imp4p: miss_mcar_process – R documentation

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miss_mcar_process

Estimating the MCAR mechanism in a sample.

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

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>

Examples

#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 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")

imp4p

Imputation for Proteomics

v1.1

GPL-3

Authors

Quentin Giai Gianetto

Initial release