Description Usage Arguments Details Value Examples
This function 'summerize' a (big) population in a smaller groups of individual. Hopefully, the smaller groups will have the same properties than the whole population. The trajectories of the smaller groups are called the 'senator' (since they are representing the whole population). The 'election' is done using the classical kmeans algorithm. The trajectories should be in 'wide' format.
1  reduceNbId(id, trajWide, nbSenators = 64, imputationMethod = "linearInterpol")

id 

trajWide 
[ 
nbSenators 
[ 
imputationMethod 
[ 
This function 'summerize' a (big) population in a smaller groups of individual. Hopefully, the smaller groups will have the same properties than the whole population. The trajectories of the smaller groups are called the 'senator' (since they are representing the whole population). The 'election' is done using the classical kmeans algorithm. The trajectories should be in 'wide' format.
A list with three fields:
mySenator: [data.frame]
whose first column is the individual
identifier and whose second column is the 'senator' that represent
the individual of the first column.
senatorsWide [matrix]
containing the trajectories of the senators,
in wide format. The first column is an unique identifier for each senators.
senatorsWeight[vector(numeric)]
Number of individual that a senator is representing
(i.e. number of individual that are in the cluster whose senator is the mean.)
1 2 3 4 5 6 7 8 9 10 11 12 13  par(mfrow=c(1,3))
### Some artificial data
myTraj < t(sapply(1:1000,function(x)dnorm(1:200,runif(1,50,150),20)*rnorm(1,10,2)))
matplot(t(myTraj),type="l",ylim=c(0,0.33))
### Election of 64 senator
### All individual is closed to one senators. Senators are representatives.
election64 < reduceNbId(id=1:1000,myTraj,nbSenators=64)
matplot(t(election64$senatorsWide[,1]),type="l",ylim=c(0,0.33))
### Election of 4 senators. They are not representatives.
election4 < reduceNbId(id=1:1000,myTraj,nbSenators=4)
matplot(t(election4$senatorsWide[,1]),type="l",ylim=c(0,0.33))

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