| SeqimputeEM {mvdalab} | R Documentation |
Sequential Expectation Maximization (EM) for imputation of missing values.
Description
Missing values are sequentially updated via an EM algorithm.
Usage
SeqimputeEM(data, max.ncomps = 5, max.ssq = 0.99, Init = "mean",
adjmean = FALSE, max.iters = 200,
tol = .Machine$double.eps^0.25)
Arguments
data |
a dataset with missing values. |
max.ncomps |
integer corresponding to the maximum number of components to test |
max.ssq |
maximal SSQ for final number of components. This will be improved by automation. |
Init |
For continous variables impute either the mean or median. |
adjmean |
Adjust (recalculate) mean after each iteration. |
max.iters |
maximum number of iterations for the algorithm. |
tol |
the threshold for assessing convergence. |
Details
A completed data frame is returned that mirrors the model matrix. NAs are replaced with convergence values as obtained via Seqential EM algorithm. If object contains no NAs, it is returned unaltered.
Value
Imputed.DataFrames |
A list of imputed data frames across |
ncomps |
number of components to test |
Author(s)
Thanh Tran (thanh.tran@mvdalab.com), Nelson Lee Afanador (nelson.afanador@mvdalab.com)
References
NOTE: Publication Pending
Examples
dat <- introNAs(iris, percent = 25)
SeqimputeEM(dat)