ComputeA {mipfp}

Computes the marginal matrix A and margins vector m of an estimation problem

Description

Given a set of marginal target constraints and the dimension of the array X to wich the targets relate to, this function computes the matrix A of full rank and vector m such that

A^T \pi = (m, 1)^T

where vector m contains all components but one of every target and \pi is a vector of the (unknow) components of X.

Usage

ComputeA(dim.arr, target.list, target.data)

Arguments

dim.arr

The dimension of the array X to which the margins are applied.

target.list

A list of the target margins provided in target.data. Each component of the list is an array whose cells indicates which dimension the corresponding margin relates to.

target.data

A list containing the data of the target margins. Each component of the list is an array storing a margin. The list order must follow the one defined in target.list. Note that the cells of the arrays must be non-negative.

Value

A list whose elements are defined below.

marginal.matrix

The marginal matrix.

margins

A vector containing the margins associated with A.

df

The degree of freedom of the problem.

Author(s)

Johan Barthelemy

Maintainer: Johan Barthelemy <[e-mail]>.

See Also

MarginalMatrix.

Examples

# loading the data
data(spnamur, package = "mipfp")
# subsetting the data frame, keeping only the first 3 variables
spnamur.sub <- subset(spnamur, select = Household.type:Prof.status)
# true table
true.table <- table(spnamur.sub)
# extracting the margins
tgt.v1        <- apply(true.table, 1, sum)
tgt.v1.v2     <- apply(true.table, c(1,2), sum)
tgt.v2.v3     <- apply(true.table, c(2,3), sum)
tgt.list.dims <- list(1, c(1,2), c(2,3))
tgt.data      <- list(tgt.v1, tgt.v1.v2, tgt.v2.v3)
# creating the seed, a 10 pct sample of spnamur
seed.df <- spnamur.sub[sample(nrow(spnamur), round(0.10*nrow(spnamur))), ]
seed.table <- table(seed.df)
# computing the associated marginal matrix and margins vector
res.marg <- ComputeA(dim(seed.table), tgt.list.dims, tgt.data)
print(res.marg)