Package index • mi4p

All functions

MVgen(): Amputation of a dataset

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.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

make.design(): Builds the design matrix

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

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