glm.synds, lm.synds  R Documentation 
Fits generalized linear models or simple linear models to the synthesised
data set(s) using glm
and lm
function respectively.
glm.synds(formula, family = "binomial", data, ...) lm.synds(formula, data, ...) ## S3 method for class 'fit.synds' print(x, msel = NULL, ...)
formula 
a symbolic description of the model to be estimated.
A typical model has the form 
family 
a description of the error distribution
and link function to be used in the model. See the documentation of

data 
an object of class 
... 
additional parameters passed to 
x 
an object of class 
msel 
index or indices of synthetic data copies for which coefficient
estimates are to be displayed. If 
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 

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 the dependent variable in the model was not synthesised. 
mcoef 
a matrix of coefficients estimates from all 
mvar 
a matrix of variance estimates from all 
glm
, lm
,
multinom.synds
, polr.synds
,
compare.fit.synds
, summary.fit.synds
### Logit model ods < SD2011[1:1000, c("sex", "age", "edu", "marital", "ls", "smoke")] s1 < syn(ods, m = 3) f1 < glm.synds(smoke ~ sex + age + edu + marital + ls, data = s1, family = "binomial") f1 print(f1, msel = 1:2) ### Linear model ods < SD2011[1:1000,c("sex", "age", "income", "marital", "depress")] ods$income[ods$income == 8] < NA s2 < syn(ods, m = 3) f2 < lm.synds(depress ~ sex + age + log(income) + marital, data = s2) f2 print(f2,1:3)
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