miss_mnar_process: Estimating the MNAR mechanism in a sample.

Description Usage Arguments Value Author(s) See Also Examples

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

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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 <[email protected]>

See Also

estim.mix

Examples

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#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")

imp4p documentation built on June 1, 2017, 5:02 p.m.