missMethods (version 0.2.0)

impute_LS_combined: LSimpute_combined

Description

Perform LSimpute_combined as described by Bo et al. (2004)

Usage

impute_LS_combined(
  ds,
  k = 10,
  eps = 1e-06,
  min_common_obs = 5,
  p_mis_sim = 0.05,
  verbose_gene = FALSE,
  verbose_array = FALSE,
  verbose_gene_p = FALSE,
  verbose_array_p = 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().

p_mis_sim

Percentage of observed values that are set NA to estimate the mixing coefficient p. The default value (0.05) corresponds to the choice of Bo et al. (2004).

verbose_gene

Should impute_LS_gene() be verbose?

verbose_array

Should impute_LS_array() be verbose?

verbose_gene_p

Should impute_LS_gene() be verbose while estimating p?

verbose_array_p

Should impute_LS_array() be verbose while estimating p?

Value

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

Details

This function performs LSimpute_combined as described by Bo et al. (2004).The function assumes that the genes are the rows of ds.

LSimpute_combined combines imputation values from impute_LS_gene() and impute_LS_array() using a global approach for the mixing coefficient p. The amount of feedback given from these underlying functions is controlled via verbose_gene, verbose_array, verbose_gene_p, verbose_array_p. The last two control the amount of feedback while estimating p and the first two the amount of feedback during the estimation of the values that are mixed with p. Internally, the imputed dataset from impute_LS_gene() is passed on to impute_LS_array(). Therefore, all messages from impute_LS_gene() are truly from impute_LS_gene() and not a part of impute_LS_array(), which never calls impute_LS_gene() in this case. Furthermore, all messages from impute_expected_values() belong to impute_LS_array().

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_array(), 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_combined(ds_mis)
# }