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Create a prediction function in generated quantities
adChong
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Nov 26, 2019
| for (i in 1:(n-1)) { | ||
| sigma_delta[i, i] = 1/lambda_delta + 1e-9; | ||
| for (j in (i+1):n) { | ||
| sigma_delta[i, j] = exp(-dot_self((xf[i] - xf[j]) .* beta_delta))/lambda_delta; |
| for (i in 1:(N-1)) { | ||
| sigma_eta[i, i] = 1/lambda_eta + sigma; | ||
| for (j in (i+1):N) { | ||
| sigma_eta[i, j] = exp(-dot_self((xt[i] - xt[j]) .* beta_eta))/lambda_eta; |
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| // elements of sigma_eta | ||
| for (i in 1:(N-1)) { | ||
| sigma_eta[i, i] = 1/lambda_eta + sigma; |
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| cov_y_pred = sigma_eta_pred + sigma_delta_pred; | ||
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| // Evaluate COV{y(x_pred, tf)|y,.} = s22 - s21 s11{-1} s12 |
Owner
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align your comments with the code
| } | ||
| for (i in 1:n) { | ||
| for (j in 1:n_pred) { | ||
| k_x1_x2[i, j] = k_x1_x2[i, j] + exp(-dot_self((xt[i][1:p] - xt_pred[j][1:p]) .* beta_delta)) / lambda_delta; |
| for (i in 1:(n_pred-1)) { | ||
| sigma_delta_pred[i, i] = 1/lambda_delta + 1e-9; | ||
| for (j in (i+1):n_pred) { | ||
| sigma_delta_pred[i, j] = exp(-dot_self((xt_pred[i][1:p] - xt_pred[j][1:p]) .* beta_delta))/lambda_delta; |
| } | ||
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| generated quantities { | ||
| vector[n_pred] f_pred = gp_pred_rng(n, N, n_pred, p, q, z, xf, xc, x_pred, tf, tc, beta_eta, lambda_eta, beta_delta, lambda_delta, sigma); |
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| // elements of sigma_delta and add observation errors | ||
| for (i in 1:(n-1)) { | ||
| sigma_delta[i, i] = 1/lambda_delta + 1e-9; |
Owner
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set delta as a variable in the transformed data block instead of adding 1e-9 at every line
| row_vector<lower=0,upper=1>[p] rho_delta; | ||
| real<lower=0> lambda_eta; | ||
| real<lower=0> lambda_delta; | ||
| real<lower=0> sigma; |
Owner
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I'm confused by your use of sigma and delta.
Wouldn't sigma = 1/lambda_eta and delta is just a constant used to make the covariance matrix positive definite.
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Create a prediction function in generated quantities