Fits ordered logistic models to the synthesised data set(s)
using the polr function.
polr.synds(formula, data, ...)To print the results the print function (print.fit.synds) can
be used. The summary function (summary.fit.synds)
can be used to obtain the combined results of models fitted to each of the
m synthetic data sets.
An object of class fit.synds. It is a list with the following
components:
the original call to polr.synds.
combined (average) coefficient estimates.
combined (average) variance estimates of mcoef.
an object summarising the fit to each synthetic data set
or a list of m such objects. Note that this is different from
the object created by summary.polr to make it compatible with other
fitting methods for the compare.fit.synds and summary.fit.synds
functions. In particular the coefficients combine the items coefficients
and zeta from summary.polr to make a combined vector of coefficients.
function used to fit the model.
a number of cases in the original data.
a number of cases in the synthesised data.
a logical value indicating whether synthetic data were generated using proper synthesis.
the number of synthetic versions of the observed data.
a vector of synthesising methods applied to each variable in the saved synthesised data.
a logical value indicating whether the dependent variable in the model was not synthesised.
a matrix of coefficients estimates from all m syntheses.
a matrix of variance estimates from all m syntheses.
a symbolic description of the model to be estimated. A typical
model has the form response ~ predictors. See the documentation of
polr and formula for details.
an object of class synds, which stands for 'synthesised
data set'. It is typically created by function syn and it
includes data$m synthesised data set(s).
additional parameters passed to polr.
polr, glm.synds,
multinom.synds, print.fit.synds,
summary.fit.synds, compare.fit.synds
ods <- SD2011[1:1000, c("sex", "age", "edu", "marital", "ls", "smoke")]
s1 <- syn(ods, m = 3)
f1 <- polr.synds(edu ~ sex + age, data = s1)
summary(f1)
print(f1, msel = 1:2)
compare(f1, SD2011)
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