Setup: Creates linear inverse matrices
In LIM: Linear Inverse Model examples and solution methods

Description Usage Arguments Value Author(s) See Also Examples

Creates the linear problem with equality and inequality equations.

Takes as input either a liminput list, as generated by Read or a filename with the linear inverse model specifications. Creates:

inverse matrices and vectors A, b, G, h of the equalities/inequalities:

Ax=b

Gx>=h
if present, also generates the cost/profit function which is used as:

\min(cost)

or

\max(profit)
if the input was a flow network, Setup will also create the flow matrix (see details).

Setup(...)
## S3 method for class 'limfile'
Setup(file, verbose = TRUE, ...)
## S3 method for class 'character'
Setup(...)
## S3 method for class 'liminput'
Setup(liminput,...)

`file`	name of the inverse input file.
`verbose`	if `TRUE`: prints warnings and messages to the screen.
`liminput`	list of elements, as returned by `Read`.
`...`	extra parameters allowing this to be a generic function.

a list containing:

`file`	name of the inverse input file.
`NUnknowns`	number of unknowns.
`NEquations`	number of equations.
`NConstraints`	number of constraints.
`NComponents`	number of components.
`NExternal`	number of externals.
`NVariables`	number of variables.
`A`	matrix A of equalities Ax=B.
`B`	vector B of equalities Ax=B.
`G`	matrix G of inequalities Gx>h.
`H`	vector H of inequalities Gx=h.
`Cost`	cost vector (to minimise), the weight of each unknown; if not specified; 1 for all unknowns.
`Profit`	profit vector (to maximise).
`Flowmatrix`	matrix where element ij denotes flow from compartment i to j.
`VarA`	matrix VarA of variable equation VarA*x=VarB.
`VarB`	vector VarB of variable equation VarA*x=VarB.
`Flows`	a vector with flow names.
`Parameters`	a data.frame with parameter names and values.
`Components`	a data.frame with state names and values.
`Externals`	a data.frame with external names and values.
`rates`	a data.frame with rate names and values.
`markers`	a data.frame with marker names and values.
`Variables`	a vector with variable names.
`Unknowns`	a vector with names of unknowns (either states or flows).
`Weight`	a vector with the weights of unknowns- default is 1.