irmi {VIM} | R Documentation |
In each step of the iteration, one variable is used as a response variable and the remaining variables serve as the regressors.
irmi(x, eps = 5, maxit = 100, mixed = NULL, count = NULL, step = FALSE, robust = FALSE, takeAll = TRUE, noise = TRUE, noise.factor = 1, force = FALSE, robMethod = "MM", force.mixed = TRUE, mi = 1, addMixedFactors = FALSE, trace = FALSE,init.method="kNN")
x |
data.frame or matrix |
eps |
threshold for convergency |
maxit |
maximum number of iterations |
mixed |
column index of the semi-continuous variables |
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. |
addMixedFactors
|
if factor variables for the mixed variables should be created for the regression models |
mi |
number of multiple imputations. |
trace |
Additional information about the iterations when trace equals TRUE. |
init.method |
Method for initialization of missing values (kNN or median) |
The method works sequentially and iterative. The method can deal with a mixture of continuous, semi-continuous, ordinal and nominal variables including outliers.
A full description of the method will be uploaded soon in form of a package vignette.
the imputed data set.
Matthias Templ, Alexander Kowarik
M. Templ, A. Kowarik, P. Filzmoser (2011) Iterative stepwise regression imputation using standard and robust methods. Journal of Computational Statistics and Data Analysis, Vol. 55, pp. 2793-2806.
data(sleep) irmi(sleep)