Rounding continuous covariates creates "micro-post-strata" and therefore
tends to reduce the number of distinct covariate vectors. After rounding,
the data is collapsed so that there is exactly one row for each distinct
covariate vector, and a column called `mct`

(for multinomial cell
count) is appended with that contains the number of records corresponding to
each row.

1 | ```
micro.post.stratify(dat, round.vars = NULL, rounding.scale = NULL)
``` |

`dat` |
The data in a matrix form |

`round.vars` |
A vector of names of variables to be rounded for the purpose of collapsing the data. |

`rounding.scale` |
A vector of scalars that determines how much each
corresponding variable in |

Continuous variables will be divided by `rounding.scale`

, then rounded
to the nearest whole number, and then multiplied by `rounding.scale`

.
The net effect is to round to the nearest multiple of `rounding.scale`

Another matrix, just like the input `dat`

except that there are
fewer rows, data values are rounded, and there is a new column `mct`

, which gives the number of data points corresponding to each row.

Zach Kurtz

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