bootstrap_and_CI {ppsbm} | R Documentation |
Bootstrap and Confidence Bands
Description
Plots confidence bands for estimated intensities between pairs of groups obtained by bootstrap.
Usage
bootstrap_and_CI(
sol,
Time,
R,
alpha = 0.05,
nbcores = 1,
d_part = 5,
n_perturb = 10,
perc_perturb = 0.2,
directed,
filename = NULL
)
Arguments
sol |
One list (for one value of |
Time |
Positive real number. [0,Time] is the total time interval of observation. |
R |
Number of bootstrap samples. |
alpha |
Level of confidence: |
nbcores |
Number of cores for parallel execution. If set to 1 it does sequential execution. Beware: parallelization with fork (multicore): doesn't work on Windows! |
d_part |
Maximal level for finest partitions of time interval [0,T], used for kmeans initializations on the bootstrap samples
|
n_perturb |
Number of different perturbations on k-means result on the bootstrap samples. |
perc_perturb |
Percentage of labels that are to be perturbed (= randomly switched) on the bootstrap samples. |
directed |
Boolean for directed (TRUE) or undirected (FALSE) case. |
filename |
Name of the file where to save the results. |
Details
Not for sparse models and only for histogram method.
Examples
# data of a synthetic graph with 50 individuals and 3 clusters
n <- 50
Q <- 3
Time <- generated_Q3$data$Time
data <- generated_Q3$data
z <- generated_Q3$z
K <- 2^3
# VEM-algo hist:
sol.hist <- mainVEM(list(Nijk=statistics(data,n,K,directed=FALSE),Time=Time),
n,Qmin=3,directed=FALSE,method='hist',d_part=1,n_perturb=0)[[1]]
# compute bootstrap confidence bands
boot <- bootstrap_and_CI(sol.hist,Time,R=10,alpha=0.1,nbcores=1,d_part=1,n_perturb=0,
directed=FALSE)
# plot confidence bands
alpha.hat <- exp(sol.hist$logintensities.ql)
vec.x <- (0:K)*Time/K
ind.ql <- 0
par(mfrow=c(2,3))
for (q in 1:Q){
for (l in q:Q){
ind.ql <- ind.ql+1
ymax <- max(c(boot$CI.limits[ind.ql,2,],alpha.hat[ind.ql,]))
plot(vec.x,c(alpha.hat[ind.ql,],alpha.hat[ind.ql,K]),type='s',col='black',
ylab='Intensity',xaxt='n',xlab= paste('(',q,',',l,')',sep=""),
cex.axis=1.5,cex.lab=1.5,ylim=c(0,ymax),main='Confidence bands')
lines(vec.x,c(boot$CI.limits[ind.ql,1,],boot$CI.limits[ind.ql,1,K]),col='blue',
type='s',lty=3)
lines(vec.x,c(boot$CI.limits[ind.ql,2,],boot$CI.limits[ind.ql,2,K]),col='blue',
type='s',lty=3)
}
}