Perform LSimpute_array as described by Bo et al. (2004)
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
)An object of the same class as ds with imputed missing values.
A data frame or matrix with missing values.
Directly passed to impute_LS_gene().
Directly passed to impute_LS_gene().
Directly passed to impute_LS_gene().
Result of imputing ds with ds_impute_LS_gene(),
if this already exists (see details).
Should impute_LS_gene() be verbose?
Should impute_expected_values() be verbose?
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().
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
Other LSimpute functions:
impute_LS_adaptive(),
impute_LS_combined(),
impute_LS_gene()
set.seed(123)
ds_mis <- delete_MCAR(mvtnorm::rmvnorm(100, rep(0, 10)), 0.1)
ds_imp <- impute_LS_array(ds_mis)
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