I’m working with time series data. Several variables among subjects are measured at intervals of two weeks over 1.5 years. The main goal is to estimate associations between different variables.

Consider a mixed model with AR(1) autocorrelation. In my sample, time points for measurements are irregular. Alternately there is a random coefficient model. How does a random effect structure handle correlation between observations? If the autocorrelation between measurements within subject is constant no matter how far apart in time the measurements are, the model is not an option for me.

I’m working with time series data. Several variables among subjects are measured at intervals of two weeks over 1.5 years. The main goal is to estimate associations between different variables.

Consider a mixed model with AR(1) autocorrelation. In my sample, time points for measurements are irregular, so the spherical correlation between ordered observations in time may be imprecise. Alternately there is a random coefficient model. How does a random effect structure handle correlation between observations? If the autocorrelation between measurements within subject is constant no matter how far apart in time the measurements are, the model is not an option for me.

I’m working with time series data (samplings been done for about every two weeks in 1,5 year) from several subjects where they have measured different variables changing in time. The main goal is to see the relationship between different variables. Can one variable be predicted from another? I'd like to use a mixed model (for two variables at a time) with autocorrelation similar to AR(1) where measurements closer in time are more correlated that measurements further apart. The sample time points for measurements are irregular though. I've heard that the random coefficient model is to prefer over covariance pattern model if time is irregular. But I can't see how the autocorrelation is taking care of just because intercept are a random effect within subjects. If the autocorrelation between measurements within subject is constant no matter how far apart in time the measurements are, the model is not an option for me.

I’m working with time series data. Several variables among subjects are measured at intervals of two weeks over 1.5 years. The main goal is to estimate associations between different variables.

Consider a mixed model with AR(1) autocorrelation. In my sample, time points for measurements are irregular. Alternately there is a random coefficient model. How does a random effect structure handle correlation between observations? If the autocorrelation between measurements within subject is constant no matter how far apart in time the measurements are, the model is not an option for me.

I’m working with time series data (samplings been done for about every two weeks in 1,5 year) from several subjects where they have measured different variables changing in time. The main goal is to see the relationship between different variables. Can one variable be predicted from another? I like to use a mixed model (for two variables at a time) with autocorrelation similar to AR(1) where measurements closer in time are more correlated that measurements further apart. The sample time points for measurements are irregular though. I've heard that the random coefficient model is to prefer over covariance pattern model if time is irregular. But I cant se how the autocorrelation is taking care of just because intercept are a random effect within subjects. Is the autocorrelation between measurements within subject constant no matter how far apart in time the measurements are, the model is not an option for me.

I’m working with time series data (samplings been done for about every two weeks in 1,5 year) from several subjects where they have measured different variables changing in time. The main goal is to see the relationship between different variables. Can one variable be predicted from another? I'd like to use a mixed model (for two variables at a time) with autocorrelation similar to AR(1) where measurements closer in time are more correlated that measurements further apart. The sample time points for measurements are irregular though. I've heard that the random coefficient model is to prefer over covariance pattern model if time is irregular. But I can't see how the autocorrelation is taking care of just because intercept are a random effect within subjects. If the autocorrelation between measurements within subject is constant no matter how far apart in time the measurements are, the model is not an option for me.