- originaldata
The data in wide form (i.e. one row per subject)
- smformula
A formula of the form "Surv(t,d)~x1+x2+x3", where t is the discrete time variable, d is the binary event
indicator, and the covariates should not include time. The time variable should be
an integer coded numeric variable taking values from 1 up to the final time period.
- timeEffects
Specifies how the effect of time is modelled. timeEffects="factor"
(the default) models time as a
factor variable. timeEffects="linear"
and timeEffects="quad"
specify that time be modelled as a continuous
linear or quadratic effect on the log odds scale respectively.
- method
A required vector of strings specifying for each variable either
that it does not need to be imputed (""), the type of regression model to be
be used to impute. Possible values are "norm"
(normal linear regression),
"logreg"
(logistic regression), "brlogreg"
(bias reduced logistic regression),
"poisson"
(Poisson regression),
"podds"
(proportional odds regression for ordered categorical variables),
"mlogit"
(multinomial logistic regression for unordered categorical variables),
or a custom expression which defines a passively imputed variable, e.g.
"x^2"
or "x1*x2"
. "latnorm"
indicates the variable is a latent
normal variable which is measured with error. If this is specified for a variable,
the "errorProneMatrix"
argument should also be used.
- predictorMatrix
An optional predictor matrix. If specified, the matrix defines which
covariates will be used as predictors in the imputation models
(the outcome must not be included). The i'th row of the matrix should consist of
0s and 1s, with a 1 in the j'th column indicating the j'th variable be used
as a covariate when imputing the i'th variable. If not specified, when
imputing a given variable, the imputation model covariates are the other
covariates of the substantive model which are partially observed
(but which are not passively imputed) and any fully observed covariates (if present)
in the substantive model. Note that the outcome variable is implicitly conditioned
on by the rejection sampling scheme used by smcfcs, and should not be specified as a predictor
in the predictor matrix.
- m
The number of imputed datasets to generate. The default is 5.
- numit
The number of iterations to run when generating each imputation.
In a (limited) range of simulations good performance was obtained with the
default of 10 iterations. However, particularly when the proportion of missingness
is large, more iterations may be required for convergence to stationarity.
- rjlimit
Specifies the maximum number of attempts which should be made
when using rejection sampling to draw from imputation models. If the limit is reached
when running a warning will be issued. In this case it is probably advisable to
increase the rjlimit
until the warning does not appear.
- noisy
logical value (default FALSE) indicating whether output should be noisy, which can
be useful for debugging or checking that models being used are as desired.
- errorProneMatrix
An optional matrix which if specified indicates that some variables
are measured with classical measurement error. If the i'th variable is measured with error
by variables j and k, then the (i,j) and (i,k) entries of this matrix should be 1, with the
remainder of entries 0. The i'th element of the method argument should then be specified
as "latnorm"
. See the measurement error vignette for more details.