R: Estimating the missing data mechanism in a sample.

miss.total.process {imp4p} | R Documentation

## Estimating the missing data mechanism in a sample.

### 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

    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 <quentin2g@yahoo.fr>

### See Also

`estim.mix`

### Examples

    #Simulating data
    res.sim=sim.data(nb.pept=2000,nb.miss=600);

    #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")

[Package imp4p version 1.1 Index]