A suplist object is a list of hyper2 objects. Each
element is a hyper2 object that is consistent with an
incomplete rank observation \(R\); the list elements are exclusive
and exhaustive for \(R\). If S is a suplist object,
and S=list(H1,H2,...,Hn) where the Hi are hyper2
objects, then
Prob(p|H_1)++Prob(p|H_n)Prob(p|H1)+...+Prob(p|Hn).
This is because the elements of a suplist object are disjoint
alternatives.
It is incorrect to say that a likelihood function
L_S(p)L_S(p) for \(p\) is the sum of separate
likelihood functions. This is incorrect because the arbitrary
multiplicative constant messes up the math, for example we might have
L_H_1(p)=C_1Prob(p|H_1)? and
L_H_2(p)=C_2Prob(p|H_2)? and indeed
L_H_1 H_2(p)=C_12(Prob(p|H_1)+Prob(p|H_2))? but
L_H_1(p)+L_H_2(p)
C_1Prob(p|H_1)+C_2Prob(p|H_2)see PDF
(the right hand side is meaningless).
Functions suplist_add() and sum.suplist() implement
“S1+S2” as the support function for independent
observations S1 and S2. The idea is that the support
functions “add” in the following sense. If
S1=list(H1,...,Hr) and S2=list(I1,...,Is) where
Hx,Ix are hyper2 objects, then the likelihood function
for “S1+S2” is the likelihood function for S1
followed by (independent) S2. Formally
Prob(p|S_1+S_2) =
(
Prob(p|H_1)
++
Prob(p|H_r)
)(
Prob(p|I_1)
++
Prob(p|I_s)
)omitted; see PDF
Prob(p|S_1+S_2) =
(
Prob(p|H_1)
++
Prob(p|H_r)
)+(
Prob(p|I_1)
++
Prob(p|I_s)
)omitted; see PDF
However, S1+S2 is typically a large and unwieldy object, and
can be very slow to evaluate. These functions are here because they
provide slick R idiom.