prob_mcar: Estimation of a vector of probabilities that missing values...

Description Usage Arguments Value Author(s) See Also Examples

Description

This function returns a vector of probabilities that each missing value is MCAR from specified confidence intervals.

Usage

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prob.mcar(b.l,b.u,absc,pi.mcar,F.tot,F.na)

Arguments

b.l

A numeric vector of lower bounds for missing values.

b.u

A numeric vector of upper bounds for missing values.

absc

The interval on which is estimated the MCAR data mechanism.

pi.mcar

The estimated proportion of MCAR values.

F.tot

An estimation of the cumulative distribution function of the complete values on the interval absc.

F.na

An estimation of the cumulative distribution function of the missing values on the interval absc.

Value

A numeric vector of estimated probabilities to be MCAR for missing values in the confidence intervals defined by b.l and b.u. The input arguments absc, pi.mcar, F.tot and F.na can be estimated thanks to the function estim.mix.

Author(s)

Quentin Giai Gianetto <[email protected]>

See Also

estim.mix

Examples

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#Simulating data
#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);

#Computing probabilities to be MCAR
born=estim.bound(tab=res.sim$dat.obs,conditions=res.sim$condition);

#Computing probabilities to be MCAR in the first column of result$tab.mod
proba=prob.mcar(b.l=born$tab.lower[,1],b.u=born$tab.upper[,1],absc=res$abs.mod,
pi.mcar=res$pi.mcar[1], F.tot=res$F.tot[,1], F.na=res$F.na[,1]);

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