model.Selector
(Package: imputeLCMD) :
Model selector for hybrid missing data imputation
The function sets a flag "1" for lines (proteins/peptides) where the imputation of missing values should be performed using a MAR/MCAR specific method, and "0" if the imputation should be performed using a MNAR specific method.
● Data Source:
CranContrib
● Keywords: ~kwd1, ~kwd2
● Alias: model.Selector
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This is a wrapper function that performs SVD-based imputation of missing data. The wrapper is built around the pca function from the pcaMethods package.
● Data Source:
CranContrib
● Keywords: ~kwd1, ~kwd2
● Alias: impute.wrapper.SVD
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impute.MAR
(Package: imputeLCMD) :
Generic function for the imputation of MAR/MCAR missing data
Performs the imputation of missing data under the randomness assumption (either MAR or MCAR). The function allows treating the missing values using one of the following MAR/MCAR specific imputation methods: MLE, SVD, KNN.
● Data Source:
CranContrib
● Keywords: ~kwd1, ~kwd2
● Alias: impute.MAR
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impute.MinProb
(Package: imputeLCMD) :
Imputation of left-censored missing data using stochastic minimal value approach.
Performs the imputation of left-censored missing data by random draws from a Gaussian distribution centered in a minimal value. Considering a peptide/protein expression data matrix with n columns corresponding to biological samples and p lines corresponding to peptides/proteins, for each sample (column), the mean value of the Gaussian distribution is set to a minimal value observed in that sample. The minimal value observed is estimated as being the q-th quantile (e.g. q = 0.01) of the observed values in that sample. The standard deviation is estimated as the median of the peptide/protein-wise standard deviations. Note that when estimating the standard deviation of the Gaussian distribution, only the peptides/proteins which present more than 50% recorded values are considered.
● Data Source:
CranContrib
● Keywords: ~kwd1, ~kwd2
● Alias: impute.MinProb
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impute.QRILC
(Package: imputeLCMD) :
Imputation of left-censored missing data using QRILC method.
This function implements QRILC, a missing data imputation method that performs the imputation of left-censored missing data using random draws from a truncated distribution with parameters estimated using quantile regression.
● Data Source:
CranContrib
● Keywords: ~kwd1, ~kwd2
● Alias: impute.QRILC
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Generates peptide to protein map. For a given peptide expression matrix with nPep peptides, this functions creates a random map to be used for the aggregation of the peptides into nProt proteins.
● Data Source:
CranContrib
● Keywords: ~kwd1, ~kwd2
● Alias: generate.RollUpMap
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impute.ZERO
(Package: imputeLCMD) :
Imputation of missing entries by code{0
This function performs the trivial imputation of missing values by 0. Is is only used for comparison purposes.
● Data Source:
CranContrib
● Keywords: ~kwd1, ~kwd2
● Alias: impute.ZERO
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Is is a wrapper function that performs the imputation of missing data using EM algorithm. The wrapper is built around the imp.norm function from the norm package.
● Data Source:
CranContrib
● Keywords: ~kwd1, ~kwd2
● Alias: impute.wrapper.MLE
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insertMVs
(Package: imputeLCMD) :
Generates missing data in a complete data matrix.
This function generates missing data in a complete data matrix. Both random and left-censored missing data can be generated. The percentage of all missing data is controlled by mean.THR. The percentage of missing data which are left-censored is controlled by MNAR.rate.
● Data Source:
CranContrib
● Keywords: ~kwd1, ~kwd2
● Alias: insertMVs
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Performs the imputation of missing values assuming that the missing values are both MAR and MNAR. It is assumed that the MNAR missing values are left-censored, more precisely, only rows (proteins/peptides) with a mean value below a censoring threshold are considered to contain left-censored missing data. The method relies on a estimation of the left-censoring threshold that is further used to distinguish rows (proteins/peptides) that contain left-censored missing data from those lines who contain random missing data.
● Data Source:
CranContrib
● Keywords: ~kwd1, ~kwd2
● Alias: impute.MAR.MNAR
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