Is there a LME function for autocorrelation?
My issue is that I have tried a number of different autocorrelation structures and can’t seem to achieve the random, non-significant residuals needed to confirm a lack of autocorrelation. I am using the function lme in the R package nlme to deal with autocorrelation.
How to deal with autocorrelation in mixed models?
I tried using corAR1 (correlation = corAR1 (form = ~ 1 | whale)) but when I plot ACF there is still a strong autocorrelation. What can this mean and how could I proceed to solve it?
What are the lags in autocorrelation R-Cross?
As I mentioned above, I have tried a number of different cor functions (the four listed above) and different form specifications. They all end up with ACF/PCF plots like the last two models with a first lag at below 0.2 in the ACF plot and a PCF plot with the first three lags around 0.10.
When to leave residual autocorrelation in a model?
“The estimate is slightly larger than zero so will have negligible effect on the model fit and hence you might wish to leave it in the model if there is a strong a priori reason to assume residual autocorrelation.” Potentially there is some autocorrelation that is not being caused by temporal autocorrelation, like outliers?
What is the function LME in the your package nlme?
I am using the function lme in the R package nlme to deal with autocorrelation. I have tried the various autocorrelation classes with variations to form 1) corAR1 (autoregressive process of order 1).
How does autocorrelation affect the coefficients of a model?
This post ( Do autocorrelated residual patterns remain even in models with appropriate correlation structures, & how to select the best models?) states that small amounts of autocorrelation probably won’t impact the model coefficients very much.
How to plot the autocorrelation function in R?
How to Plot the Autocorrelation Function in R. We can plot the autocorrelation function for a time series in R by simply not using the pl=FALSE argument: #plot autocorrelation function acf (x) The x-axis displays the number of lags and the y-axis displays the autocorrelation at that number of lags. By default, the plot starts at lag = 0 and