
for the basic likelihood function.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ## Not run:
data(Bluestone)
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)
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.
## End(Not run)
|
Loading required package: nnet
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40 *
TRUE
41
FALSE TRUE
40 1
FALSE TRUE
class011 3 0
class121 37 1
S H Best.class LL.class LLD.class LL.max dAIC c1
27 0.4758065 0.4354839 class121 -67.23528 3900.188 -66.58898 -0.7073996 TRUE
c2
27 TRUE
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