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sethharju authored Sep 12, 2023
1 parent 0ed3715 commit c7410ca
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11 changes: 11 additions & 0 deletions model-api/src/model-builder/code-blocks/derived-quantities/pred-site-means/_template_
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for(i in PRED_SITE_IDX) {
## Site-level predictions at observed levels of any covariates.
pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]] <- LINK
B[j.pred[i], 1, k.pred[i]] +
B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta
)
#pred.site.sd[j.pred[i], k.pred[i]] <- sigma.ss[j.pred[i], k.pred[i]]
PRED_SITE_NEW_OBS_SWITCH
PRED_SITE_OOS_SWITCH
}
13 changes: 13 additions & 0 deletions model-api/src/model-builder/code-blocks/derived-quantities/pred-site-means/_zi-template_
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for(i in PRED_SITE_IDX) {
## Site-level predictions at observed levels of any covariates.
pred.site.mu.p[j.pred[i], x.pred.index[i], k.pred[i]] <- LINK
B[j.pred[i], 1, k.pred[i]] +
B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta
)
#pred.site.z[j.pred[i], x.pred.index[i], k.pred[i]] ~ dbern(p.zero[k.pred[i]])
pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]] <-
pred.site.mu.p[j.pred[i], x.pred.index[i], k.pred[i]] * (1 - p.zero[k.pred[i]])
PRED_SITE_NEW_OBS_SWITCH
PRED_SITE_OOS_SWITCH
}
16 changes: 16 additions & 0 deletions model-api/src/model-builder/code-blocks/derived-quantities/pred-site-means/beta-binomial
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for(i in PRED_SITE_IDX) {
## Site-level predictions at observed levels of any covariates.
pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]] <- LINK
B[j.pred[i], 1, k.pred[i]] +
B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta
)
pred.site.p[j.pred[i], x.pred.index[i], k.pred[i]] <- ilogit(
B[j.pred[i], 1, k.pred[i]] +
B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta +
site.eps[j.pred[i], k.pred[i]]
)
PRED_SITE_NEW_OBS_SWITCH
PRED_SITE_OOS_SWITCH
}
10 changes: 10 additions & 0 deletions model-api/src/model-builder/code-blocks/derived-quantities/pred-site-means/gen-pois
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for(i in PRED_SITE_IDX) {
pred.site.eta[j.pred[i], x.pred.index[i], k.pred[i]] <-
B[j.pred[i], 1, k.pred[i]] + B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta
pred.site.lambda[j.pred[i], x.pred.index[i], k.pred[i]] <-
(1 - delta[k.pred[i]]) * exp(pred.site.eta[j.pred[i], x.pred.index[i], k.pred[i]])
pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]] <-
pred.site.lambda[j.pred[i], x.pred.index[i], k.pred[i]] / (1 - delta[k.pred[i]])
PRED_SITE_OOS_SWITCH
}
23 changes: 23 additions & 0 deletions ...c/model-builder/code-blocks/derived-quantities/pred-site-means/hurdle-ordinal-latent-beta
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for(i in PRED_SITE_IDX) {
## Site-level predictions at observed levels of any covariates.
phi.pred.site[j.pred[i], x.pred.index[i], k.pred[i]] <- ilogit(
G[j.pred[i], 1, k.pred[i]] +
G[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Gamma
)
mu.pred.site[j.pred[i], x.pred.index[i], k.pred[i]] <- ilogit(
B[j.pred[i], 1, k.pred[i]] +
B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta
)

PRED_SITE_NEW_OBS_SWITCH
PRED_SITE_OOS_SWITCH
pred.site.mean.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mu.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]] *
(1 - phi.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
pred.site.mean.01.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mean(phi.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
pred.site.mean.gt0.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mean(mu.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
}
21 changes: 21 additions & 0 deletions ...-builder/code-blocks/derived-quantities/pred-site-means/hurdle-ordinal-latent-beta-b-drop
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for(i in PRED_SITE_IDX) {
## Site-level predictions at observed levels of any covariates.
phi.pred.site[j.pred[i], x.pred.index[i], k.pred[i]] <- ilogit(
G[j.pred[i], 1, k.pred[i]] +
G[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Gamma
)
mu.pred.site[j.pred[i], x.pred.index[i], k.pred[i]] <- ilogit(
B[j.pred[i], 1, k.pred[i]]
)

PRED_SITE_NEW_OBS_SWITCH
PRED_SITE_OOS_SWITCH
pred.site.mean.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mu.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]] *
(1 - phi.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
pred.site.mean.01.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mean(phi.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
pred.site.mean.gt0.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mean(mu.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
}
19 changes: 19 additions & 0 deletions ...builder/code-blocks/derived-quantities/pred-site-means/hurdle-ordinal-latent-beta-bg-drop
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for(i in PRED_SITE_IDX) {
## Site-level predictions at observed levels of any covariates.
phi.pred.site[j.pred[i], x.pred.index[i], k.pred[i]] <- ilogit(
G[j.pred[i], 1, k.pred[i]]
)
mu.pred.site[j.pred[i], x.pred.index[i], k.pred[i]] <- ilogit(
B[j.pred[i], 1, k.pred[i]]
)

PRED_SITE_NEW_OBS_SWITCH
PRED_SITE_OOS_SWITCH
pred.site.mean.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mu.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]] *
(1 - phi.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
pred.site.mean.01.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mean(phi.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
pred.site.mean.gt0.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mean(mu.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
}
21 changes: 21 additions & 0 deletions ...-builder/code-blocks/derived-quantities/pred-site-means/hurdle-ordinal-latent-beta-g-drop
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for(i in PRED_SITE_IDX) {
## Site-level predictions at observed levels of any covariates.
phi.pred.site[j.pred[i], x.pred.index[i], k.pred[i]] <- ilogit(
G[j.pred[i], 1, k.pred[i]]
)
mu.pred.site[j.pred[i], x.pred.index[i], k.pred[i]] <- ilogit(
B[j.pred[i], 1, k.pred[i]] +
B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta
)

PRED_SITE_NEW_OBS_SWITCH
PRED_SITE_OOS_SWITCH
pred.site.mean.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mu.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]] *
(1 - phi.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
pred.site.mean.01.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mean(phi.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
pred.site.mean.gt0.oos[j.pred[i], x.pred.index[i], k.pred[i]] <-
mean(mu.pred.site.oos[j.pred[i], x.pred.index[i], k.pred[i]])
}
9 changes: 9 additions & 0 deletions model-api/src/model-builder/code-blocks/derived-quantities/pred-site-means/lognormal
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for(i in PRED_SITE_IDX) {
## Site-level predictions at observed levels of any covariates.
pred.site.mean.log.y[j.pred[i], x.pred.index[i], k.pred[i]] <- log(pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]]) -
1/2 * log((sigma.ss[j.pred[i], k.pred[i]]^2 + pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]]^2) / pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]]^2)
pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]] <- LINK
B[j.pred[i], 1, k.pred[i]] + B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta
)
}
12 changes: 12 additions & 0 deletions model-api/src/model-builder/code-blocks/derived-quantities/pred-site-means/monomolecular
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for(i in PRED_SITE_IDX) {
## Site-level predictions at observed levels of any covariates.
pred.site.lp[j.pred[i], x.pred.index[i], k.pred[i]] <- LINK
B[j.pred[i], 1, k.pred[i]] +
B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta
)
pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]] <-
max(0.00001, maxR * (1 - exp(-pred.site.lp[j.pred[i], x.pred.index[i], k.pred[i]])))
PRED_SITE_NEW_OBS_SWITCH
PRED_SITE_OOS_SWITCH
}
9 changes: 9 additions & 0 deletions ...src/model-builder/code-blocks/derived-quantities/pred-site-means/negative-binomial-DEPREC
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for(i in PRED_SITE_IDX) {
## Site-level predictions at observed levels of any covariates.
pred.site.sigma.y[j.pred[i], x.pred.index[i], k.pred[i]] <-
sigma.ss[j.pred[i], k.pred[i]]
pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]] <- LINK
B[j.pred[i], 1, k.pred[i]] + B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta
)
}
26 changes: 26 additions & 0 deletions ...pi/src/model-builder/code-blocks/derived-quantities/pred-site-means/ordinal-latent-normal
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for(i in PRED_SITE_IDX) {
pred.site.mu[j.pred[i], x.pred.index[i], k.pred[i]] <-
B[j.pred[i], 1, k.pred[i]] + B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta

pred.site.pr[j.pred[i], x.pred.index[i], k.pred[i], 1] <-
pnorm(theta[1], pred.site.mu[j.pred[i], x.pred.index[i], k.pred[i]] ,
tau.site[j.pred[i], k.pred[i]])
for (m in 2:(nYlevels - 1)) {
pred.site.pr[j.pred[i], x.pred.index[i], k.pred[i], m] <-
max(0,
pnorm(theta[m] , pred.site.mu[j.pred[i], x.pred.index[i], k.pred[i]],
tau.site[j.pred[i], k.pred[i]]) -
pnorm(theta[m - 1] , pred.site.mu[j.pred[i], x.pred.index[i], k.pred[i]],
tau.site[j.pred[i], k.pred[i]]))
}
pred.site.pr[j.pred[i], x.pred.index[i], k.pred[i], nYlevels] <-
1 - pnorm(theta[nYlevels - 1], pred.site.mu[j.pred[i], x.pred.index[i], k.pred[i]],
tau.site[j.pred[i], k.pred[i]])

pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]] <-
inprod(pred.site.pr[j.pred[i], x.pred.index[i], k.pred[i], 1:nYlevels], 1:nYlevels)

PRED_SITE_NEW_OBS_SWITCH
PRED_SITE_OOS_SWITCH
}
20 changes: 20 additions & 0 deletions .../model-builder/code-blocks/derived-quantities/pred-site-means/zero-inflated-beta-binomial
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for(i in PRED_SITE_IDX) {
## Site-level predictions at observed levels of any covariates.
pred.site.mu.p[j.pred[i], x.pred.index[i], k.pred[i]] <- LINK
B[j.pred[i], 1, k.pred[i]] +
B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta
)
pred.site.mean[j.pred[i], x.pred.index[i], k.pred[i]] <-
pred.site.mu.p[j.pred[i], x.pred.index[i], k.pred[i]] * (1 - site.z[j.pred[i], k.pred[i]])
pred.site.p[j.pred[i], x.pred.index[i], k.pred[i]] <- ilogit(
B[j.pred[i], 1, k.pred[i]] +
B[j.pred[i], 2, k.pred[i]] * x.pred[i] +
X.pred[i, ] %*% Beta +
site.eps[j.pred[i], k.pred[i]]
)
pred.site.mean.extra[j.pred[i], x.pred.index[i], k.pred[i]] <-
pred.site.p[j.pred[i], x.pred.index[i], k.pred[i]] * (1 - site.z[j.pred[i], k.pred[i]])
PRED_SITE_NEW_OBS_SWITCH
PRED_SITE_OOS_SWITCH
}

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