Estimating the missing data mechanism in a sample.

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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

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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

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#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600,pi.mcar=0.2,para=10,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 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")

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