prob.mcar {imp4p}

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 <[e-mail]>

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]);