Rounding continuous covariates creates "micropoststrata" 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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