- x
data.frame or matrix
- eps
threshold for convergency
- maxit
maximum number of iterations
- mixed
column index of the semi-continuous variables
- mixed.constant
vector with length equal to the number of
semi-continuous variables specifying the point of the semi-continuous
distribution with non-zero probability
- count
column index of count variables
- step
a stepwise model selection is applied when the parameter is set
to TRUE
- robust
if TRUE, robust regression methods will be applied
- takeAll
takes information of (initialised) missings in the response
as well for regression imputation.
- noise
irmi has the option to add a random error term to the imputed
values, this creates the possibility for multiple imputation. The error term
has mean 0 and variance corresponding to the variance of the regression
residuals.
- noise.factor
amount of noise.
- force
if TRUE, the algorithm tries to find a solution in any case,
possible by using different robust methods automatically.
- robMethod
regression method when the response is continuous.
- force.mixed
if TRUE, the algorithm tries to find a solution in any
case, possible by using different robust methods automatically.
- mi
number of multiple imputations.
- addMixedFactors
if TRUE add additional factor variable for each
mixed variable as X variable in the regression
- trace
Additional information about the iterations when trace equals
TRUE.
- init.method
Method for initialization of missing values (kNN or
median)
- modelFormulas
a named list with the name of variables for the rhs
of the formulas, which must contain a rhs formula for each variable with
missing values, it should look like `list(y1=c("x1","x2"),y2=c("x1","x3"))``
if factor variables for the mixed variables should be created for the
regression models
- multinom.method
Method for estimating the multinomial models
(current default and only available method is multinom)
- imp_var
TRUE/FALSE if a TRUE/FALSE variables for each imputed
variable should be created show the imputation status
- imp_suffix
suffix for the TRUE/FALSE variables showing the imputation
status