Open gavinsimpson opened 4 years ago
Thanks for opening this issue! Can we think of another name as cor_car1 though as it is too close to the cor_car function for (spatial) conditionally autoregessive models and thus will confuse users.
Would cor_ctar1()
be sufficiently different or do we need to think further from the box?
Sounds reasonable. Perhaps we should go for cor_ctar
and make the 1
an argument in case we ever want to go beyond processes of order 1.
cor_ctar()
sounds good to me. :+1:
Has the cor_ctar
function been added yet? I cant seem to find any other (except this one) reference to it. If not, are there any plans to add this continuous temporal term?
It has not been added yet unfortunately. There are plans as per this issue but no specific timeline.
The discrete time AR(1) correlation structure can be generalised to the continuous time setting.
A continuous time AR(1) or CAR(1) correlation structure can be defined as
h(s, ϕ) = ϕs, s ≥ 0, ϕ ≥ 0
where s is a non-negative real. In contrast to the AR(1), the correlation parameter ϕ is constrained to be non-negative. The CAR(1) correlation function is a univariate special case of the exponential spatial correlation function.
The nlme package has an implementation of this correlation function in its
nlme::corCAR1()
.A
cor_car1()
function in brms mirroringcor_ar()
but for the CAR(1) correlation would be very useful for modelling longitudinal data for example in settings where the observations are not regularly spaced in time.There is a small amount of discussion of this on the Stan Discourse site.