impSeq {rrcovNA} | R Documentation |
Sequential imputation of missing values
Description
Impute missing multivariate data using sequential algorithm
Usage
impSeq(x, norm_impute=FALSE, check_data=FALSE, verbose=TRUE)
Arguments
x |
the original incomplete data matrix. |
norm_impute |
If there are not enough complete observations and |
check_data |
whether to check the variables: only numeric, non discrete,
with less than 50% NAs and with non-zero MAD. The default is |
verbose |
whether to write messages about the checking of the data. By default
|
Details
SEQimpute
starts from a complete subset of the data set Xc
and estimates
sequentially the missing values in an incomplete observation,
say x*, by minimizing the determinant of the covariance of the augmented
data matrix X* = [Xc; x']. Then the observation x* is added to the complete data matrix
and the algorithm continues with the next observation with missing values.
Value
A list containing the following elements:
x |
a matrix of the same form as |
colInAnalysis |
the column indices of the columns used in the analysis. |
namesNotNumeric |
the names of the variables which are not numeric. |
namesNAcol |
names of the columns left out due to too many NA's. |
namesDiscrete |
names of the discrete variables. |
namesZeroScale |
names of the variables with zero scale. |
References
S. Verboven, K. Vanden Branden and P. Goos (2007). Sequential imputation for missing values. Computational Biology and Chemistry, 31, 320–327.
Examples
data(bush10)
impSeq(bush10) # impute squentially missing data