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i am using sarima() function as below ___________________________________________________________________________________________ sarima=function(data,p,d,q,P=0,D=0,Q=0,S=-1,tol=.001){ n=length(data) constant=1:n xmean=matrix(1,n,1) if (d>0 & D>0) fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q), period=S), optim.control=list(trace=1,REPORT=1,reltol=tol)) if (d>0 & D==0) fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q), period=S), xreg=constant,include.mean=F, optim.control=list(t...

Can someone please explain what the $minimum result of the optimize function actually is? I'm trying to optimize the function: fitIT<-function(ampFac,ts_wave1,ts_template){ template<-stretchWaveTime(ts_template,ampFac) fit<-calcFit(ts_wave1,template) return(fit) } with >optimize(f=fitIT,interval=c(0.5,4),ts_wave1=test.data[,1],ts_template=test.data[,1]) $minimum [1] 3.764685 $objective [1] 1.037864 how...

Dear all, I'm new to R and time series analysis. I'd appreciate if you could shed light on my problem. Here is what I have been trying to do: 1. I fit the model ARIMA(1,0,0) with the training dataset xdata[1:100] fitit = arima(xdata, order=c(1,0,0) 2. I have some current observations in the buffer. Say that buf = xdata_new[1:20] 3. I'm trying to forecast the xdata_new[21] based on the history fore=predict(fitit,buf,n.ahead=1) However, the results of fore$pred give me 20 predicted values from 101 to 120. I&...

Just a general question concerning the woolf test (package vcd), when we have stratified data (2x2 tables) and when the p.value of the woolf-test is below 0.05 then we assume that there is a heterogeneity and a common odds ratio cannot be computed? Does this mean that we have to try to add more stratification variables (stratify more) to make the woolf-test p.value insignificant? Also in the