Description Usage Arguments Details Value Note References See Also Examples

Markov Chain Monte Carlo method for generating posterior draws of the
parameters of the unrestricted general location model, given a matrix
of incomplete mixed data. After a suitable number of steps are taken,
the resulting value of the parameter may be regarded as a random draw
from its observed-data posterior distribution. May be used together
with `imp.mix`

to create multiple imputations
of the missing data.

1 2 | ```
dabipf.mix(s, margins, design, start, steps=1, prior=0.5,
showits=FALSE)
``` |

`s` |
summary list of an incomplete data matrix created by the
function |

`margins` |
vector describing the sufficient configurations or margins in the
desired loglinear model. The variables are ordered in the original
order of the columns of |

`design` |
design matrix specifying the relationship of the continuous
variables to the categorical ones. The dimension is |

`start` |
starting value of the parameter. This is a parameter list
such as one created by this function or by |

`steps` |
number of steps of data augmentation-Bayesian IPF to be taken. |

`prior` |
Optional vector or array of hyperparameter(s) for a Dirichlet prior
distribution. The default is the Jeffreys prior (all hyperparameters
= .5). If structural zeros appear in the table, prior counts for these
cells should be set to |

`showits` |
if |

The prior distribution used by this function is a combination of a constrained Dirichlet prior for the cell probabilities, an improper uniform prior for the regression coefficients, and the improper Jeffreys prior for the covariance matrix. The posterior distribution is not guaranteed to exist, especially in sparse-data situations. If this seems to be a problem, then better results may be obtained by imposing restrictions further restrictions on the parameters.

a new parameter list. The parameter can be put into a more
understandable format by the function `getparam.mix`

.

The random number generator seed must be set at least once by the
function `rngseed`

before this function can be used.

The starting value should satisfy the restrictions of the model and
should lie in the interior of the parameter space. A suitable starting
value can be obtained by running `ecm.mix`

,
possibly with the prior
hyperparameters set to some value greater than 1, to ensure that the
mode lies in the interior.

Schafer, J. L. (1996) *Analysis of Incomplete Multivariate Data.*
Chapman \& Hall, Chapter 9.

`prelim.mix`

, `getparam.mix`

,
`ecm.mix`

, `rngseed`

, `imp.mix`

.

1 2 3 4 5 6 7 8 | ```
data(stlouis)
s <- prelim.mix(stlouis,3) # do preliminary manipulations
margins <- c(1,2,3) # saturated contingency table model
design <- diag(rep(1,12)) # identity matrix D=no of cells
thetahat <- ecm.mix(s,margins,design) # find ML estimate
rngseed(1234567) # random generator seed
newtheta <- dabipf.mix(s,margins,design,thetahat,steps=200)
ximp <- imp.mix(s,newtheta,stlouis) # impute under newtheta
``` |

```
Steps of ECM:
1...2...3...4...5...6...7...8...9...10...11...12...13...14...15...16...17...18...19...20...21...22...23...24...25...26...27...28...29...30...31...32...33...34...35...36...37...38...39...40...41...42...43...44...45...46...47...48...49...50...51...52...53...54...55...56...57...58...59...60...61...62...63...64...65...66...67...68...69...70...71...72...73...74...75...76...77...78...79...80...81...82...83...84...85...86...87...88...89...90...91...92...93...94...95...96...97...98...99...100...101...102...103...104...105...106...107...108...109...110...111...112...113...114...115...116...117...118...119...120...121...122...123...124...125...126...127...128...129...130...131...132...133...134...135...136...137...138...139...140...141...142...143...144...145...146...147...148...149...150...151...152...153...154...155...156...157...158...159...160...161...162...163...164...165...166...167...168...169...170...171...172...173...174...175...176...177...178...179...180...181...
```

mix documentation built on June 20, 2017, 9:13 a.m.

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