# Unlist the Value "solution"from the OUwie Results ou4.solution<-sapply(x,"[","solution") solution<-unlist(ou4.solution) # "solution1" Corresponds to Alpha. Take the Mean to Obtain a Mean Value for This Parameter alpha<-mean(subset(solution,names(solution)=="solution1")) # "solution2" Corresponds to Sigma. Take the Mean to Obtain a Mean Value for This Parameter sigma<-mean(subset(solution,names(solution)=="solution2")) # Unlist the Value "theta"from the OUwie Results oum.theta<-sapply(w,"[","theta") thetas<-unlist(oum.theta) # The Different Theta Values Correspond to the Optima for the Different Locomotor Habits. Take the Mean for Each Fos<-mean(subset(thetas,names(thetas)=="theta1")) Nat<-mean(subset(thetas,names(thetas)=="theta2")) Sca<-mean(subset(thetas,names(thetas)=="theta3")) Ter<-mean(subset(thetas,names(thetas)=="theta3")) # Combine All Mean Values into One R Object Fit.Values<-cbind(alpha,sigma,FosNat,Sca,Ter)