Estimating the MCAR mechanism in a sample.

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Description

This function allows estimating the MCAR data mechanism, i.e. the probability to be MCAR 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.mcar.process(abs,pi_mcar,F_tot,F_na)

Arguments

abs

The interval on which is estimated the MCAR data mechanism.

pi_mcar

An estimation of the proportion of MCAR values.

F_tot

An estimation of the cumulative distribution function of the complete 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 MCAR data mechanism.

p

The estimated probability to be MCAR given that the value is missing on the interval abs.

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 MCAR mechanism in the first replicate
mcp=miss.mcar.process(res$abs.mod,res$pi.mcar[1],res$F.tot[,1],res$F.na[,1])
plot(mcp$abs,mcp$p,ty="l",xlab="Intensity values",ylab="Estimated probability to be MCAR")

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