Synthesis of a group of categorical variables by iterative proportional fitting
A fit to the table is obtained from the log-linear fit that matches the numbers in the margins specified by the margin parameters.
syn.ipf(x, k, proper = FALSE, priorn = 1, structzero = NULL,
gmargins = "twoway", othmargins = NULL,
maxtable = 1e8, print.its = FALSE, ...)x |
a data frame of the set of original data to be synthesised. |
k |
a number of rows in each synthetic data set - defaults to |
proper |
if |
priorn |
the sum of the parameters of the Dirichlet prior which can be thought of as a pseudo-count giving the number of observations that inform prior knowledge about the parameters. |
structzero |
a named list of lists that defines which cells in the table
are structural zeros and will remain as zeros in the synthetic data, by
leaving their prior as zeros. Each element of the |
gmargins |
a single character to define a group of margins. At present there is "oneway" and "twoway" option that creates, respectively, all 1-way and 2-way margins from the table. |
othmargins |
a list of margins that will be fitted. If |
maxtable |
the number of cells in the cross-tabulation of all the variables that will trigger a severe warning. |
print.its |
if true the iterations from |
... |
additional parameters. |
When used in syn function the group of variables with
method = "ipf" must all be together at the start of the visit sequence.
This function is designed for categorical variables, but it can also be used for
numerical variables if they are categorised by specifying them in the
numtocat parameter of the main function syn. Subsequent variables
in visit.sequence are then synthesised conditional on the synthesised
values of the grouped variables. A fit to the table is obtained from the
log-linear fit that matches the numbers in the margins specified by the margin
parameters. Prior probabilities for the proportions in each cell of the table
are given by a Dirichlet distribution with the same parameter for every cell
in the table that is not a structural zero. The sum of these parameters is
priorn. The default priorn = 1 can be thought of as equivalent
to the knowledge that 1 observation would be equally likely to
fall in any cell of the table. The synthetic data are generated from a multinomial
distribution with parameters given by the expected posterior probabilities for
each cell of the table. If the maximum likelihood estimate from the log-linear
fit to cell c_i is p_i and the table has N cells that are not
structural zeros then the expectation of the posterior probability
for this cell is (p_i + priorn/N^2) / (1 + priorn / N^2) or
equivalently (N * p_i + priorn/N) / (N + priorn / N).
Unlike syn.satcat, which fits saturated models from their conditional
distrinutions, x can include any combination of variables, including
those not present in the original data, except those defined by structzero.
NOTE that when the function is called by setting elements of
method in syn to "ipf", the parameters priorn,
structzero, gmargins, othmargins, maxtable
and print.its must be supplied to syn as e.g. ipf.priorn.
A list with two components:
res |
a data frame with |
fit |
a list made up of two lists: the margins fitted and the original data for each margin. |
ods <- SD2011[, c(1, 4, 5, 6, 2, 10, 11)]
table(ods[, c("placesize", "region")])
# Each \code{placesize_region} sublist:
# for each relevant level of \code{placesize} defined in the first element,
# the second element defines regions (variable \code{region}) that do not
# have places of that size.
struct.zero <- list(
placesize_region = list("URBAN 500,000 AND OVER", c(2, 4, 5, 8:13, 16)),
placesize_region = list("URBAN 200,000-500,000", c(3, 4, 10:11, 13)),
placesize_region = list("URBAN 20,000-100,000", c(1, 3, 5, 6, 8, 9, 14:15)))
synipf <- syn(ods, method = c(rep("ipf", 4), "ctree", "normrank", "ctree"),
ipf.gmargins = "twoway", ipf.othmargins = list(c(1, 2, 3)),
ipf.priorn = 2, ipf.structzero = struct.zero)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.