Description Usage Arguments Details Value Author(s) References See Also Examples

Returns marginal matrix; i.e., matrix required to obtained marginal frequencies

1 | ```
MarginalMatrix(var, marg, dim, SubsetCoding="Identity",SelectCells="All")
``` |

`var` |
character or numeric vector containing variables |

`marg` |
list of character or numeric indicating marginals |

`dim` |
numeric vector indicating the dimension of |

`SubsetCoding` |
allows a (character) type or a matrix to be assigned to variables for each element of |

`SelectCells` |
Useful option for empirical likelihood. Default "All" includes all columns, if a list of cells in the table is given only the corresponding columns of MarginalMatrix are included. |

Gives the matrix which, multiplied by a probability vector, gives the marginal probabilities.
The probability vector is assumed to be a vectorized form of the probabilities in a table, such that the last variable changes value fastest,
then the before last variable, etc. For example, the cells of a *2 x 3* table are arranged in vector form as (11,12,13,21,22,23).
To achieve this, the appropriate way to vectorize a data frame `dat`

is using `c(t(ftable(dat)))`

.

Special case of transposed `DesignMatrix`

:

1 2 | ```
MarginalMatrix <- function(var,marg,dim,SubsetCoding="Identity")
t(DesignMatrix(var,marg,dim,SubsetCoding=SubsetCoding,MakeSubsets=FALSE))
``` |

Allows weighted sums of probabilities using `SubsetCoding`

matrix

W. P. Bergsma w.p.bergsma@lse.ac.uk

Bergsma, W. P. (1997).
*Marginal models for categorical data*.
Tilburg, The Netherlands: Tilburg University Press.
http://stats.lse.ac.uk/bergsma/pdf/bergsma_phdthesis.pdf

Bergsma, W. P., Croon, M. A., & Hagenaars, J. A. P. (2009). Marginal models for dependent, clustered, and longitudunal categorical data. Berlin: Springer.

`ConstraintMatrix`

, `DesignMatrix`

, `DirectSum`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
# Computing marginal frequencies
n <- c(1:6) #example list of frequencies
var <- c("A","B")
marg <- list(c("A"),c("B"))
dim <- c(2,3)
at <- MarginalMatrix(var,marg,dim)
# list of marginal frequencies:
at
# identitymatrix: several ways of specifying:
marg <- c("A","B")
MarginalMatrix(var, marg,dim)
MarginalMatrix(var, marg, dim,
SubsetCoding = list(c("A", "B"), list("Identity", "Identity")))
MarginalMatrix(var, marg, dim,
SubsetCoding = list(c("A","B"), list(rbind(c(1,0),c(0,1)), rbind(c(1,0,0),c(0,1,0),c(0,0,1)))))
# omit second category of first variable
at <- MarginalMatrix(var, marg, dim,
SubsetCoding = list(c("A","B"), list(rbind(c(1,0)),"Identity")))
at
``` |

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