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I forgot to drop that requirement. It was being used for |
bgoodri
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bgoodri
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bgoodri
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| vector[order == 2 ? Q_n[1] : 0] w; | ||
| int v[order == 2 ? Q_n[1] : 0]; | ||
| int u[order == 2 ? N+1 : 0]; | ||
| // prior stuff |
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Can this all be handled by a #include /data/hyperparameters.stan like many of the other Stan programs?
bgoodri
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| else if (model_type == 3) | ||
| psi = tau*(sqrt(1-rho[1])*theta_raw + sqrt(rho[1]/scaling_factor)*phi); | ||
| // for regression coefficients | ||
| // "tparameters.stan" |
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Can this be subsumed with a #include?
bgoodri
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| } | ||
| /* else prior_dist is 0 and nothing is added */ | ||
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| // Log-prior for intercept |
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Can this be subsumed with a #include /model/priors_glm.stan ?
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We may need to separate out the intercept part of a GLM prior
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bgoodri
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| $$ | ||
| 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. | ||
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| 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, |
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Is
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It's aux in the stan model but I think this maps to different things depending on the likelihood (e.g. it's sigma in the gaussian case).
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| ## GMRF Hierarchical Component | ||
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| 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, |
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Can we include an image for a small graph like that?
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Like you want to include a plot of the lattice? I have that in there if you build the vignette.
bgoodri
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| ## An Example Using Simulated Data on a Lattice | ||
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| As an example we use spatial units defined on a lattice. Below we plot a GMRF of 900 spatial units available in the rstanarm package. |
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Got a bunch of things from |
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Ah, I must have had eval = FALSE set.
…On Sun, Oct 28, 2018 at 4:50 PM Imad Ali ***@***.***> wrote:
***@***.**** commented on this pull request.
------------------------------
In vignettes/spatial.Rmd
<#322 (comment)>:
> +
+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.
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@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. |
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Just checking in on this. Any status update? |
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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 ***@***.***> wrote:
Just checking in on this. Any status update?
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Does that mean we can also finally merge the survival stuff? |
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Maybe
…On Mon, Nov 30, 2020 at 6:53 PM Jonah Gabry ***@***.***> wrote:
Does that mean we can also finally merge the survival stuff?
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This is almost ready to go. But before it gets merged there are a couple of issues that I need help with.
loodoesn't work forbinomial(link="log")but it works for the other link functions in the binomial family.test_pp_validate.R)