miss_total_process: Estimating the missing data mechanism in a sample. In imp4p: Imputation for Proteomics

Description

This function allows estimating the missing data mechanism, i.e. the probability to be missing in function of the intensity level, from an estimation of a mixture model of MNAR and MCAR values (see `estim.mix` function).

Usage

 `1` ```miss.total.process(abs,pi_na,F_na,F_tot) ```

Arguments

 `abs` The interval on which is estimated the missing data mechanism. `pi_na` The proportion of missing values. `F_na` An estimation of the cumulative distribution function of the missing values on the interval `abs`. `F_tot` An estimation of the cumulative distribution function of the complete values on the interval `abs`.

Value

A list composed of:

 `abs` The interval on which is estimated the missing data mechanism. `p` The estimated probability to be missing in function of the intensity level.

Author(s)

Quentin Giai Gianetto <[email protected]>

`estim.mix`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```#Simulating data res.sim=sim.data(nb.pept=2000,nb.miss=600,pi.mcar=0.2,para=0.5,nb.cond=2,nb.repbio=3, nb.sample=5,m.c=25,sd.c=2,sd.rb=0.5,sd.r=0.2); #Imputation of missing values with the slsa algorithm dat.slsa=impute.slsa(tab=res.sim\$dat.obs,conditions=res.sim\$condition,repbio=res.sim\$repbio); #Estimation of the mixture model res=estim.mix(tab=res.sim\$dat.obs, tab.imp=dat.slsa, conditions=res.sim\$condition); #Estimating the missing mechanism in the first replicate mtp=miss.total.process(res\$abs.mod,res\$pi.na[1],res\$F.na[,1],res\$F.tot[,1]) plot(mtp\$abs,mtp\$p,ty="l",xlab="Intensity values",ylab="Estimated probability to be missing") ```