feature/car

[imadmali]

This is almost ready to go. But before it gets merged there are a couple of issues that I need help with.

• loo doesn't work for binomial(link="log") but it works for the other link functions in the binomial family.
• some of the tests are failing (e.g. test_pp_validate.R)

[bgoodri]

Thanks. Jonah and / or I will look at this. What is the INLA package actually being used for? It would be better if we didn't have to suggest a non-CRAN package.

[imadmali]

I forgot to drop that requirement. It was being used for INLA::qinv but I am doing the non-sparse version here: https://github.com/stan-dev/rstanarm/blob/feature/car/R/stan_spatial.fit.R#L390-L423

[bgoodri]

Got a bunch of things from R CMD check particularly if --run-dontrun --run-donttest is specified

[bgoodri]

Ah, I must have had eval = FALSE set.

On Sun, Oct 28, 2018 at 4:50 PM Imad Ali [email protected] wrote:

@imadmali commented on this pull request.

In vignettes/spatial.Rmd https://github.com/stan-dev/rstanarm/pull/322#discussion_r228764618:

+The linear predictor takes the following form, +$$ +\boldsymbol{\eta} = \alpha + \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\psi} +$$ +where $\alpha$ is the intercept, $\mathbf{X}$ is an $N$-by-$K$ matrix of predictors ($N$ being the number of observations and $K$ being the number of predictors), $\boldsymbol{\beta}$ is a $K$-dimensional vector of regression coefficients, and $\boldsymbol{\psi}$ is a $N$-dimensional vector representing the spatial effect. The construction of $\boldsymbol{\psi}$ depends on the model, which is discussed in the relevant sections below. + +Depending on the choice of likelihood there may or may not be an additional auxiliary parameter $\gamma$ in the model (e.g. in a Gaussian likelihood this would be the variation of the data). With all these components, for some probability density/mass function $f$, we can state the general form of the likelihood as, + +$$ +\mathcal{L}(\alpha, \boldsymbol{\beta}, \gamma | \mathbf{y}) = \prod_{i=1}^N f(y_i | \alpha, \boldsymbol{\beta}, \gamma ) +$$ + +## GMRF Hierarchical Component + +CAR models require that you define the spatial component as a Gaussian Markov Random Field (GMRF). The random vector $\boldsymbol{\phi}$ is a GMRF with respect to the graph $\mathcal{G} = (\mathcal{V} = {1,\ldots,n},\mathcal{E})$ with mean vector $\boldsymbol{\mu}$ and precision matrix $\mathbf{W}$ if its probability density function takes the precision form of the multivariate normal distribution,

Like you want to include a plot of the lattice? I have that in there if you build the vignette.

— You are receiving this because you commented. Reply to this email directly, view it on GitHub https://github.com/stan-dev/rstanarm/pull/322#discussion_r228764618, or mute the thread https://github.com/notifications/unsubscribe-auth/ADOrqlcmrkxFaNR0hiT4iMMwDyxzV4q8ks5uphiCgaJpZM4X-FS1 .

[imadmali]

@bgoodri thanks for bringing this up-to-date. I'll try to take a look over the weekend to see if I've left anything hanging.

[jgabry]

Just checking in on this. Any status update?

[bgoodri]

I think we can merge new .stan files again now that Windows can be tricked into using LTO.

On Mon, Nov 30, 2020 at 2:40 PM Jonah Gabry [email protected] wrote:

Just checking in on this. Any status update?

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/stan-dev/rstanarm/pull/322#issuecomment-735998957, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAZ2XKXAH3YTPQNMQW6LJM3SSPYMPANCNFSM4F7YKS2Q .

[jgabry]

Does that mean we can also finally merge the survival stuff?

[bgoodri]

Maybe

On Mon, Nov 30, 2020 at 6:53 PM Jonah Gabry [email protected] wrote:

Does that mean we can also finally merge the survival stuff?

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/stan-dev/rstanarm/pull/322#issuecomment-736125168, or unsubscribe https://github.com/notifications/unsubscribe-auth/AAZ2XKSJ23VU2JFNHMWVHLLSSQWAJANCNFSM4F7YKS2Q .