miss_total_process: Estimating the missing data mechanism in a sample.

Description Usage Arguments Value Author(s) See Also Examples

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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