impute_LS_array {missMethods} | R Documentation |
LSimpute_array
Description
Perform LSimpute_array as described by Bo et al. (2004)
Usage
impute_LS_array(
ds,
k = 10,
eps = 1e-06,
min_common_obs = 5,
ds_impute_LS_gene = NULL,
verbose_gene = FALSE,
verbose_expected_values = FALSE
)
Arguments
ds |
A data frame or matrix with missing values. |
k |
Directly passed to |
eps |
Directly passed to |
min_common_obs |
Directly passed to |
ds_impute_LS_gene |
Result of imputing |
verbose_gene |
Should |
verbose_expected_values |
Should |
Details
This function performs LSimpute_array as described by Bo et al. (2004).The function assumes that the genes are the rows of ds
.
The mean vector and covariance matrix for the imputation in LSimpute_array is
based on a imputed dataset from LSimpute_gene. This dataset can be supplied
directly via ds_impute_LS_gene
or will automatically be created with
impute_LS_gene()
(if ds_impute_LS_gene
is NULL
). The imputation values
are the expected values given the estimated parameters and the observed
values. They are calculated via impute_expected_values()
. The amount of
feedback from these two functions is controlled via verbose_gene
and
verbose_expected_values
. The values of these two arguments are passed on to
the argument verbose
from impute_LS_gene()
and
impute_expected_values()
.
Value
An object of the same class as ds
with imputed missing values.
References
Bo, T. H., Dysvik, B., & Jonassen, I. (2004). LSimpute: accurate estimation of missing values in microarray data with least squares methods. Nucleic acids research, 32(3), e34
See Also
Other LSimpute functions:
impute_LS_adaptive()
,
impute_LS_combined()
,
impute_LS_gene()
Examples
set.seed(123)
ds_mis <- delete_MCAR(mvtnorm::rmvnorm(100, rep(0, 10)), 0.1)
ds_imp <- impute_LS_array(ds_mis)