Multiple Imputation for Proteomics

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Documentation for package ‘mi4p’ version 1.2

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

check.conditions	Check if the design is valid
check.design	Check if the design is valid
datasim	A single simulated dataset
eBayes.mod	MI-aware Modifed eBayes Function
formatLimmaResult	Format a Result from Limma
hid.ebayes	MI-aware Modifed eBayes Function
limmaCompleteTest.mod	Computes a hierarchical differential analysis
make.contrast	Builds the contrast matrix
make.design	Builds the design matrix
make.design.1	Builds the design matrix for designs of level 1
make.design.2	Builds the design matrix for designs of level 2
make.design.3	Builds the design matrix for designs of level 3
meanImp_emmeans	Multiple Imputation Estimate
mi4limma	Differential analysis after multiple imputation
mm_peptides	mm_peptides - peptide-level intensities for mouse
multi.impute	Multiple imputation of quantitative proteomics datasets
MVgen	Amputation of a dataset
norm.200.m100.sd1.vs.m200.sd1.list	A list of simulated datasets.
proj_matrix	Variance-Covariance Matrix Projection
protdatasim	Data simulation function
qData	Extract of the abundances of Exp1_R25_pept dataset
rubin1.all	First Rubin rule (all peptides)
rubin1.one	First Rubin rule (a given peptide)
rubin2.all	Computes the 2nd Rubin's rule (all peptides)
rubin2bt.all	2nd Rubin's rule Between-Imputation component (all peptides)
rubin2bt.one	2nd Rubin's rule Between-Imputation Component (a given peptide)
rubin2wt.all	2nd Rubin's rule Within-Variance Component (all peptides)
rubin2wt.one	2nd Rubin's rule Within-Variance Component (a given peptide)
sTab	Experimental design for the Exp1_R25_pept dataset
test.design	Check if xxxxxx
within_variance_comp_emmeans	Multiple Imputation Within Variance Component