# prob_mcar: Estimation of a vector of probabilities that missing values... In imp4p: Imputation for Proteomics

## Description

This function returns a vector of probabilities that each missing value is MCAR from specified confidence intervals.

## Usage

 `1` ```prob.mcar(b.l,b.u,absc,pi.mcar,F.tot,F.na) ```

## Arguments

 `b.l` A numeric vector of lower bounds for missing values. `b.u` A numeric vector of upper bounds for missing values. `absc` The interval on which is estimated the MCAR data mechanism. `pi.mcar` The estimated proportion of MCAR values. `F.tot` An estimation of the cumulative distribution function of the complete values on the interval `absc`. `F.na` An estimation of the cumulative distribution function of the missing values on the interval `absc`.

## Value

A numeric vector of estimated probabilities to be MCAR for missing values in the confidence intervals defined by `b.l` and `b.u`. The input arguments `absc`, `pi.mcar`, `F.tot` and `F.na` can be estimated thanks to the function `estim.mix`.

## Author(s)

Quentin Giai Gianetto <[email protected]>

`estim.mix`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```#Simulating data #Simulating data res.sim=sim.data(nb.pept=2000,nb.miss=600,pi.mcar=0.2,para=10,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); #Computing probabilities to be MCAR born=estim.bound(tab=res.sim\$dat.obs,conditions=res.sim\$condition); #Computing probabilities to be MCAR in the first column of result\$tab.mod proba=prob.mcar(b.l=born\$tab.lower[,1],b.u=born\$tab.upper[,1],absc=res\$abs.mod, pi.mcar=res\$pi.mcar[1], F.tot=res\$F.tot[,1], F.na=res\$F.na[,1]); ```