Description Usage Arguments Value Author(s) See Also Examples
This function allows estimating the missing data mechanism, i.e. the probability to be missing in function of the intensity level, from an estimation of a mixture model of MNAR and MCAR values (see estim.mix
function).
1 | miss.total.process(abs,pi_na,F_na,F_tot)
|
abs |
The interval on which is estimated the missing data mechanism. |
pi_na |
The proportion of missing values. |
F_na |
An estimation of the cumulative distribution function of the missing values on the interval |
F_tot |
An estimation of the cumulative distribution function of the complete values on the interval |
A list composed of:
abs |
The interval on which is estimated the missing data mechanism. |
p |
The estimated probability to be missing in function of the intensity level. |
Quentin Giai Gianetto <quentin2g@yahoo.fr>
1 2 3 4 5 6 7 8 9 10 11 12 | #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$condition);
#Estimating the missing mechanism in the first replicate
mtp=miss.total.process(res$abs.mod,res$pi.na[1],res$F.na[,1],res$F.tot[,1])
plot(mtp$abs,mtp$p,ty="l",xlab="Intensity values",ylab="Estimated probability to be missing")
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