Estimation of a vector of probabilities that missing values are MCAR.
This function returns a vector of probabilities that each missing value is MCAR from specified confidence intervals.
prob.mcar(b.u,absc,pi.na,pi.mcar,F.tot,F.obs)
b.u |
A numeric vector of upper bounds for missing values. |
absc |
The interval on which is estimated the MCAR data mechanism. |
pi.na |
The estimated proportion of missing values. |
pi.mcar |
The estimated proportion of MCAR values among missing values. |
F.tot |
An estimation of the cumulative distribution function of the complete values on the interval |
F.obs |
An estimation of the cumulative distribution function of the missing values on the interval |
A numeric vector of estimated probabilities to be MCAR for missing values assuming upper bounds for them (b.u). The input arguments absc, pi.mcar, pi.na, F.tot and F.obs can be estimated thanks to the function estim.mix.
Quentin Giai Gianetto <quentin2g@yahoo.fr>
#Simulating data #Simulating data res.sim=sim.data(nb.pept=2000,nb.miss=600); #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.u=born$tab.upper[,1],absc=res$abs.mod,pi.na=res$pi.na[1], pi.mcar=res$pi.mcar[1], F.tot=res$F.tot[,1], F.obs=res$F.obs[,1]);
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