Fitting ordered logistic models to synthetic data
Fits ordered logistic models to the synthesised data set(s)
using the polr function.
polr.synds(formula, data, ...)
formula |
a symbolic description of the model to be estimated. A typical
model has the form |
data |
an object of class |
... |
additional parameters passed to |
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:
call |
the original call to |
mcoefavg |
combined (average) coefficient estimates. |
mvaravg |
combined (average) variance estimates of |
analyses |
an object summarising the fit to each synthetic data set
or a list of |
fitting.function |
function used to fit the model. |
n |
a number of cases in the original data. |
k |
a number of cases in the synthesised data. |
proper |
a logical value indicating whether synthetic data were generated using proper synthesis. |
m |
the number of synthetic versions of the observed data. |
method |
a vector of synthesising methods applied to each variable in the saved synthesised data. |
incomplete |
a logical value indicating whether any of the variables in the model were not synthesised. |
mcoef |
a matrix of coefficients estimates from all |
mvar |
a matrix of variance estimates from all |
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, ods)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.