AB.Ab.aB.ab.RdEstimates the parameter of the AB-Ab-aB-ab blood group system.
AB.Ab.aB.ab(link = "logitlink", init.p = NULL)Link function applied to p.
See Links for more choices.
Optional initial value for p.
This one parameter model involves a probability called p.
An object of class "vglmff" (see vglmff-class).
The object is used by modelling functions such as vglm
and vgam.
Lange, K. (2002). Mathematical and Statistical Methods for Genetic Analysis, 2nd ed. New York: Springer-Verlag.
The input can be a 4-column matrix of counts, where the columns
are AB, Ab, aB and ab
(in order).
Alternatively, the input can be a 4-column matrix of
proportions (so each row adds to 1) and the weights
argument is used to specify the total number of counts for each row.
ymat <- cbind(AB=1997, Ab=906, aB=904, ab=32) # Data from Fisher (1925)
fit <- vglm(ymat ~ 1, AB.Ab.aB.ab(link = "identitylink"), trace = TRUE)
#> Iteration 1: deviance = 2.018914
#> Iteration 2: deviance = 2.018722
#> Iteration 3: deviance = 2.018721
#> Iteration 4: deviance = 2.018721
fit <- vglm(ymat ~ 1, AB.Ab.aB.ab, trace = TRUE)
#> Iteration 1: deviance = 2.01883
#> Iteration 2: deviance = 2.018722
#> Iteration 3: deviance = 2.018721
#> Iteration 4: deviance = 2.018721
rbind(ymat, sum(ymat)*fitted(fit))
#> AB Ab aB ab
#> 1997.000 906.0000 904.0000 32.00000
#> 1 1953.775 925.4751 925.4751 34.27487
Coef(fit) # Estimated p
#> p
#> 0.1889769
p <- sqrt(4*(fitted(fit)[, 4]))
p*p
#> [1] 0.03571229
summary(fit)
#>
#> Call:
#> vglm(formula = ymat ~ 1, family = AB.Ab.aB.ab, trace = TRUE)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -1.45667 0.05039 -28.91 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Name of linear predictor: logitlink(p)
#>
#> Residual deviance: 2.0187 on 0 degrees of freedom
#>
#> Log-likelihood: -11.983 on 0 degrees of freedom
#>
#> Number of Fisher scoring iterations: 4
#>
#> Warning: Hauck-Donner effect detected in the following estimate(s):
#> '(Intercept)'
#>