Imputation for Proteomics

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Documentation for package ‘imp4p’ version 1.2

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imp4p-package Introduction to the IMP4P package
estim.bound Estimation of lower and upper bounds for missing values.
estim.mix Estimation of a mixture model of MCAR and MNAR values in each column of a data matrix.
fast_apply_nb_na Function similar to the function 'apply(X,dim,function(x)sum(is.na(x)))'.
fast_apply_nb_not_na Function similar to the function 'apply(X,dim,function(x)sum(!is.na(x)))'.
fast_apply_sd_na_rm_T Function similar to the function 'apply(X,dim,sd,na.rm=TRUE)'.
fast_apply_sum_na_rm_T Function similar to the function 'apply(X,dim,sum,na.rm=TRUE)'.
fast_sim Function to compute similarity measures between a vector and each row of a matrix.
gen.cond Function allowing to create a vector indicating the membership of each sample to a condition.
imp4p Introduction to the IMP4P package
impute.igcda Imputing missing values by assuming that the distribution of complete values is Gaussian in each column of an input matrix. This algorithm is named "Imputation under a Gaussian Complete Data Assumption" (IGCDA).
impute.mi Imputation of data sets containing peptide intensities with a multiple imputation strategy.
impute.mix Imputation using a decision rule under an assumption of a mixture of MCAR and MNAR values.
impute.mle Imputing missing values using a maximum likelihood estimation (MLE).
impute.pa Imputation of peptides having no value in a biological condition (present in a condition / absent in another).
impute.PCA Imputing missing values using Principal Components Analysis.
impute.rand Imputation of peptides with a random value.
impute.RF Imputing missing values using Random Forest.
impute.slsa Imputing missing values using an adaptation of the LSimpute algorithm (Bo et al. (2004)) to experimental designs. This algorithm is named "Structured Least Squares Algorithm" (SLSA).
mi.mix Multiple imputation from a matrix of probabilities of being MCAR for each missing value.
miss.mcar.process Estimating the MCAR mechanism in a sample.
miss.total.process Estimating the missing data mechanism in a sample.
pi.mcar.karpievitch Estimating the proportion of MCAR values in biological conditions using the method of Karpievitch (2009).
pi.mcar.logit Estimating the proportion of MCAR values in a sample using a logit model.
pi.mcar.probit Estimating the proportion of MCAR values in a sample using a probit model.
prob.mcar Estimation of a vector of probabilities that missing values are MCAR.
prob.mcar.tab Estimation of a matrix of probabilities that missing values are MCAR.
sim.data Simulation of data sets by controlling the proportion of MCAR values and the distribution of MNAR values.
translatedRandomBeta Function to generated values following a translated Beta distribution