R: Estimating the MNAR mechanism in a sample.

miss.mnar.process {imp4p} | R Documentation

## Estimating the MNAR mechanism in a sample.

### Description

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

### Usage

    miss.mnar.process(abs,pi_mcar,F_mnar,F_na)

### Arguments

`abs` | The interval on which is estimated the MNAR data mechanism.
`pi_mcar` | An estimation of the proportion of MCAR values.
`F_mnar` | An estimation of the cumulative distribution function of the MNAR values on the interval `abs`.
`F_na` | An estimation of the cumulative distribution function of the missing values on the interval `abs`.

### Value

A list composed of:

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

### Author(s)

Quentin Giai Gianetto <quentin2g@yahoo.fr>

### See Also

`estim.mix`

### Examples

    #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 MNAR mechanism in the first replicate
    mnp=miss.mnar.process(res$abs.mod,res$pi.mcar[1],res$F.mnar[,1],res$F.na[,1])
    plot(mnp$abs,mnp$p,ty="l",xlab="Intensity values",ylab="Estimated probability to be MNAR")

[Package imp4p version 0.7 Index]