#example of field pattern distribution:
fieldpattern<-rnorm(100,10,5)
#model results:
model1runs<- rnorm(100*5,10.5,6)
model2runs<- rnorm(100*5,9.5,2)
#POMIC measurements:
pomic(fieldpattern,model1runs,eps=10^-20,plotting=TRUE)
pomic(fieldpattern,model2runs,eps=10^-20,plotting=TRUE)
pomic.simple(fieldpattern,model2runs,eps=10^-20)
pomic.corrected(fieldpattern,model1runs,eps=10^-20)
pomic.corrected(fieldpattern,model2runs,eps=10^-20)
#An analysis with random POMIC scores:
dataset<-data.frame(P1=rep(1:5,each=5),P2=rep(seq(0,5,length=5),5),
pomics=runif(25)*20)
analyse.pomics(dataset,c(1,2),3)
#example for time series patterns:
timeserie<-runif(100)+1:100-(seq(1,10,length=100)^2)
model<-NULL
for(i in 1:100){
model<-cbind(model,runif(100)+1:100-(seq(1,10,length=100)^2.01))
}
pomic.timeseries(timeserie,model,eps=10^-20,fullmsd=TRUE,
plotting=TRUE,half_window_size=10,
check_whole=TRUE,check_diffs=TRUE)
Run the code above in your browser using DataLab