prob.mcar {imp4p} | R Documentation |
Estimation of a vector of probabilities that missing values are MCAR.
Description
This function returns a vector of probabilities that each missing value is MCAR from specified confidence intervals.
Usage
prob.mcar(b.u,absc,pi.na,pi.mcar,F.tot,F.obs)
Arguments
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 |
Value
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
.
Author(s)
Quentin Giai Gianetto <quentin2g@yahoo.fr>
See Also
Examples
#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]);