# MarginalMatrix: MarginalMatrix In cmm: Categorical Marginal Models

## Description

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

## Usage

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

## Arguments

 `var` character or numeric vector containing variables `marg` list of character or numeric indicating marginals `dim` numeric vector indicating the dimension of `var` `SubsetCoding` allows a (character) type or a matrix to be assigned to variables for each element of `suffconfigs`, see examples and `DesignMatrix` `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.

## Details

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

## Author(s)

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

## References

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.

## See Also

`ConstraintMatrix`, `DesignMatrix`, `DirectSum`

## Examples

 ``` 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 ```

cmm documentation built on May 2, 2019, 3:36 a.m.