R: Estimation of a vector of probabilities that missing values...

prob.mcar {imp4p} | R Documentation

## Estimation of a vector of probabilities that missing values are MCAR.

### Description

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

### Usage

    prob.mcar(b.u,absc,pi.na,pi.mcar,F.tot,F.obs)

### Arguments

`b.u` | A numeric vector of upper bounds for missing values.
`absc` | The interval on which is estimated the MCAR data mechanism.
`pi.na` | The estimated proportion of missing values.
`pi.mcar` | The estimated proportion of MCAR values among missing values.
`F.tot` | An estimation of the cumulative distribution function of the complete values on the interval `absc`.
`F.obs` | 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 assuming upper bounds for them (`b.u`). The input arguments `absc`, `pi.mcar`, `pi.na`, `F.tot` and `F.obs` can be estimated thanks to the function `estim.mix`.

### Author(s)

Quentin Giai Gianetto <quentin2g@yahoo.fr>

### See Also

`estim.mix`

### Examples

    #Simulating data
    #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);

    #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$tab.mod
    proba=prob.mcar(b.u=born$tab.upper[,1],absc=res$abs.mod,pi.na=res$pi.na[1],
    pi.mcar=res$pi.mcar[1], F.tot=res$F.tot[,1], F.obs=res$F.obs[,1]);

[Package imp4p version 1.2 Index]