miss.total.process {imp4p} | R Documentation |
Estimating the missing data mechanism in a sample.
Description
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).
Usage
miss.total.process(abs,pi_na,F_na,F_tot)
Arguments
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 |
Value
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. |
Author(s)
Quentin Giai Gianetto <quentin2g@yahoo.fr>
See Also
Examples
#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")
[Package imp4p version 1.2 Index]