Various utilities to coerce and manipulate MB objects
MB(dep, m, pnames=character(0))
# S3 method for MB
as.array(x, ...)
# S4 method for MB
getM(x)
# S3 method for gunter_MB
print(x, ...)
Primary argument to MB(). Typically a matrix with each row
being an observation (see ‘details’ section below for an
example). If an object of class Oarray, function MB()
coerces to an MB object
Vector containing the relative sizes of the various marginal binomial distributions
Object of class MB to be converted to an Oarray object
Further arguments to as.array(), currently ignored
In function MB(), a character vector of
names for the entries
Robin K. S. Hankin
Function MB() returns an object of class MB. This is
essentially a matrix with one row corresponding to a single
observation; repeated rows indicate identical observations as shown
below. Observational data is typically in this form. The idea is
that the user can coerce to a gunter_MB object, which is then
analyzable by Lindsey().
The multivariate multiplicative binomial distribution is defined by _i=1^t m_i x_i\, z_ip_i^x_iq_i^z_i_i^x_iz_i _i < j_ij^x_ix_j Equation 20 of the vignette
Thus if ==1theta=phi=1 the system reduces to a product of independent binomial distributions with probability p_i and size m_i for i=1,...,t1,...,t.
There follows a short R transcript showing the MB class in use,
with annotation.
The first step is to define an m vector:
R> m <- c(2,3,1)
This means that m_1=2,m_2=3,m_3=1m1=2,m2=3,m3=1. So m_1=2m1=2 means that i=1 corresponds to a binomial distribution with size 2 [that is, the observation is in the set {0,1,2}0,1,2]; and m_2=3m2=3 means that i=2 corresponds to a binomial with size 3 [ie the set {0,1,2,3}0,1,2,3].
Now we need some observations:
R> a <- matrix(c(1,0,0, 1,0,0, 1,1,1, 2,3,1, 2,0,1),5,3,byrow=T)
R> a
[,1] [,2] [,3]
[1,] 1 0 0
[2,] 1 0 0
[3,] 1 1 1
[4,] 2 3 1
[5,] 2 0 1
In matrix a, the first observation, viz c(1,0,0) is
interpreted as x_1=1,x_2=0,x_3=0x1=1,x2=0,x3=0. Thus, because
x_i+z_i=m_ixi+zi=mi, we have z_1=1,z_2=3,z_3=1z1=1,z2=3,z3=1. Now
we can create an object of class MB, using function MB():
R> mx <- MB(a, m, letters[1:3])
The third argument gives names to the observations corresponding to the
columns of a. The values of m_1, m_2, m_3m1,m2,m3 may
be extracted using getM():
R> getM(mx)
a b c
2 3 1
R>
The getM() function returns a named vector, with names
given as the third argument to MB().
Now we illustrate the print method:
R> mx
a na b nb c nc
[1,] 1 1 0 3 0 1
[2,] 1 1 0 3 0 1
[3,] 1 1 1 2 1 0
[4,] 2 0 3 0 1 0
[5,] 2 0 0 3 1 0
R>
See how the columns are in pairs: the first pair total 2 (because m_1=2m1=2), the second pair total 3 (because m_2=3m2=3), and the third pair total 1 (because m_3=1m3=1). Each pair of columns has only a single degree of freedom, because m_imi is known.
Also observe how the column names are in pairs. The print method puts
these in place. Take the first two columns. These are named
‘a’ and ‘na’: this is intented to mean
‘a’ and ‘not a’.
We can now coerce to a gunter_MB:
R> (gx <- gunter(mx))
$tbl
a b c
1 0 0 0
2 1 0 0
3 2 0 0
[snip]
24 2 3 1$d
[1] 0 2 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1
$m
a b c
2 3 1
Take the second line of the element tbl of gx, as an
example. This reads c(1,0,0) corresponding to the observations
of a,b,c respectively, and the second line of element d
[“d” for “data”], viz 2, shows that this
observation occurred twice (and in fact these were the first two lines
of a).
Now we can coerce object mx to an array:
R> (ax <- as.array(mx))
, , c = 0 b
a 0 1 2 3
0 0 0 0 0
1 0 0 2 0
2 0 0 0 0
, , c = 1
b
a 0 1 2 3
0 0 1 0 0
1 0 0 0 0
2 1 1 0 0
>
(actually, ax is an Oarray object). The location of an
element in ax corresponds to an observation of abc, and
the entry corresponds to the number of times that observation was made.
For example, ax[1,2,0]=2 shows that c(1,2,0) occurred
twice (the first two lines of a).
The Lindsey Poisson device is applicable: see help(danaher) for
an application to the bivariate case and help(Lindsey) for an
example where a table is created from scratch.
MM, Lindsey, danaher
a <- matrix(c(1,0,0, 1,0,0, 1,1,1, 2,3,1, 2,0,1),5,3,byrow=TRUE)
m <- c(2,3,1)
mx <- MB(a, m, letters[1:3]) # mx is of class 'MB'; column headings
# mean "a" and "not a".
ax <- as.array(mx)
gx <- gunter(ax)
ax2 <- as.array(gx)
data(danaher)
summary(Lindsey_MB(danaher))
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