Estimation of a matrix of probabilities that missing values are MCAR.
This function returns a matrix of probabilities that each missing value is MCAR from specified confidence intervals.
prob.mcar.tab(tab.u,res)
tab.u |
A numeric matrix of upper bounds for missing values. |
res |
An output list resulting from the function |
A numeric matrix of estimated probabilities to be MCAR for missing values assuming upper bounds for them (tab.u
).
Quentin Giai Gianetto <quentin2g@yahoo.fr>
#Simulating data res.sim=sim.data(nb.pept=2000,nb.miss=600,para=5); #Imputation of missing values with a MCAR-devoted algorithm: here 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$condition); proba=prob.mcar.tab(born$tab.upper,res); #Histogram of probabilities to be MCAR associated to generated MCAR values hist(proba[res.sim$list.MCAR[[1]],1], freq=FALSE,main="Estimated probabilities to be MCAR for known MCAR values",xlab="",col=2);
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