Description Usage Arguments Details Value Author(s) References See Also Examples
HItest3
compares the best fit of fifteen early generation diploid hybrid genotypes (parental, F1, F2, backcross between all three pairs of parental lineages) to the maximum likelihood genotype proportions estimated by threeway
or HIC3
.
1 |
class |
Object containing output from |
MLE |
Object containing output from |
thresholds |
Vector of length 2, containing criteria for classification. The first criterion
( |
The AIC for the continuous model accounts for k = 5 estimated paramaters. For the classification model, on a per individual basis, there is only one estimated parameter. Classification could be expanded to include more complex genotypic combinations, but it is not clear how (or whether) to account for additional complexity in model comparison. It is probably advisable to consider testing only classifications reflecting a clear biological question, such as whether F1 hybrids are completely sterile.
A data matrix with 7 named columns and one row per individual.
Best.class |
Most likely classification of the individual given the data. Classes are indicated by their expected genomic proportions in order: |
LL.class |
The log-likelihood of the best classification given the data. This will be |
LLD.class |
The difference in log-likelihood between the best and second best classification. |
LL.max |
The log-liklihood of the MLE genomic proportions, not constrained to fit a class (continuous model). |
dAIC |
Difference in AIC between the MLE and best classification. This will be negative when the AIC for classification (1 parameter) is lower than the AIC for the continuous model MLE (5 parameters). |
c1 |
Logical: |
c2 |
Logical: |
Ben Fitzpatrick
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
threeway
finds maximum likelihood estimates for non-diagnostic marker data. thirdclass
evaluates simple classification of three-way hybrids into parental, F1, F2, and backcross categories. For conventional two-way hybrid zone analyses, see HIC, HIest, HIclass, HItest
.
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 33 34 35 36 37 38 39 40 41 42 43 | ## Not run:
## all possible 2-way crosses after 2 generations
G <- rbind(
rep(1,12),rep(1,12), # parental 1
rep(2,12),rep(2,12), # parental 2
rep(3,12),rep(3,12), # parental 3
rep(1,12),rep(2,12), # 1 x 2 F1
rep(1:2,each=6),rep(1:2,6), # 1 x 2 F2
rep(1,12),rep(1:2,6), # 1 x 1 x 2 BC
rep(2,12),rep(1:2,6), # 1 x 2 x 2 BC
rep(1,12),rep(3,12), # 1 x 3 F1
rep(c(1,3),each=6),rep(c(1,3),6), # 1 x 3 F2
rep(1,12),rep(c(1,3),6), # 1 x 1 x 3 BC
rep(3,12),rep(c(1,3),6), # 1 x 3 x 3 BC
rep(2,12),rep(3,12), # 2 x 3 F1
rep(2:3,each=6),rep(2:3,6), # 2 x 3 F2
rep(3,12),rep(2:3,6), # 2 x 3 x 3 BC
rep(2,12),rep(2:3,6) # 2 x 2 x 3 BC
)
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))
Est <- HIC3(G,P)
Class <- thirdclass(G,P)
HItest3(Class,Est)
## now for some three-way mixes
G3 <- matrix(1+rbinom(200,2,.5),ncol=10)
Est3 <- HIC3(G3,P)
Class3 <- thirdclass(G3,P)
HItest3(Class3,Est3) # usually all classifications will be impossible because all
# individuals will have nonzero contributions from each of the
# three parentals
## bias toward parental 1
G3 <- matrix(1+rbinom(200,2,.25),ncol=10)
Est3 <- HIC3(G3,P)
Class3 <- thirdclass(G3,P)
HItest3(Class3,Est3) # now you might have a few that look like F2's
# between 1 and 2 (c110200)
## End(Not run)
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