missMethods (version 0.2.0)

impute_LS_array: 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 impute_LS_gene().

eps

Directly passed to impute_LS_gene().

min_common_obs

Directly passed to impute_LS_gene().

ds_impute_LS_gene

Result of imputing ds with ds_impute_LS_gene(), if this already exists (see details).

verbose_gene

Should impute_LS_gene() be verbose?

verbose_expected_values

Should impute_expected_values() be verbose?

Value

An object of the same class as ds with imputed missing values.

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().

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

# NOT RUN {
set.seed(123)
ds_mis <- delete_MCAR(mvtnorm::rmvnorm(100, rep(0, 10)), 0.1)
ds_imp <- impute_LS_array(ds_mis)
# }