Timeline for Testing the independence of a time series
Current License: CC BY-SA 3.0
3 events
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| May 9, 2016 at 13:56 | comment | added | Zap |
And furthermore I fail to reproduce the acf-values from your data. Here is my code for lag 1: n1<-nottem[1:(length(nottem)-1)] n2<-nottem[2:(length(nottem))] m1<-mean(n1) m2<-mean(n2) acf_lag1<-sum((n1-m1)*(n2-m2))/(sqrt(sum((n1-m1)^2))*sqrt(sum((n2-m2)^2))) acf(nottem)$acf[2]
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| May 9, 2016 at 11:58 | comment | added | Zap |
Well I expected a low p-value because all observation are nearly the same, so a high correlation. After wiki I still have troubles in understanding those plots. For example acf has six breaks on the x-axis between 0 and 0.5 but delivers only five values in the plot.For pacf it's six breaks, six values. And what sense does a lag of for example 0.3 in your data even make? R can not even perform that since it expects integers. lag(nottem,k=0.3)
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| May 9, 2016 at 8:58 | history | answered | JupiterM104 | CC BY-SA 3.0 |