From 36773a3ae0f30221ef5420642fb4f196cf107758 Mon Sep 17 00:00:00 2001 From: Scott Claessens Date: Tue, 6 Aug 2024 18:11:51 +0100 Subject: [PATCH 01/12] Initial optimisations --- R/coev_make_stancode.R | 61 ++++++++++++++---------------------------- 1 file changed, 20 insertions(+), 41 deletions(-) diff --git a/R/coev_make_stancode.R b/R/coev_make_stancode.R index 4604db7..4575bab 100644 --- a/R/coev_make_stancode.R +++ b/R/coev_make_stancode.R @@ -101,54 +101,36 @@ coev_make_stancode <- function(data, variables, id, tree, # write functions block sc_functions <- paste0( "functions {\n", - " // returns the Kronecker Product\n", - " matrix kronecker_prod(matrix A, matrix B) {\n", - " matrix[rows(A) * rows(B), cols(A) * cols(B)] C;\n", - " int m;\n", - " int n;\n", - " int p;\n", - " int q;\n", - " m = rows(A);\n", - " n = cols(A);\n", - " p = rows(B);\n", - " q = cols(B);\n", - " for (i in 1:m)\n", - " for (j in 1:n)\n", - " for (k in 1:p)\n", - " for (l in 1:q)\n", - " C[p*(i-1)+k,q*(j-1)+l] = A[i,j]*B[k,l];\n", + " // returns the Kronecker sum of two square matrices with same dimensions\n", + " matrix kronecker_sum(matrix A, matrix B) {\n", + " int n = rows(A);\n", + " matrix[n*n, n*n] C;\n", + " for (i in 1:n) {\n", + " for (j in 1:n) {\n", + " for (k in 1:n) {\n", + " for (l in 1:n) {\n", + " C[(i-1)*n + k, (j-1)*n + l] = A[i,j] * (i == j) + B[k,l] * (k == l);\n", + " C[(i-1)*n + k, (j-1)*n + l] = A[i,j] * (k == l) + B[k,l] * (i == j);\n", + " }\n", + " }\n", + " }\n", + " }\n", " return C;\n", " }\n", " \n", - " // expected auto and cross effects over a discrete time t\n", - " matrix A_dt(matrix A, real t) {\n", - " return( matrix_exp(A * t) );\n", - " }\n", - " \n", - " // calculate A sharp matrix\n", - " matrix A_sharp(matrix A) {\n", - " matrix[rows(A) * rows(A), cols(A) * cols(A)] A_temp;\n", - " matrix[rows(A),cols(A)] I; // identity matrix\n", - " I = diag_matrix(rep_vector(1,rows(A)));\n", - " A_temp = kronecker_prod(A,I) + kronecker_prod(I,A);\n", - " return(A_temp);\n", - " }\n", - " \n", " // solve SDE\n", " matrix cov_drift(matrix A, matrix Q, real ts) {\n", " matrix[rows(A) * rows(A), cols(A) * cols(A)] A_sharp_temp;\n", - " matrix[rows(A) * rows(A), cols(A) * cols(A)] I; // identity matrix\n", " vector[rows(Q)*cols(Q)] row_Q;\n", " vector[rows(A)*cols(A)] irow_vec;\n", " matrix[rows(A),cols(A)] irow_mat;\n", - " I = diag_matrix(rep_vector(1,rows(A_sharp_temp)));\n", - " A_sharp_temp = A_sharp(A);\n", + " A_sharp_temp = kronecker_sum(A, A);\n", " // row operation takes elements of a matrix rowwise and puts them into a column vector\n", " for (i in 1:rows(Q))\n", " for (j in 1:cols(Q)) {\n", " row_Q[i + (j-1)*rows(Q)] = Q[j,i];\n", " }\n", - " irow_vec = inverse(A_sharp_temp) * (matrix_exp(A_sharp_temp * ts) - I) * row_Q;\n", + " irow_vec = (A_sharp_temp \\ add_diag(matrix_exp(A_sharp_temp * ts), -1)) * row_Q;\n", " // irow takes elements of a column vector and puts them in a matrix rowwise\n", " {\n", " int row_size = rows(A);\n", @@ -163,7 +145,7 @@ coev_make_stancode <- function(data, variables, id, tree, " irow_mat[row_ticker,col_ticker] = irow_vec[i];\n", " }\n", " }\n", - " return(irow_mat);\n", + " return irow_mat;\n", " }\n", " \n", " // return number of matches of y in vector x\n", @@ -321,7 +303,6 @@ coev_make_stancode <- function(data, variables, id, tree, "transformed parameters{\n", " matrix[N_seg,J] eta;\n", " matrix[J,J] Q; // drift matrix\n", - " matrix[J,J] I; // identity matrix\n", " matrix[J,J] A; // selection matrix\n", " vector[J*J - J] Q_offdiag = rep_vector(0.0, J*J - J);\n" ) @@ -384,8 +365,6 @@ coev_make_stancode <- function(data, variables, id, tree, " }\n", " for (j in 1:J) Q[j,j] = Q_diag[j];\n", " }\n", - " // identity matrix\n", - " I = diag_matrix(rep_vector(1,J));\n", " // setting ancestral states and placeholders\n", " for (j in 1:J) {\n", " eta[node_seq[1],j] = eta_anc[j];\n", @@ -396,13 +375,13 @@ coev_make_stancode <- function(data, variables, id, tree, " matrix[J,J] A_delta; // amount of deterministic change (selection)\n", " matrix[J,J] VCV; // variance-covariance matrix of stochastic change (drift)\n", " vector[J] drift_seg; // accumulated drift over the segment\n", - " A_delta = A_dt(A, ts[i]);\n", + " A_delta = matrix_exp(A * ts[i]);\n", " VCV = cov_drift(A, Q, ts[i]);\n", " drift_seg = cholesky_decompose(VCV) * to_vector( z_drift[i-1,] );\n", " // if not a tip, add the drift parameter\n", " if (tip[i] == 0) {\n", " eta[node_seq[i],] = to_row_vector(\n", - " A_delta * to_vector(eta[parent[i],]) + (inverse(A) * (A_delta - I) * b) + drift_seg\n", + " A_delta * to_vector(eta[parent[i],]) + ((A \\ add_diag(A_delta, -1)) * b) + drift_seg\n", " );\n", " drift_tips[node_seq[i],] = to_row_vector(rep_vector(-99, J));\n", " sigma_tips[node_seq[i],] = to_row_vector(rep_vector(-99, J));\n", @@ -410,7 +389,7 @@ coev_make_stancode <- function(data, variables, id, tree, " // if is a tip, omit, we'll deal with it in the model block;\n", " else {\n", " eta[node_seq[i],] = to_row_vector(\n", - " A_delta * to_vector(eta[parent[i],]) + (inverse(A) * (A_delta - I) * b)\n", + " A_delta * to_vector(eta[parent[i],]) + ((A \\ add_diag(A_delta, -1)) * b)\n", " );\n", " drift_tips[node_seq[i],] = to_row_vector(drift_seg);\n", " sigma_tips[node_seq[i],] = to_row_vector(diagonal(Q));\n", From b1c67148029fb17c99fd39bb9e048effeeaad1cc Mon Sep 17 00:00:00 2001 From: Scott Claessens Date: Wed, 7 Aug 2024 15:13:19 +0100 Subject: [PATCH 02/12] Implement kronecker_sum_self() Stan function --- R/coev_make_stancode.R | 28 ++++++++++++++++++---------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/R/coev_make_stancode.R b/R/coev_make_stancode.R index 4575bab..49d970a 100644 --- a/R/coev_make_stancode.R +++ b/R/coev_make_stancode.R @@ -101,21 +101,29 @@ coev_make_stancode <- function(data, variables, id, tree, # write functions block sc_functions <- paste0( "functions {\n", - " // returns the Kronecker sum of two square matrices with same dimensions\n", - " matrix kronecker_sum(matrix A, matrix B) {\n", + " // returns the Kronecker sum of a square matrix with itself\n", + " matrix kronecker_sum_self(matrix A) {\n", " int n = rows(A);\n", - " matrix[n*n, n*n] C;\n", + " matrix[n*n, n*n] result = rep_matrix(0, n*n, n*n);\n", + " // fill the diagonal blocks\n", + " for (i in 1:n) {\n", + " int start_row = (i - 1) * n + 1;\n", + " int end_row = i * n;\n", + " result[start_row:end_row, start_row:end_row] = add_diag(A, 1) * A[i, i];\n", + " }\n", + " // fill the off-diagonal blocks\n", " for (i in 1:n) {\n", " for (j in 1:n) {\n", - " for (k in 1:n) {\n", - " for (l in 1:n) {\n", - " C[(i-1)*n + k, (j-1)*n + l] = A[i,j] * (i == j) + B[k,l] * (k == l);\n", - " C[(i-1)*n + k, (j-1)*n + l] = A[i,j] * (k == l) + B[k,l] * (i == j);\n", - " }\n", + " if (i != j) {\n", + " int start_row = (i - 1) * n + 1;\n", + " int end_row = i * n;\n", + " int start_col = (j - 1) * n + 1;\n", + " int end_col = j * n;\n", + " result[start_row:end_row, start_col:end_col] = A[i, j] * identity_matrix(n);\n", " }\n", " }\n", " }\n", - " return C;\n", + " return result;\n", " }\n", " \n", " // solve SDE\n", @@ -124,7 +132,7 @@ coev_make_stancode <- function(data, variables, id, tree, " vector[rows(Q)*cols(Q)] row_Q;\n", " vector[rows(A)*cols(A)] irow_vec;\n", " matrix[rows(A),cols(A)] irow_mat;\n", - " A_sharp_temp = kronecker_sum(A, A);\n", + " A_sharp_temp = kronecker_sum_self(A);\n", " // row operation takes elements of a matrix rowwise and puts them into a column vector\n", " for (i in 1:rows(Q))\n", " for (j in 1:cols(Q)) {\n", From 18cf1e6d07a985f24cb4f0804f270885048e5c85 Mon Sep 17 00:00:00 2001 From: Scott Claessens Date: Wed, 7 Aug 2024 15:49:30 +0100 Subject: [PATCH 03/12] Revert "Implement kronecker_sum_self() Stan function" This reverts commit b1c67148029fb17c99fd39bb9e048effeeaad1cc. --- R/coev_make_stancode.R | 28 ++++++++++------------------ 1 file changed, 10 insertions(+), 18 deletions(-) diff --git a/R/coev_make_stancode.R b/R/coev_make_stancode.R index 49d970a..4575bab 100644 --- a/R/coev_make_stancode.R +++ b/R/coev_make_stancode.R @@ -101,29 +101,21 @@ coev_make_stancode <- function(data, variables, id, tree, # write functions block sc_functions <- paste0( "functions {\n", - " // returns the Kronecker sum of a square matrix with itself\n", - " matrix kronecker_sum_self(matrix A) {\n", + " // returns the Kronecker sum of two square matrices with same dimensions\n", + " matrix kronecker_sum(matrix A, matrix B) {\n", " int n = rows(A);\n", - " matrix[n*n, n*n] result = rep_matrix(0, n*n, n*n);\n", - " // fill the diagonal blocks\n", - " for (i in 1:n) {\n", - " int start_row = (i - 1) * n + 1;\n", - " int end_row = i * n;\n", - " result[start_row:end_row, start_row:end_row] = add_diag(A, 1) * A[i, i];\n", - " }\n", - " // fill the off-diagonal blocks\n", + " matrix[n*n, n*n] C;\n", " for (i in 1:n) {\n", " for (j in 1:n) {\n", - " if (i != j) {\n", - " int start_row = (i - 1) * n + 1;\n", - " int end_row = i * n;\n", - " int start_col = (j - 1) * n + 1;\n", - " int end_col = j * n;\n", - " result[start_row:end_row, start_col:end_col] = A[i, j] * identity_matrix(n);\n", + " for (k in 1:n) {\n", + " for (l in 1:n) {\n", + " C[(i-1)*n + k, (j-1)*n + l] = A[i,j] * (i == j) + B[k,l] * (k == l);\n", + " C[(i-1)*n + k, (j-1)*n + l] = A[i,j] * (k == l) + B[k,l] * (i == j);\n", + " }\n", " }\n", " }\n", " }\n", - " return result;\n", + " return C;\n", " }\n", " \n", " // solve SDE\n", @@ -132,7 +124,7 @@ coev_make_stancode <- function(data, variables, id, tree, " vector[rows(Q)*cols(Q)] row_Q;\n", " vector[rows(A)*cols(A)] irow_vec;\n", " matrix[rows(A),cols(A)] irow_mat;\n", - " A_sharp_temp = kronecker_sum_self(A);\n", + " A_sharp_temp = kronecker_sum(A, A);\n", " // row operation takes elements of a matrix rowwise and puts them into a column vector\n", " for (i in 1:rows(Q))\n", " for (j in 1:cols(Q)) {\n", From caf14a303b85ac93858de7c25585dceeae458668 Mon Sep 17 00:00:00 2001 From: Scott Claessens Date: Thu, 8 Aug 2024 14:45:42 +0100 Subject: [PATCH 04/12] Put constraints on data in Stan code --- R/coev_make_stancode.R | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/R/coev_make_stancode.R b/R/coev_make_stancode.R index 4575bab..c578bf8 100644 --- a/R/coev_make_stancode.R +++ b/R/coev_make_stancode.R @@ -174,19 +174,19 @@ coev_make_stancode <- function(data, variables, id, tree, # write data block sc_data <- paste0( "data{\n", - " int N_tips; // number of tips\n", - " int N_obs; // number of observations\n", - " int J; // number of response traits\n", - " int N_seg; // total number of segments in the tree\n", - " array[N_seg] int node_seq; // index of tree nodes\n", - " array[N_seg] int parent; // index of the parent node of each descendent\n", + " int N_tips; // number of tips\n", + " int N_obs; // number of observations\n", + " int J; // number of response traits\n", + " int N_seg; // total number of segments in the tree\n", + " array[N_seg] int node_seq; // index of tree nodes\n", + " array[N_seg] int parent; // index of the parent node of each descendent\n", " array[N_seg] real ts; // time since parent\n", - " array[N_seg] int tip; // indicator of whether a given segment ends in a tip\n", - " array[J,J] int effects_mat; // which effects should be estimated?\n", - " int num_effects; // number of effects being estimated\n", + " array[N_seg] int tip; // indicator of whether a given segment ends in a tip\n", + " array[J,J] int effects_mat; // which effects should be estimated?\n", + " int num_effects; // number of effects being estimated\n", " matrix[N_obs,J] y; // observed data\n", " matrix[N_obs,J] miss; // are data points missing?\n", - " array[N_obs] int tip_id; // index between 1 and N_tips that gives the group id\n" + " array[N_obs] int tip_id; // index between 1 and N_tips that gives the group id\n" ) # add distance matrix if user has defined one if (!is.null(dist_mat)) { @@ -200,7 +200,7 @@ coev_make_stancode <- function(data, variables, id, tree, sc_data <- paste0( sc_data, - " int prior_only; // should the likelihood be ignored?\n}" + " int prior_only; // should the likelihood be ignored?\n}" ) # write transformed data block sc_transformed_data <- "transformed data{\n" From 6025d6f75c416b6f60ba03e0d9cb98c66658d26c Mon Sep 17 00:00:00 2001 From: Scott Claessens Date: Thu, 8 Aug 2024 15:48:23 +0100 Subject: [PATCH 05/12] Move and reshape some Stan parameters --- R/coev_make_stancode.R | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/R/coev_make_stancode.R b/R/coev_make_stancode.R index c578bf8..3bc6311 100644 --- a/R/coev_make_stancode.R +++ b/R/coev_make_stancode.R @@ -203,7 +203,11 @@ coev_make_stancode <- function(data, variables, id, tree, " int prior_only; // should the likelihood be ignored?\n}" ) # write transformed data block - sc_transformed_data <- "transformed data{\n" + sc_transformed_data <- + paste0( + "transformed data{\n", + " vector[J*J - J] Q_offdiag = rep_vector(0.0, J*J - J);\n" + ) for (j in 1:length(distributions)) { sc_transformed_data <- paste0( sc_transformed_data, @@ -246,7 +250,7 @@ coev_make_stancode <- function(data, variables, id, tree, " vector[J] Q_diag; // self-drift terms\n", " vector[J] b; // SDE intercepts\n", " vector[J] eta_anc; // ancestral states\n", - " matrix[N_seg - 1,J] z_drift; // stochastic drift, unscaled and uncorrelated\n" + " array[N_seg - 1] vector[J] z_drift; // stochastic drift, unscaled and uncorrelated\n" ) for (i in 1:length(distributions)) { # add cut points for ordinal_logistic distributions @@ -283,7 +287,7 @@ coev_make_stancode <- function(data, variables, id, tree, if (!is.null(dist_mat)) { sc_parameters <- paste0( sc_parameters, - " matrix[N_tips,J] dist_z; // spatial covariance random effects, unscaled and uncorrelated\n", + " array[J] vector[N_tips] dist_z; // spatial covariance random effects, unscaled and uncorrelated\n", " vector[J] rho_dist; // how quickly does covariance decline with distance\n", " vector[J] sigma_dist; // maximum covariance due to spatial location\n" ) @@ -303,8 +307,7 @@ coev_make_stancode <- function(data, variables, id, tree, "transformed parameters{\n", " matrix[N_seg,J] eta;\n", " matrix[J,J] Q; // drift matrix\n", - " matrix[J,J] A; // selection matrix\n", - " vector[J*J - J] Q_offdiag = rep_vector(0.0, J*J - J);\n" + " matrix[J,J] A; // selection matrix\n" ) # add distance random effects if distance matrix specified by user if (!is.null(dist_mat)) { @@ -412,7 +415,7 @@ coev_make_stancode <- function(data, variables, id, tree, " for ( q in 1:N_tips )\n", " dist_cov[q,q] = sigma_dist[j] + 0.01;\n", " L_dist_cov = cholesky_decompose(dist_cov);\n", - " dist_v[,j] = L_dist_cov * dist_z[,j];\n", + " dist_v[,j] = L_dist_cov * dist_z[j];\n", " }\n" ) } @@ -422,7 +425,7 @@ coev_make_stancode <- function(data, variables, id, tree, "model{\n", " b ~ ", priors$b, ";\n", " eta_anc ~ ", priors$eta_anc, ";\n", - " to_vector(z_drift) ~ std_normal();\n", + " for (i in 2:N_seg) z_drift[i] ~ std_normal();\n", " A_offdiag ~ ", priors$A_offdiag, ";\n", " A_diag ~ ", priors$A_diag, ";\n", " Q_diag ~ ", priors$Q_diag, ";\n" @@ -461,7 +464,7 @@ coev_make_stancode <- function(data, variables, id, tree, if (!is.null(dist_mat)) { sc_model <- paste0( sc_model, - " to_vector(dist_z) ~ std_normal();\n", + " for (j in 1:J) dist_z[j] ~ std_normal();\n", " sigma_dist ~ ", priors$sigma_dist, ";\n", " rho_dist ~ ", priors$rho_dist, ";\n" ) From aa10a4b63e15b7b4bc92726b163499d8970449ff Mon Sep 17 00:00:00 2001 From: Scott Claessens Date: Thu, 8 Aug 2024 17:26:00 +0100 Subject: [PATCH 06/12] Revert "Move and reshape some Stan parameters" This reverts commit 6025d6f75c416b6f60ba03e0d9cb98c66658d26c. --- R/coev_make_stancode.R | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/R/coev_make_stancode.R b/R/coev_make_stancode.R index 3bc6311..c578bf8 100644 --- a/R/coev_make_stancode.R +++ b/R/coev_make_stancode.R @@ -203,11 +203,7 @@ coev_make_stancode <- function(data, variables, id, tree, " int prior_only; // should the likelihood be ignored?\n}" ) # write transformed data block - sc_transformed_data <- - paste0( - "transformed data{\n", - " vector[J*J - J] Q_offdiag = rep_vector(0.0, J*J - J);\n" - ) + sc_transformed_data <- "transformed data{\n" for (j in 1:length(distributions)) { sc_transformed_data <- paste0( sc_transformed_data, @@ -250,7 +246,7 @@ coev_make_stancode <- function(data, variables, id, tree, " vector[J] Q_diag; // self-drift terms\n", " vector[J] b; // SDE intercepts\n", " vector[J] eta_anc; // ancestral states\n", - " array[N_seg - 1] vector[J] z_drift; // stochastic drift, unscaled and uncorrelated\n" + " matrix[N_seg - 1,J] z_drift; // stochastic drift, unscaled and uncorrelated\n" ) for (i in 1:length(distributions)) { # add cut points for ordinal_logistic distributions @@ -287,7 +283,7 @@ coev_make_stancode <- function(data, variables, id, tree, if (!is.null(dist_mat)) { sc_parameters <- paste0( sc_parameters, - " array[J] vector[N_tips] dist_z; // spatial covariance random effects, unscaled and uncorrelated\n", + " matrix[N_tips,J] dist_z; // spatial covariance random effects, unscaled and uncorrelated\n", " vector[J] rho_dist; // how quickly does covariance decline with distance\n", " vector[J] sigma_dist; // maximum covariance due to spatial location\n" ) @@ -307,7 +303,8 @@ coev_make_stancode <- function(data, variables, id, tree, "transformed parameters{\n", " matrix[N_seg,J] eta;\n", " matrix[J,J] Q; // drift matrix\n", - " matrix[J,J] A; // selection matrix\n" + " matrix[J,J] A; // selection matrix\n", + " vector[J*J - J] Q_offdiag = rep_vector(0.0, J*J - J);\n" ) # add distance random effects if distance matrix specified by user if (!is.null(dist_mat)) { @@ -415,7 +412,7 @@ coev_make_stancode <- function(data, variables, id, tree, " for ( q in 1:N_tips )\n", " dist_cov[q,q] = sigma_dist[j] + 0.01;\n", " L_dist_cov = cholesky_decompose(dist_cov);\n", - " dist_v[,j] = L_dist_cov * dist_z[j];\n", + " dist_v[,j] = L_dist_cov * dist_z[,j];\n", " }\n" ) } @@ -425,7 +422,7 @@ coev_make_stancode <- function(data, variables, id, tree, "model{\n", " b ~ ", priors$b, ";\n", " eta_anc ~ ", priors$eta_anc, ";\n", - " for (i in 2:N_seg) z_drift[i] ~ std_normal();\n", + " to_vector(z_drift) ~ std_normal();\n", " A_offdiag ~ ", priors$A_offdiag, ";\n", " A_diag ~ ", priors$A_diag, ";\n", " Q_diag ~ ", priors$Q_diag, ";\n" @@ -464,7 +461,7 @@ coev_make_stancode <- function(data, variables, id, tree, if (!is.null(dist_mat)) { sc_model <- paste0( sc_model, - " for (j in 1:J) dist_z[j] ~ std_normal();\n", + " to_vector(dist_z) ~ std_normal();\n", " sigma_dist ~ ", priors$sigma_dist, ";\n", " rho_dist ~ ", priors$rho_dist, ";\n" ) From 37ca58e4c7581c6f2fb95122d01444eec3c3d1f0 Mon Sep 17 00:00:00 2001 From: Scott Claessens Date: Fri, 9 Aug 2024 11:42:35 +0100 Subject: [PATCH 07/12] Implement asymptotic covariance function and other optimisations --- DESCRIPTION | 2 +- R/coev_make_stancode.R | 269 +++++++++++++++++++++-------------------- 2 files changed, 136 insertions(+), 135 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2826b22..d52a3c7 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -16,7 +16,7 @@ URL: https://github.com/ScottClaessens/coevolve BugReports: https://github.com/ScottClaessens/coevolve/issues Encoding: UTF-8 Roxygen: list(markdown = TRUE) -RoxygenNote: 7.2.3 +RoxygenNote: 7.3.2 Suggests: knitr, rmarkdown, diff --git a/R/coev_make_stancode.R b/R/coev_make_stancode.R index c578bf8..4232237 100644 --- a/R/coev_make_stancode.R +++ b/R/coev_make_stancode.R @@ -101,51 +101,73 @@ coev_make_stancode <- function(data, variables, id, tree, # write functions block sc_functions <- paste0( "functions {\n", - " // returns the Kronecker sum of two square matrices with same dimensions\n", - " matrix kronecker_sum(matrix A, matrix B) {\n", - " int n = rows(A);\n", - " matrix[n*n, n*n] C;\n", - " for (i in 1:n) {\n", - " for (j in 1:n) {\n", - " for (k in 1:n) {\n", - " for (l in 1:n) {\n", - " C[(i-1)*n + k, (j-1)*n + l] = A[i,j] * (i == j) + B[k,l] * (k == l);\n", - " C[(i-1)*n + k, (j-1)*n + l] = A[i,j] * (k == l) + B[k,l] * (i == j);\n", + " // Charles Driver's optimized way of solving for the asymptotic Q matrix\n", + " matrix ksolve (matrix A, matrix Q) {\n", + " int d = rows(A);\n", + " int d2 = (d * d - d) %/% 2;\n", + " matrix [d + d2, d + d2] O;\n", + " vector [d + d2] triQ;\n", + " matrix[d,d] AQ;\n", + " int z = 0; // z is row of output\n", + " for (j in 1:d) { // for column reference of solution vector\n", + " for (i in 1:j) { // and row reference...\n", + " if (j >= i) { // if i and j denote a covariance parameter (from upper tri)\n", + " int y = 0; // start new output row\n", + " z += 1; // shift current output row down\n", + " for (ci in 1:d) { // for columns and\n", + " for (ri in 1:d) { // rows of solution\n", + " if (ci >= ri) { // when in upper tri (inc diag)\n", + " y += 1; // move to next column of output\n", + " if (i == j) { // if output row is for a diagonal element\n", + " if (ri == i) O[z, y] = 2 * A[ri, ci];\n", + " if (ci == i) O[z, y] = 2 * A[ci, ri];\n", + " }\n", + " if (i != j) { // if output row is not for a diagonal element\n", + " //if column of output matches row of output, sum both A diags\n", + " if (y == z) O[z, y] = A[ri, ri] + A[ci, ci];\n", + " if (y != z) { // otherwise...\n", + " // if solution element we refer to is related to output row...\n", + " if (ci == ri) { // if solution element is a variance\n", + " // if variance of solution corresponds to row of our output\n", + " if (ci == i) O[z, y] = A[j, ci];\n", + " // if variance of solution corresponds to col of our output\n", + " if (ci == j) O[z, y] = A[i, ci];\n", + " }\n", + " //if solution element is a related covariance\n", + " if (ci != ri && (ri == i || ri == j || ci == i || ci == j )) {\n", + " // for row 1,2 / 2,1 of output, if solution row ri 1 (match)\n", + " // and column ci 3, we need A[2,3]\n", + " if (ri == i) O[z, y] = A[j, ci];\n", + " if (ri == j) O[z, y] = A[i, ci];\n", + " if (ci == i) O[z, y] = A[j, ri];\n", + " if (ci == j) O[z, y] = A[i, ri];\n", + " }\n", + " }\n", + " }\n", + " if (is_nan(O[z, y])) O[z, y] = 0;\n", + " }\n", + " }\n", " }\n", " }\n", " }\n", " }\n", - " return C;\n", - " }\n", - " \n", - " // solve SDE\n", - " matrix cov_drift(matrix A, matrix Q, real ts) {\n", - " matrix[rows(A) * rows(A), cols(A) * cols(A)] A_sharp_temp;\n", - " vector[rows(Q)*cols(Q)] row_Q;\n", - " vector[rows(A)*cols(A)] irow_vec;\n", - " matrix[rows(A),cols(A)] irow_mat;\n", - " A_sharp_temp = kronecker_sum(A, A);\n", - " // row operation takes elements of a matrix rowwise and puts them into a column vector\n", - " for (i in 1:rows(Q))\n", - " for (j in 1:cols(Q)) {\n", - " row_Q[i + (j-1)*rows(Q)] = Q[j,i];\n", + " z = 0; // get upper tri of Q\n", + " for (j in 1:d) {\n", + " for (i in 1:j) {\n", + " z += 1;\n", + " triQ[z] = Q[i, j];\n", " }\n", - " irow_vec = (A_sharp_temp \\ add_diag(matrix_exp(A_sharp_temp * ts), -1)) * row_Q;\n", - " // irow takes elements of a column vector and puts them in a matrix rowwise\n", - " {\n", - " int row_size = rows(A);\n", - " int row_ticker = 1;\n", - " int col_ticker = 0;\n", - " for (i in 1:num_elements(irow_vec)) {\n", - " col_ticker += 1;\n", - " if (col_ticker > row_size) {\n", - " row_ticker += 1;\n", - " col_ticker = 1;\n", - " }\n", - " irow_mat[row_ticker,col_ticker] = irow_vec[i];\n", + " }\n", + " triQ = -O \\ triQ; // get upper tri of asymQ\n", + " z = 0; // put upper tri of asymQ into matrix\n", + " for (j in 1:d) {\n", + " for (i in 1:j) {\n", + " z += 1;\n", + " AQ[i, j] = triQ[z];\n", + " if (i != j) AQ[j, i] = triQ[z];\n", " }\n", " }\n", - " return irow_mat;\n", + " return AQ;\n", " }\n", " \n", " // return number of matches of y in vector x\n", @@ -246,7 +268,7 @@ coev_make_stancode <- function(data, variables, id, tree, " vector[J] Q_diag; // self-drift terms\n", " vector[J] b; // SDE intercepts\n", " vector[J] eta_anc; // ancestral states\n", - " matrix[N_seg - 1,J] z_drift; // stochastic drift, unscaled and uncorrelated\n" + " array[N_seg - 1] vector[J] z_drift; // stochastic drift, unscaled and uncorrelated\n" ) for (i in 1:length(distributions)) { # add cut points for ordinal_logistic distributions @@ -301,10 +323,12 @@ coev_make_stancode <- function(data, variables, id, tree, # write transformed parameters block sc_transformed_parameters <- paste0( "transformed parameters{\n", - " matrix[N_seg,J] eta;\n", - " matrix[J,J] Q; // drift matrix\n", - " matrix[J,J] A; // selection matrix\n", - " vector[J*J - J] Q_offdiag = rep_vector(0.0, J*J - J);\n" + " array[N_seg] vector[J] eta;\n", + " matrix[J,J] A = diag_matrix(A_diag); // selection matrix\n", + " matrix[J,J] Q = diag_matrix(Q_diag); // drift matrix\n", + " matrix[J,J] Q_inf; // asymptotic covariance matrix\n", + " array[N_seg] vector[J] drift_tips; // terminal drift parameters\n", + " array[N_seg] vector[J] sigma_tips; // terminal drift parameters\n" ) # add distance random effects if distance matrix specified by user if (!is.null(dist_mat)) { @@ -324,75 +348,52 @@ coev_make_stancode <- function(data, variables, id, tree, } sc_transformed_parameters <- paste0( sc_transformed_parameters, - " matrix[N_seg,J] drift_tips; // terminal drift parameters\n", - " matrix[N_seg,J] sigma_tips; // terminal drift parameters\n", - " // fill A matrix //////////\n", + " // fill off diagonal of A matrix\n", " {\n", " int ticker = 1;\n", - " // fill upper triangle of matrix\n", - " for (i in 1:(J-1)) {\n", - " for (j in (i+1):J) {\n", - " if (effects_mat[i,j] == 1) {\n", - " A[i,j] = A_offdiag[ticker];\n", - " ticker += 1;\n", - " } else if (effects_mat[i,j] == 0) {\n", - " A[i,j] = 0;\n", - " }\n", - " }\n", - " }\n", - " // fill lower triangle of matrix\n", - " for (i in 1:(J-1)) {\n", - " for (j in (i+1):J) {\n", - " if (effects_mat[j,i] == 1) {\n", - " A[j,i] = A_offdiag[ticker];\n", - " ticker += 1;\n", - " } else if (effects_mat[j,i] == 0) {\n", - " A[j,i] = 0;\n", + " for (i in 1:J) {\n", + " for (j in 1:J) {\n", + " if (i != j) {\n", + " if (effects_mat[i,j] == 1) {\n", + " A[i,j] = A_offdiag[ticker];\n", + " ticker += 1;\n", + " } else if (effects_mat[i,j] == 0) {\n", + " A[i,j] = 0;\n", + " }\n", " }\n", " }\n", " }\n", - " // fill diag of matrix\n", - " for (j in 1:J) A[j,j] = A_diag[j];\n", - " }\n", - " // fill Q matrix //////////\n", - " {\n", - " int ticker = 1;\n", - " for (i in 1:(J-1))\n", - " for (j in (i+1):J) {\n", - " Q[i,j] = Q_offdiag[ticker];\n", - " Q[j,i] = Q[i,j]; // symmetry of covariance\n", - " ticker += 1;\n", - " }\n", - " for (j in 1:J) Q[j,j] = Q_diag[j];\n", " }\n", + " // calculate asymptotic covariance\n", + " Q_inf = ksolve(A, Q);\n", " // setting ancestral states and placeholders\n", " for (j in 1:J) {\n", - " eta[node_seq[1],j] = eta_anc[j];\n", - " drift_tips[node_seq[1],j] = -99;\n", - " sigma_tips[node_seq[1],j] = -99;\n", + " eta[node_seq[1]][j] = eta_anc[j];\n", + " drift_tips[node_seq[1]][j] = -99;\n", + " sigma_tips[node_seq[1]][j] = -99;\n", " }\n", " for (i in 2:N_seg) {\n", " matrix[J,J] A_delta; // amount of deterministic change (selection)\n", " matrix[J,J] VCV; // variance-covariance matrix of stochastic change (drift)\n", " vector[J] drift_seg; // accumulated drift over the segment\n", " A_delta = matrix_exp(A * ts[i]);\n", - " VCV = cov_drift(A, Q, ts[i]);\n", - " drift_seg = cholesky_decompose(VCV) * to_vector( z_drift[i-1,] );\n", + " VCV = Q_inf - quad_form_sym(Q_inf, A_delta');\n", + " drift_seg = cholesky_decompose(VCV) * z_drift[i-1];\n", " // if not a tip, add the drift parameter\n", " if (tip[i] == 0) {\n", - " eta[node_seq[i],] = to_row_vector(\n", - " A_delta * to_vector(eta[parent[i],]) + ((A \\ add_diag(A_delta, -1)) * b) + drift_seg\n", + " eta[node_seq[i]] = to_vector(\n", + " A_delta * eta[parent[i]] + ((A \\ add_diag(A_delta, -1)) * b) + drift_seg\n", " );\n", - " drift_tips[node_seq[i],] = to_row_vector(rep_vector(-99, J));\n", - " sigma_tips[node_seq[i],] = to_row_vector(rep_vector(-99, J));\n", + " drift_tips[node_seq[i]] = rep_vector(-99, J);\n", + " sigma_tips[node_seq[i]] = rep_vector(-99, J);\n", " }\n", " // if is a tip, omit, we'll deal with it in the model block;\n", " else {\n", - " eta[node_seq[i],] = to_row_vector(\n", - " A_delta * to_vector(eta[parent[i],]) + ((A \\ add_diag(A_delta, -1)) * b)\n", + " eta[node_seq[i]] = to_vector(\n", + " A_delta * eta[parent[i]] + ((A \\ add_diag(A_delta, -1)) * b)\n", " );\n", - " drift_tips[node_seq[i],] = to_row_vector(drift_seg);\n", - " sigma_tips[node_seq[i],] = to_row_vector(diagonal(Q));\n", + " drift_tips[node_seq[i]] = drift_seg;\n", + " sigma_tips[node_seq[i]] = diagonal(Q);\n", " }\n", " }\n" ) @@ -422,7 +423,7 @@ coev_make_stancode <- function(data, variables, id, tree, "model{\n", " b ~ ", priors$b, ";\n", " eta_anc ~ ", priors$eta_anc, ";\n", - " to_vector(z_drift) ~ std_normal();\n", + " for (i in 1:(N_seg - 1)) z_drift[i] ~ std_normal();\n", " A_offdiag ~ ", priors$A_offdiag, ";\n", " A_diag ~ ", priors$A_diag, ";\n", " Q_diag ~ ", priors$Q_diag, ";\n" @@ -487,64 +488,64 @@ coev_make_stancode <- function(data, variables, id, tree, sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "bernoulli_logit(eta[tip_id[i],", j, "]", + "bernoulli_logit(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "]);\n" + " + drift_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "ordered_logistic") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "ordered_logistic(eta[tip_id[i],", j, "]", + "ordered_logistic(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "], c", j, ");\n" + " + drift_tips[tip_id[i]][", j, "], c", j, ");\n" ) } else if (distributions[j] == "poisson_softplus") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "poisson(mean(obs", j, ") * log1p_exp(eta[tip_id[i],", j, "]", + "poisson(mean(obs", j, ") * log1p_exp(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "]));\n" + " + drift_tips[tip_id[i]][", j, "]));\n" ) } else if (distributions[j] == "normal") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", - "normal(eta[tip_id[i],", j, "]", + "normal(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - ", sigma_tips[tip_id[i],", j, "]);\n" + ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "student_t") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", - "student_t(nu", j, ", eta[tip_id[i],", j, "]", + "student_t(nu", j, ", eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - ", sigma_tips[tip_id[i],", j, "]);\n" + ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "lognormal") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", - "lognormal(eta[tip_id[i],", j, "]", + "lognormal(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - ", sigma_tips[tip_id[i],", j, "]);\n" + ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "negative_binomial_softplus") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "neg_binomial_2(mean(obs", j, ") * log1p_exp(eta[tip_id[i],", j, "]", + "neg_binomial_2(mean(obs", j, ") * log1p_exp(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "]), phi", j, ");\n" + " + drift_tips[tip_id[i]][", j, "]), phi", j, ");\n" ) } } @@ -582,101 +583,101 @@ coev_make_stancode <- function(data, variables, id, tree, sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "bernoulli_logit_lpmf(to_int(y[i,", j, "]) | eta[tip_id[i],", j, "]", + "bernoulli_logit_lpmf(to_int(y[i,", j, "]) | eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "]);\n", + " + drift_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "bernoulli_logit_rng(eta[tip_id[i],", j, "]", + "bernoulli_logit_rng(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "]);\n" + " + drift_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "ordered_logistic") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "ordered_logistic_lpmf(to_int(y[i,", j, "]) | eta[tip_id[i],", j, "]", + "ordered_logistic_lpmf(to_int(y[i,", j, "]) | eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "], c", j, ");\n", + " + drift_tips[tip_id[i]][", j, "], c", j, ");\n", " yrep_temp[i,", j, "] = ", - "ordered_logistic_rng(eta[tip_id[i],", j, "]", + "ordered_logistic_rng(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "], c", j, ");\n" + " + drift_tips[tip_id[i]][", j, "], c", j, ");\n" ) } else if (distributions[j] == "poisson_softplus") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", "poisson_lpmf(to_int(y[i,", j, "]) | mean(obs", j, - ") * log1p_exp(eta[tip_id[i],", j, "]", + ") * log1p_exp(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "]));\n", + " + drift_tips[tip_id[i]][", j, "]));\n", " yrep_temp[i,", j, "] = ", - "poisson_rng(mean(obs", j, ") * log1p_exp(eta[tip_id[i],", j, "]", + "poisson_rng(mean(obs", j, ") * log1p_exp(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "]));\n" + " + drift_tips[tip_id[i]][", j, "]));\n" ) } else if (distributions[j] == "normal") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "normal_lpdf(y[i,", j, "] | eta[tip_id[i],", j, "]", + "normal_lpdf(y[i,", j, "] | eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - ", sigma_tips[tip_id[i],", j, "]);\n", + ", sigma_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "normal_rng(eta[tip_id[i],", j, "]", + "normal_rng(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - ", sigma_tips[tip_id[i],", j, "]);\n" + ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "student_t") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "student_t_lpdf(y[i,", j, "] | nu", j, ", eta[tip_id[i],", j, "]", + "student_t_lpdf(y[i,", j, "] | nu", j, ", eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - ", sigma_tips[tip_id[i],", j, "]);\n", + ", sigma_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "student_t_rng(nu", j, ", eta[tip_id[i],", j, "]", + "student_t_rng(nu", j, ", eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - ", sigma_tips[tip_id[i],", j, "]);\n" + ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "lognormal") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "lognormal_lpdf(y[i,", j, "] | eta[tip_id[i],", j, "]", + "lognormal_lpdf(y[i,", j, "] | eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - ", sigma_tips[tip_id[i],", j, "]);\n", + ", sigma_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "lognormal_rng(eta[tip_id[i],", j, "]", + "lognormal_rng(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - ", sigma_tips[tip_id[i],", j, "]);\n" + ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "negative_binomial_softplus") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", "neg_binomial_2_lpmf(to_int(y[i,", j, "]) | mean(obs", j, - ") * log1p_exp(eta[tip_id[i],", j, "]", + ") * log1p_exp(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "]), phi", j, ");\n", + " + drift_tips[tip_id[i]][", j, "]), phi", j, ");\n", " yrep_temp[i,", j, "] = ", - "neg_binomial_2_rng(mean(obs", j, ") * log1p_exp(eta[tip_id[i],", j, "]", + "neg_binomial_2_rng(mean(obs", j, ") * log1p_exp(eta[tip_id[i]][", j, "]", ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), - " + drift_tips[tip_id[i],", j, "]), phi", j, ");\n" + " + drift_tips[tip_id[i]][", j, "]), phi", j, ");\n" ) } } From ed6b49e6fd9c2ec4ed9caa2ae270096e2b2cefe2 Mon Sep 17 00:00:00 2001 From: Scott Claessens Date: Fri, 9 Aug 2024 15:14:55 +0100 Subject: [PATCH 08/12] Reduce repetition and fix num_effects error in coev_make_stancode() --- R/coev_make_stancode.R | 114 ++++++++++++++++------------------------- 1 file changed, 43 insertions(+), 71 deletions(-) diff --git a/R/coev_make_stancode.R b/R/coev_make_stancode.R index 4232237..194b964 100644 --- a/R/coev_make_stancode.R +++ b/R/coev_make_stancode.R @@ -205,7 +205,7 @@ coev_make_stancode <- function(data, variables, id, tree, " array[N_seg] real ts; // time since parent\n", " array[N_seg] int tip; // indicator of whether a given segment ends in a tip\n", " array[J,J] int effects_mat; // which effects should be estimated?\n", - " int num_effects; // number of effects being estimated\n", + " int num_effects; // number of effects being estimated\n", " matrix[N_obs,J] y; // observed data\n", " matrix[N_obs,J] miss; // are data points missing?\n", " array[N_obs] int tip_id; // index between 1 and N_tips that gives the group id\n" @@ -483,68 +483,68 @@ coev_make_stancode <- function(data, variables, id, tree, " if (!prior_only) {\n", " for (i in 1:N_obs) {\n" ) + # save linear model + lmod <- paste0( + "eta[tip_id[i]][", j, "]", + ifelse( + !is.null(dist_mat), + paste0(" + dist_v[tip_id[i],", j, "]"), + "" + ), + ifelse( + any(duplicated(data[,id])), + paste0(" + group_v[tip_id[i],", j, "]"), + "" + ) + ) for (j in 1:length(distributions)) { if (distributions[j] == "bernoulli_logit") { sc_model <- paste0( sc_model, - " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "bernoulli_logit(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", + "bernoulli_logit(", lmod, " + drift_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "ordered_logistic") { sc_model <- paste0( sc_model, - " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "ordered_logistic(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", + "ordered_logistic(", lmod, " + drift_tips[tip_id[i]][", j, "], c", j, ");\n" ) } else if (distributions[j] == "poisson_softplus") { sc_model <- paste0( sc_model, - " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "poisson(mean(obs", j, ") * log1p_exp(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", + "poisson(mean(obs", j, ") * log1p_exp(", lmod, " + drift_tips[tip_id[i]][", j, "]));\n" ) } else if (distributions[j] == "normal") { sc_model <- paste0( sc_model, - " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", - "normal(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", + "normal(", lmod, ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "student_t") { sc_model <- paste0( sc_model, - " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", - "student_t(nu", j, ", eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", + "student_t(nu", j, ", ", lmod, ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "lognormal") { sc_model <- paste0( sc_model, - " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", - "lognormal(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", + "lognormal(", lmod, ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "negative_binomial_softplus") { sc_model <- paste0( sc_model, - " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "neg_binomial_2(mean(obs", j, ") * log1p_exp(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", + "neg_binomial_2(mean(obs", j, ") * log1p_exp(", lmod, " + drift_tips[tip_id[i]][", j, "]), phi", j, ");\n" ) } @@ -583,28 +583,20 @@ coev_make_stancode <- function(data, variables, id, tree, sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "bernoulli_logit_lpmf(to_int(y[i,", j, "]) | eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "bernoulli_logit_lpmf(to_int(y[i,", j, "]) | ", lmod, " + drift_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "bernoulli_logit_rng(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "bernoulli_logit_rng(", lmod, " + drift_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "ordered_logistic") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "ordered_logistic_lpmf(to_int(y[i,", j, "]) | eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "ordered_logistic_lpmf(to_int(y[i,", j, "]) | ", lmod, " + drift_tips[tip_id[i]][", j, "], c", j, ");\n", " yrep_temp[i,", j, "] = ", - "ordered_logistic_rng(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "ordered_logistic_rng(", lmod, " + drift_tips[tip_id[i]][", j, "], c", j, ");\n" ) } else if (distributions[j] == "poisson_softplus") { @@ -612,56 +604,40 @@ coev_make_stancode <- function(data, variables, id, tree, sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", "poisson_lpmf(to_int(y[i,", j, "]) | mean(obs", j, - ") * log1p_exp(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + ") * log1p_exp(", lmod, " + drift_tips[tip_id[i]][", j, "]));\n", " yrep_temp[i,", j, "] = ", - "poisson_rng(mean(obs", j, ") * log1p_exp(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "poisson_rng(mean(obs", j, ") * log1p_exp(", lmod, " + drift_tips[tip_id[i]][", j, "]));\n" ) } else if (distributions[j] == "normal") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "normal_lpdf(y[i,", j, "] | eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "normal_lpdf(y[i,", j, "] | ", lmod, ", sigma_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "normal_rng(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "normal_rng(", lmod, ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "student_t") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "student_t_lpdf(y[i,", j, "] | nu", j, ", eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "student_t_lpdf(y[i,", j, "] | nu", j, ", ", lmod, ", sigma_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "student_t_rng(nu", j, ", eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "student_t_rng(nu", j, ", ", lmod, ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "lognormal") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "lognormal_lpdf(y[i,", j, "] | eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "lognormal_lpdf(y[i,", j, "] | ", lmod, ", sigma_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "lognormal_rng(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "lognormal_rng(", lmod, ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "negative_binomial_softplus") { @@ -669,14 +645,10 @@ coev_make_stancode <- function(data, variables, id, tree, sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", "neg_binomial_2_lpmf(to_int(y[i,", j, "]) | mean(obs", j, - ") * log1p_exp(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + ") * log1p_exp(", lmod, " + drift_tips[tip_id[i]][", j, "]), phi", j, ");\n", " yrep_temp[i,", j, "] = ", - "neg_binomial_2_rng(mean(obs", j, ") * log1p_exp(eta[tip_id[i]][", j, "]", - ifelse(!is.null(dist_mat), paste0(" + dist_v[tip_id[i],", j, "]"), ""), - ifelse(any(duplicated(data[,id])), paste0(" + group_v[tip_id[i],", j, "]"), ""), + "neg_binomial_2_rng(mean(obs", j, ") * log1p_exp(", lmod, " + drift_tips[tip_id[i]][", j, "]), phi", j, ");\n" ) } From e78aadf4bd37bc99206472da3a4b25ae381949a7 Mon Sep 17 00:00:00 2001 From: Scott Claessens Date: Fri, 9 Aug 2024 15:31:58 +0100 Subject: [PATCH 09/12] Update example models --- .../testthat/fixtures/coevfit_example1-1.csv | 122 +++++++++--------- tests/testthat/fixtures/coevfit_example1.rds | Bin 104797 -> 103186 bytes .../testthat/fixtures/coevfit_example2-1.csv | 122 +++++++++--------- tests/testthat/fixtures/coevfit_example2.rds | Bin 105617 -> 103973 bytes .../testthat/fixtures/coevfit_example3-1.csv | 120 ++++++++--------- tests/testthat/fixtures/coevfit_example3.rds | Bin 99780 -> 98395 bytes .../testthat/fixtures/coevfit_example4-1.csv | 122 +++++++++--------- tests/testthat/fixtures/coevfit_example4.rds | Bin 100149 -> 99062 bytes .../testthat/fixtures/coevfit_example5-1.csv | 122 +++++++++--------- tests/testthat/fixtures/coevfit_example5.rds | Bin 57454 -> 57805 bytes .../testthat/fixtures/coevfit_example6-1.csv | 122 +++++++++--------- tests/testthat/fixtures/coevfit_example6.rds | Bin 57130 -> 57420 bytes .../testthat/fixtures/coevfit_example7-1.csv | 122 +++++++++--------- tests/testthat/fixtures/coevfit_example7.rds | Bin 104202 -> 102805 bytes 14 files changed, 426 insertions(+), 426 deletions(-) diff --git a/tests/testthat/fixtures/coevfit_example1-1.csv b/tests/testthat/fixtures/coevfit_example1-1.csv index 8a48b3c..416815a 100644 --- a/tests/testthat/fixtures/coevfit_example1-1.csv +++ b/tests/testthat/fixtures/coevfit_example1-1.csv @@ -1,8 +1,8 @@ # stan_version_major = 2 # stan_version_minor = 34 # stan_version_patch = 1 -# model = model_1451c5e91c245a69a02e26744e116f2b_model -# start_datetime = 2024-07-30 14:22:36 UTC +# model = model_e583ad776a6fc3e0c97b6222a02e0371_model +# start_datetime = 2024-08-09 14:21:45 UTC # method = sample (Default) # sample # num_samples = 50 @@ -31,77 +31,77 @@ # num_chains = 1 (Default) # id = 1 (Default) # data -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4K8hBb/standata-3438389d7b2e.json +# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/standata-158c2af31f07.json # init = 2 (Default) # random # seed = 1 # output -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4K8hBb/model_1451c5e91c245a69a02e26744e116f2b-202407301522-1-047454.csv +# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_e583ad776a6fc3e0c97b6222a02e0371-202408091521-1-937991.csv # diagnostic_file = (Default) # refresh = 100 (Default) # sig_figs = -1 (Default) -# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4K8hBb/model_1451c5e91c245a69a02e26744e116f2b-profile-202407301522-1-8e0328.csv +# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_e583ad776a6fc3e0c97b6222a02e0371-profile-202408091521-1-79af62.csv # save_cmdstan_config = 0 (Default) # num_threads = 1 (Default) # stanc_version = stanc3 v2.34.0 -# stancflags = --name=model_1451c5e91c245a69a02e26744e116f2b_model -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_diag.3,A_diag.4,A_diag.5,A_offdiag.1,A_offdiag.2,A_offdiag.3,A_offdiag.4,A_offdiag.5,A_offdiag.6,A_offdiag.7,A_offdiag.8,A_offdiag.9,A_offdiag.10,A_offdiag.11,A_offdiag.12,A_offdiag.13,A_offdiag.14,A_offdiag.15,A_offdiag.16,A_offdiag.17,A_offdiag.18,A_offdiag.19,A_offdiag.20,Q_diag.1,Q_diag.2,Q_diag.3,Q_diag.4,Q_diag.5,b.1,b.2,b.3,b.4,b.5,eta_anc.1,eta_anc.2,eta_anc.3,eta_anc.4,eta_anc.5,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.7.1,z_drift.8.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,z_drift.7.2,z_drift.8.2,z_drift.1.3,z_drift.2.3,z_drift.3.3,z_drift.4.3,z_drift.5.3,z_drift.6.3,z_drift.7.3,z_drift.8.3,z_drift.1.4,z_drift.2.4,z_drift.3.4,z_drift.4.4,z_drift.5.4,z_drift.6.4,z_drift.7.4,z_drift.8.4,z_drift.1.5,z_drift.2.5,z_drift.3.5,z_drift.4.5,z_drift.5.5,z_drift.6.5,z_drift.7.5,z_drift.8.5,nu1,c4.1,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.8.1,eta.9.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,eta.8.2,eta.9.2,eta.1.3,eta.2.3,eta.3.3,eta.4.3,eta.5.3,eta.6.3,eta.7.3,eta.8.3,eta.9.3,eta.1.4,eta.2.4,eta.3.4,eta.4.4,eta.5.4,eta.6.4,eta.7.4,eta.8.4,eta.9.4,eta.1.5,eta.2.5,eta.3.5,eta.4.5,eta.5.5,eta.6.5,eta.7.5,eta.8.5,eta.9.5,Q.1.1,Q.2.1,Q.3.1,Q.4.1,Q.5.1,Q.1.2,Q.2.2,Q.3.2,Q.4.2,Q.5.2,Q.1.3,Q.2.3,Q.3.3,Q.4.3,Q.5.3,Q.1.4,Q.2.4,Q.3.4,Q.4.4,Q.5.4,Q.1.5,Q.2.5,Q.3.5,Q.4.5,Q.5.5,I.1.1,I.2.1,I.3.1,I.4.1,I.5.1,I.1.2,I.2.2,I.3.2,I.4.2,I.5.2,I.1.3,I.2.3,I.3.3,I.4.3,I.5.3,I.1.4,I.2.4,I.3.4,I.4.4,I.5.4,I.1.5,I.2.5,I.3.5,I.4.5,I.5.5,A.1.1,A.2.1,A.3.1,A.4.1,A.5.1,A.1.2,A.2.2,A.3.2,A.4.2,A.5.2,A.1.3,A.2.3,A.3.3,A.4.3,A.5.3,A.1.4,A.2.4,A.3.4,A.4.4,A.5.4,A.1.5,A.2.5,A.3.5,A.4.5,A.5.5,Q_offdiag.1,Q_offdiag.2,Q_offdiag.3,Q_offdiag.4,Q_offdiag.5,Q_offdiag.6,Q_offdiag.7,Q_offdiag.8,Q_offdiag.9,Q_offdiag.10,Q_offdiag.11,Q_offdiag.12,Q_offdiag.13,Q_offdiag.14,Q_offdiag.15,Q_offdiag.16,Q_offdiag.17,Q_offdiag.18,Q_offdiag.19,Q_offdiag.20,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.8.1,drift_tips.9.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,drift_tips.8.2,drift_tips.9.2,drift_tips.1.3,drift_tips.2.3,drift_tips.3.3,drift_tips.4.3,drift_tips.5.3,drift_tips.6.3,drift_tips.7.3,drift_tips.8.3,drift_tips.9.3,drift_tips.1.4,drift_tips.2.4,drift_tips.3.4,drift_tips.4.4,drift_tips.5.4,drift_tips.6.4,drift_tips.7.4,drift_tips.8.4,drift_tips.9.4,drift_tips.1.5,drift_tips.2.5,drift_tips.3.5,drift_tips.4.5,drift_tips.5.5,drift_tips.6.5,drift_tips.7.5,drift_tips.8.5,drift_tips.9.5,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.8.1,sigma_tips.9.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,sigma_tips.8.2,sigma_tips.9.2,sigma_tips.1.3,sigma_tips.2.3,sigma_tips.3.3,sigma_tips.4.3,sigma_tips.5.3,sigma_tips.6.3,sigma_tips.7.3,sigma_tips.8.3,sigma_tips.9.3,sigma_tips.1.4,sigma_tips.2.4,sigma_tips.3.4,sigma_tips.4.4,sigma_tips.5.4,sigma_tips.6.4,sigma_tips.7.4,sigma_tips.8.4,sigma_tips.9.4,sigma_tips.1.5,sigma_tips.2.5,sigma_tips.3.5,sigma_tips.4.5,sigma_tips.5.5,sigma_tips.6.5,sigma_tips.7.5,sigma_tips.8.5,sigma_tips.9.5,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,log_lik.9,log_lik.10,log_lik.11,log_lik.12,log_lik.13,log_lik.14,log_lik.15,log_lik.16,log_lik.17,log_lik.18,log_lik.19,log_lik.20,log_lik.21,log_lik.22,log_lik.23,log_lik.24,log_lik.25,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.5.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2,yrep.5.2,yrep.1.3,yrep.2.3,yrep.3.3,yrep.4.3,yrep.5.3,yrep.1.4,yrep.2.4,yrep.3.4,yrep.4.4,yrep.5.4,yrep.1.5,yrep.2.5,yrep.3.5,yrep.4.5,yrep.5.5 +# stancflags = --name=model_e583ad776a6fc3e0c97b6222a02e0371_model +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_diag.3,A_diag.4,A_diag.5,A_offdiag.1,A_offdiag.2,A_offdiag.3,A_offdiag.4,A_offdiag.5,A_offdiag.6,A_offdiag.7,A_offdiag.8,A_offdiag.9,A_offdiag.10,A_offdiag.11,A_offdiag.12,A_offdiag.13,A_offdiag.14,A_offdiag.15,A_offdiag.16,A_offdiag.17,A_offdiag.18,A_offdiag.19,A_offdiag.20,Q_diag.1,Q_diag.2,Q_diag.3,Q_diag.4,Q_diag.5,b.1,b.2,b.3,b.4,b.5,eta_anc.1,eta_anc.2,eta_anc.3,eta_anc.4,eta_anc.5,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.7.1,z_drift.8.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,z_drift.7.2,z_drift.8.2,z_drift.1.3,z_drift.2.3,z_drift.3.3,z_drift.4.3,z_drift.5.3,z_drift.6.3,z_drift.7.3,z_drift.8.3,z_drift.1.4,z_drift.2.4,z_drift.3.4,z_drift.4.4,z_drift.5.4,z_drift.6.4,z_drift.7.4,z_drift.8.4,z_drift.1.5,z_drift.2.5,z_drift.3.5,z_drift.4.5,z_drift.5.5,z_drift.6.5,z_drift.7.5,z_drift.8.5,nu1,c4.1,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.8.1,eta.9.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,eta.8.2,eta.9.2,eta.1.3,eta.2.3,eta.3.3,eta.4.3,eta.5.3,eta.6.3,eta.7.3,eta.8.3,eta.9.3,eta.1.4,eta.2.4,eta.3.4,eta.4.4,eta.5.4,eta.6.4,eta.7.4,eta.8.4,eta.9.4,eta.1.5,eta.2.5,eta.3.5,eta.4.5,eta.5.5,eta.6.5,eta.7.5,eta.8.5,eta.9.5,A.1.1,A.2.1,A.3.1,A.4.1,A.5.1,A.1.2,A.2.2,A.3.2,A.4.2,A.5.2,A.1.3,A.2.3,A.3.3,A.4.3,A.5.3,A.1.4,A.2.4,A.3.4,A.4.4,A.5.4,A.1.5,A.2.5,A.3.5,A.4.5,A.5.5,Q.1.1,Q.2.1,Q.3.1,Q.4.1,Q.5.1,Q.1.2,Q.2.2,Q.3.2,Q.4.2,Q.5.2,Q.1.3,Q.2.3,Q.3.3,Q.4.3,Q.5.3,Q.1.4,Q.2.4,Q.3.4,Q.4.4,Q.5.4,Q.1.5,Q.2.5,Q.3.5,Q.4.5,Q.5.5,Q_inf.1.1,Q_inf.2.1,Q_inf.3.1,Q_inf.4.1,Q_inf.5.1,Q_inf.1.2,Q_inf.2.2,Q_inf.3.2,Q_inf.4.2,Q_inf.5.2,Q_inf.1.3,Q_inf.2.3,Q_inf.3.3,Q_inf.4.3,Q_inf.5.3,Q_inf.1.4,Q_inf.2.4,Q_inf.3.4,Q_inf.4.4,Q_inf.5.4,Q_inf.1.5,Q_inf.2.5,Q_inf.3.5,Q_inf.4.5,Q_inf.5.5,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.8.1,drift_tips.9.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,drift_tips.8.2,drift_tips.9.2,drift_tips.1.3,drift_tips.2.3,drift_tips.3.3,drift_tips.4.3,drift_tips.5.3,drift_tips.6.3,drift_tips.7.3,drift_tips.8.3,drift_tips.9.3,drift_tips.1.4,drift_tips.2.4,drift_tips.3.4,drift_tips.4.4,drift_tips.5.4,drift_tips.6.4,drift_tips.7.4,drift_tips.8.4,drift_tips.9.4,drift_tips.1.5,drift_tips.2.5,drift_tips.3.5,drift_tips.4.5,drift_tips.5.5,drift_tips.6.5,drift_tips.7.5,drift_tips.8.5,drift_tips.9.5,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.8.1,sigma_tips.9.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,sigma_tips.8.2,sigma_tips.9.2,sigma_tips.1.3,sigma_tips.2.3,sigma_tips.3.3,sigma_tips.4.3,sigma_tips.5.3,sigma_tips.6.3,sigma_tips.7.3,sigma_tips.8.3,sigma_tips.9.3,sigma_tips.1.4,sigma_tips.2.4,sigma_tips.3.4,sigma_tips.4.4,sigma_tips.5.4,sigma_tips.6.4,sigma_tips.7.4,sigma_tips.8.4,sigma_tips.9.4,sigma_tips.1.5,sigma_tips.2.5,sigma_tips.3.5,sigma_tips.4.5,sigma_tips.5.5,sigma_tips.6.5,sigma_tips.7.5,sigma_tips.8.5,sigma_tips.9.5,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,log_lik.9,log_lik.10,log_lik.11,log_lik.12,log_lik.13,log_lik.14,log_lik.15,log_lik.16,log_lik.17,log_lik.18,log_lik.19,log_lik.20,log_lik.21,log_lik.22,log_lik.23,log_lik.24,log_lik.25,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.5.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2,yrep.5.2,yrep.1.3,yrep.2.3,yrep.3.3,yrep.4.3,yrep.5.3,yrep.1.4,yrep.2.4,yrep.3.4,yrep.4.4,yrep.5.4,yrep.1.5,yrep.2.5,yrep.3.5,yrep.4.5,yrep.5.5 # Adaptation terminated -# Step size = 0.0738804 +# Step size = 0.0873317 # Diagonal elements of inverse mass matrix: # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 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zE4X7o&=a?B*CO63pYJD;{!MeZr8H~zW=c^|-Kyflr>_Qr(qBWsQM1UiCDhysskBHz zMO8B5Qw8Pml4{NI{$TCbay4|Tx`Bk|Wfshz>Q;)^}{(!0DJ z?-va#maog^{f)MnYeUE25TWuuqHmvu$>6ZgLeuUeI}%wvwCG42i|0n$6Yx!<@3)M7 M7Y-$+NHAdk1Cy6C-2eap diff --git a/tests/testthat/fixtures/coevfit_example7-1.csv b/tests/testthat/fixtures/coevfit_example7-1.csv index 42c4eb2..44f96d6 100644 --- a/tests/testthat/fixtures/coevfit_example7-1.csv +++ b/tests/testthat/fixtures/coevfit_example7-1.csv @@ -1,8 +1,8 @@ # stan_version_major = 2 # stan_version_minor = 34 # stan_version_patch = 1 -# model = model_acfbc51126512d5f1eabc55a35956f60_model -# start_datetime = 2024-07-30 14:32:58 UTC +# model = model_f7fa7e85dd3a32aa7e998a96e88c67db_model +# start_datetime = 2024-08-09 14:31:04 UTC # method = sample (Default) # sample # num_samples = 50 @@ -31,77 +31,77 @@ # num_chains = 1 (Default) # id = 1 (Default) # data -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4K8hBb/standata-34384626816.json +# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/standata-158c1785570c.json # init = 2 (Default) # random # seed = 1 # output -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4K8hBb/model_acfbc51126512d5f1eabc55a35956f60-202407301532-1-2226de.csv +# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_f7fa7e85dd3a32aa7e998a96e88c67db-202408091531-1-18f7fc.csv # diagnostic_file = (Default) # refresh = 100 (Default) # sig_figs = -1 (Default) -# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4K8hBb/model_acfbc51126512d5f1eabc55a35956f60-profile-202407301532-1-335b39.csv +# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_f7fa7e85dd3a32aa7e998a96e88c67db-profile-202408091531-1-11d5ad.csv # save_cmdstan_config = 0 (Default) # num_threads = 1 (Default) # stanc_version = stanc3 v2.34.0 -# stancflags = --name=model_acfbc51126512d5f1eabc55a35956f60_model -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_offdiag.1,A_offdiag.2,Q_diag.1,Q_diag.2,b.1,b.2,eta_anc.1,eta_anc.2,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.7.1,z_drift.8.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,z_drift.7.2,z_drift.8.2,c2.1,c2.2,c2.3,group_z.1.1,group_z.2.1,group_z.1.2,group_z.2.2,group_z.1.3,group_z.2.3,group_z.1.4,group_z.2.4,group_z.1.5,group_z.2.5,sigma_group.1,sigma_group.2,L_group.1.1,L_group.2.1,L_group.1.2,L_group.2.2,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.8.1,eta.9.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,eta.8.2,eta.9.2,Q.1.1,Q.2.1,Q.1.2,Q.2.2,I.1.1,I.2.1,I.1.2,I.2.2,A.1.1,A.2.1,A.1.2,A.2.2,Q_offdiag.1,Q_offdiag.2,group_v.1.1,group_v.2.1,group_v.3.1,group_v.4.1,group_v.5.1,group_v.1.2,group_v.2.2,group_v.3.2,group_v.4.2,group_v.5.2,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.8.1,drift_tips.9.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,drift_tips.8.2,drift_tips.9.2,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.8.1,sigma_tips.9.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,sigma_tips.8.2,sigma_tips.9.2,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,log_lik.9,log_lik.10,log_lik.11,log_lik.12,log_lik.13,log_lik.14,log_lik.15,log_lik.16,log_lik.17,log_lik.18,log_lik.19,log_lik.20,log_lik.21,log_lik.22,log_lik.23,log_lik.24,log_lik.25,log_lik.26,log_lik.27,log_lik.28,log_lik.29,log_lik.30,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.5.1,yrep.6.1,yrep.7.1,yrep.8.1,yrep.9.1,yrep.10.1,yrep.11.1,yrep.12.1,yrep.13.1,yrep.14.1,yrep.15.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2,yrep.5.2,yrep.6.2,yrep.7.2,yrep.8.2,yrep.9.2,yrep.10.2,yrep.11.2,yrep.12.2,yrep.13.2,yrep.14.2,yrep.15.2,cor_group.1.1,cor_group.2.1,cor_group.1.2,cor_group.2.2 +# stancflags = --name=model_f7fa7e85dd3a32aa7e998a96e88c67db_model +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_offdiag.1,A_offdiag.2,Q_diag.1,Q_diag.2,b.1,b.2,eta_anc.1,eta_anc.2,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.7.1,z_drift.8.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,z_drift.7.2,z_drift.8.2,c2.1,c2.2,c2.3,group_z.1.1,group_z.2.1,group_z.1.2,group_z.2.2,group_z.1.3,group_z.2.3,group_z.1.4,group_z.2.4,group_z.1.5,group_z.2.5,sigma_group.1,sigma_group.2,L_group.1.1,L_group.2.1,L_group.1.2,L_group.2.2,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.8.1,eta.9.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,eta.8.2,eta.9.2,A.1.1,A.2.1,A.1.2,A.2.2,Q.1.1,Q.2.1,Q.1.2,Q.2.2,Q_inf.1.1,Q_inf.2.1,Q_inf.1.2,Q_inf.2.2,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.8.1,drift_tips.9.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,drift_tips.8.2,drift_tips.9.2,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.8.1,sigma_tips.9.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,sigma_tips.8.2,sigma_tips.9.2,group_v.1.1,group_v.2.1,group_v.3.1,group_v.4.1,group_v.5.1,group_v.1.2,group_v.2.2,group_v.3.2,group_v.4.2,group_v.5.2,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,log_lik.9,log_lik.10,log_lik.11,log_lik.12,log_lik.13,log_lik.14,log_lik.15,log_lik.16,log_lik.17,log_lik.18,log_lik.19,log_lik.20,log_lik.21,log_lik.22,log_lik.23,log_lik.24,log_lik.25,log_lik.26,log_lik.27,log_lik.28,log_lik.29,log_lik.30,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.5.1,yrep.6.1,yrep.7.1,yrep.8.1,yrep.9.1,yrep.10.1,yrep.11.1,yrep.12.1,yrep.13.1,yrep.14.1,yrep.15.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2,yrep.5.2,yrep.6.2,yrep.7.2,yrep.8.2,yrep.9.2,yrep.10.2,yrep.11.2,yrep.12.2,yrep.13.2,yrep.14.2,yrep.15.2,cor_group.1.1,cor_group.2.1,cor_group.1.2,cor_group.2.2 # Adaptation terminated -# Step size = 0.218474 +# Step size = 0.121652 # Diagonal elements of inverse mass matrix: # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 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zS1s>mW4Z}><5Ph3fpH|0u%GBV&5LE-i%O9svV7ZW={ZStoX-(HrhlF(BoRbFW%W20{nl2+5KV zC?W_HT?EvXXa$2JjzLk2LGkqo!e>Q9*Tm{48BtTyZCha6>`iqXBQ}E|QLfq7U5i1U zPxw<4*n|twGN(N8v8pz)X=H3&S+^?J!Dzs>YlNX}U?)a8Ov3nxhE36ljHLJRaZB0t zW6K0unU1E*?!eGBCVUKYN_bx?hO9Y!O@wMi=ARJh_j0kZ8QwaumJ}jsn4H0yS-L7} z3x{dEJG7C06`V)WnjXsx6! zNt75#Jo~~_CnOVF>v)@OY&i(_T;Av`^rOqazBv2nKlsa!{@g#<_Y2E^;h*vimXH4J z&i}LZU;o1&iSNJr?BD)B?)&fN_&0}_iTD%nBEH!U9KKZwd;&Xa*oUUO5%J*$B7EnM z{>}RtX-~`;ZPTY!+ApTqvTiNAv9)N*a9@>dvmnZ zlqvYZ`5PPs#WqCP4}jc@?Sfnn@Xv1WQ4Tvud%X_w9f00crr60v%_7I;lAlW4Z60-< zyVx9n=l6>ZX1sKcfE9bl{7E351Sacwb(?(q8MQzhx~G}>21DO_^vJYr>VkgcLt#E? z4{!>sicPS)=pqZ>=0>00I~jQbpOxp0_V--wqRWju8{I8jrWX-P2>>!e=e<>88NqLf ag6Mv|JLtB$Rd=`L`~QEudu$)H@C^Ve;WW_z From dec79d02f63172c391061de48165b68a2e94874c Mon Sep 17 00:00:00 2001 From: Scott Claessens Date: Fri, 9 Aug 2024 16:37:38 +0100 Subject: [PATCH 10/12] Fix indexing issue in likelihood --- R/coev_make_stancode.R | 68 ++++++++++++++++++++++-------------------- 1 file changed, 35 insertions(+), 33 deletions(-) diff --git a/R/coev_make_stancode.R b/R/coev_make_stancode.R index 194b964..924562b 100644 --- a/R/coev_make_stancode.R +++ b/R/coev_make_stancode.R @@ -483,68 +483,70 @@ coev_make_stancode <- function(data, variables, id, tree, " if (!prior_only) {\n", " for (i in 1:N_obs) {\n" ) - # save linear model - lmod <- paste0( - "eta[tip_id[i]][", j, "]", - ifelse( - !is.null(dist_mat), - paste0(" + dist_v[tip_id[i],", j, "]"), - "" + # function to get linear model + lmod <- function(j) { + paste0( + "eta[tip_id[i]][", j, "]", + ifelse( + !is.null(dist_mat), + paste0(" + dist_v[tip_id[i],", j, "]"), + "" ), - ifelse( - any(duplicated(data[,id])), - paste0(" + group_v[tip_id[i],", j, "]"), - "" + ifelse( + any(duplicated(data[,id])), + paste0(" + group_v[tip_id[i],", j, "]"), + "" ) - ) + ) + } for (j in 1:length(distributions)) { if (distributions[j] == "bernoulli_logit") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "bernoulli_logit(", lmod, + "bernoulli_logit(", lmod(j), " + drift_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "ordered_logistic") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "ordered_logistic(", lmod, + "ordered_logistic(", lmod(j), " + drift_tips[tip_id[i]][", j, "], c", j, ");\n" ) } else if (distributions[j] == "poisson_softplus") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "poisson(mean(obs", j, ") * log1p_exp(", lmod, + "poisson(mean(obs", j, ") * log1p_exp(", lmod(j), " + drift_tips[tip_id[i]][", j, "]));\n" ) } else if (distributions[j] == "normal") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", - "normal(", lmod, + "normal(", lmod(j), ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "student_t") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", - "student_t(nu", j, ", ", lmod, + "student_t(nu", j, ", ", lmod(j), ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "lognormal") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", - "lognormal(", lmod, + "lognormal(", lmod(j), ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "negative_binomial_softplus") { sc_model <- paste0( sc_model, " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", - "neg_binomial_2(mean(obs", j, ") * log1p_exp(", lmod, + "neg_binomial_2(mean(obs", j, ") * log1p_exp(", lmod(j), " + drift_tips[tip_id[i]][", j, "]), phi", j, ");\n" ) } @@ -583,20 +585,20 @@ coev_make_stancode <- function(data, variables, id, tree, sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "bernoulli_logit_lpmf(to_int(y[i,", j, "]) | ", lmod, + "bernoulli_logit_lpmf(to_int(y[i,", j, "]) | ", lmod(j), " + drift_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "bernoulli_logit_rng(", lmod, + "bernoulli_logit_rng(", lmod(j), " + drift_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "ordered_logistic") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "ordered_logistic_lpmf(to_int(y[i,", j, "]) | ", lmod, + "ordered_logistic_lpmf(to_int(y[i,", j, "]) | ", lmod(j), " + drift_tips[tip_id[i]][", j, "], c", j, ");\n", " yrep_temp[i,", j, "] = ", - "ordered_logistic_rng(", lmod, + "ordered_logistic_rng(", lmod(j), " + drift_tips[tip_id[i]][", j, "], c", j, ");\n" ) } else if (distributions[j] == "poisson_softplus") { @@ -604,40 +606,40 @@ coev_make_stancode <- function(data, variables, id, tree, sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", "poisson_lpmf(to_int(y[i,", j, "]) | mean(obs", j, - ") * log1p_exp(", lmod, + ") * log1p_exp(", lmod(j), " + drift_tips[tip_id[i]][", j, "]));\n", " yrep_temp[i,", j, "] = ", - "poisson_rng(mean(obs", j, ") * log1p_exp(", lmod, + "poisson_rng(mean(obs", j, ") * log1p_exp(", lmod(j), " + drift_tips[tip_id[i]][", j, "]));\n" ) } else if (distributions[j] == "normal") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "normal_lpdf(y[i,", j, "] | ", lmod, + "normal_lpdf(y[i,", j, "] | ", lmod(j), ", sigma_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "normal_rng(", lmod, + "normal_rng(", lmod(j), ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "student_t") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "student_t_lpdf(y[i,", j, "] | nu", j, ", ", lmod, + "student_t_lpdf(y[i,", j, "] | nu", j, ", ", lmod(j), ", sigma_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "student_t_rng(nu", j, ", ", lmod, + "student_t_rng(nu", j, ", ", lmod(j), ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "lognormal") { sc_generated_quantities <- paste0( sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", - "lognormal_lpdf(y[i,", j, "] | ", lmod, + "lognormal_lpdf(y[i,", j, "] | ", lmod(j), ", sigma_tips[tip_id[i]][", j, "]);\n", " yrep_temp[i,", j, "] = ", - "lognormal_rng(", lmod, + "lognormal_rng(", lmod(j), ", sigma_tips[tip_id[i]][", j, "]);\n" ) } else if (distributions[j] == "negative_binomial_softplus") { @@ -645,10 +647,10 @@ coev_make_stancode <- function(data, variables, id, tree, sc_generated_quantities, " if (miss[i,", j, "] == 0) log_lik_temp[i,", j, "] = ", "neg_binomial_2_lpmf(to_int(y[i,", j, "]) | mean(obs", j, - ") * log1p_exp(", lmod, + ") * log1p_exp(", lmod(j), " + drift_tips[tip_id[i]][", j, "]), phi", j, ");\n", " yrep_temp[i,", j, "] = ", - "neg_binomial_2_rng(mean(obs", j, ") * log1p_exp(", lmod, + "neg_binomial_2_rng(mean(obs", j, ") * log1p_exp(", lmod(j), " + drift_tips[tip_id[i]][", j, "]), phi", j, ");\n" ) } From da0a7291505246fd45cbdebfa5dc623d1c40d2f2 Mon Sep 17 00:00:00 2001 From: Scott Claessens Date: Fri, 9 Aug 2024 23:36:25 +0100 Subject: [PATCH 11/12] Re-update example models after indexing fix --- .../testthat/fixtures/coevfit_example1-1.csv | 120 +++++++++--------- tests/testthat/fixtures/coevfit_example1.rds | Bin 103186 -> 103179 bytes .../testthat/fixtures/coevfit_example2-1.csv | 120 +++++++++--------- tests/testthat/fixtures/coevfit_example2.rds | Bin 103973 -> 104004 bytes .../testthat/fixtures/coevfit_example3-1.csv | 118 ++++++++--------- tests/testthat/fixtures/coevfit_example3.rds | Bin 98395 -> 98382 bytes .../testthat/fixtures/coevfit_example4-1.csv | 120 +++++++++--------- tests/testthat/fixtures/coevfit_example4.rds | Bin 99062 -> 98783 bytes .../testthat/fixtures/coevfit_example5-1.csv | 120 +++++++++--------- tests/testthat/fixtures/coevfit_example5.rds | Bin 57805 -> 57518 bytes .../testthat/fixtures/coevfit_example6-1.csv | 120 +++++++++--------- tests/testthat/fixtures/coevfit_example6.rds | Bin 57420 -> 57453 bytes .../testthat/fixtures/coevfit_example7-1.csv | 120 +++++++++--------- tests/testthat/fixtures/coevfit_example7.rds | Bin 102805 -> 103060 bytes 14 files changed, 419 insertions(+), 419 deletions(-) diff --git a/tests/testthat/fixtures/coevfit_example1-1.csv b/tests/testthat/fixtures/coevfit_example1-1.csv index 416815a..6632490 100644 --- a/tests/testthat/fixtures/coevfit_example1-1.csv +++ b/tests/testthat/fixtures/coevfit_example1-1.csv @@ -1,8 +1,8 @@ # stan_version_major = 2 # stan_version_minor = 34 # stan_version_patch = 1 -# model = model_e583ad776a6fc3e0c97b6222a02e0371_model -# start_datetime = 2024-08-09 14:21:45 UTC +# model = model_f5fe1b4adf9152d6d691857ecc702ea6_model +# start_datetime = 2024-08-09 19:59:06 UTC # method = sample (Default) # sample # num_samples = 50 @@ -31,77 +31,77 @@ # num_chains = 1 (Default) # id = 1 (Default) # data -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/standata-158c2af31f07.json +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/standata-51501ec87240.json # init = 2 (Default) # random # seed = 1 # output -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_e583ad776a6fc3e0c97b6222a02e0371-202408091521-1-937991.csv +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_f5fe1b4adf9152d6d691857ecc702ea6-202408092059-1-04916c.csv # diagnostic_file = (Default) # refresh = 100 (Default) # sig_figs = -1 (Default) -# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_e583ad776a6fc3e0c97b6222a02e0371-profile-202408091521-1-79af62.csv +# profile_file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_f5fe1b4adf9152d6d691857ecc702ea6-profile-202408092059-1-8e2040.csv # save_cmdstan_config = 0 (Default) # num_threads = 1 (Default) # stanc_version = stanc3 v2.34.0 -# stancflags = --name=model_e583ad776a6fc3e0c97b6222a02e0371_model +# stancflags = --name=model_f5fe1b4adf9152d6d691857ecc702ea6_model lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_diag.3,A_diag.4,A_diag.5,A_offdiag.1,A_offdiag.2,A_offdiag.3,A_offdiag.4,A_offdiag.5,A_offdiag.6,A_offdiag.7,A_offdiag.8,A_offdiag.9,A_offdiag.10,A_offdiag.11,A_offdiag.12,A_offdiag.13,A_offdiag.14,A_offdiag.15,A_offdiag.16,A_offdiag.17,A_offdiag.18,A_offdiag.19,A_offdiag.20,Q_diag.1,Q_diag.2,Q_diag.3,Q_diag.4,Q_diag.5,b.1,b.2,b.3,b.4,b.5,eta_anc.1,eta_anc.2,eta_anc.3,eta_anc.4,eta_anc.5,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.7.1,z_drift.8.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,z_drift.7.2,z_drift.8.2,z_drift.1.3,z_drift.2.3,z_drift.3.3,z_drift.4.3,z_drift.5.3,z_drift.6.3,z_drift.7.3,z_drift.8.3,z_drift.1.4,z_drift.2.4,z_drift.3.4,z_drift.4.4,z_drift.5.4,z_drift.6.4,z_drift.7.4,z_drift.8.4,z_drift.1.5,z_drift.2.5,z_drift.3.5,z_drift.4.5,z_drift.5.5,z_drift.6.5,z_drift.7.5,z_drift.8.5,nu1,c4.1,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.8.1,eta.9.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,eta.8.2,eta.9.2,eta.1.3,eta.2.3,eta.3.3,eta.4.3,eta.5.3,eta.6.3,eta.7.3,eta.8.3,eta.9.3,eta.1.4,eta.2.4,eta.3.4,eta.4.4,eta.5.4,eta.6.4,eta.7.4,eta.8.4,eta.9.4,eta.1.5,eta.2.5,eta.3.5,eta.4.5,eta.5.5,eta.6.5,eta.7.5,eta.8.5,eta.9.5,A.1.1,A.2.1,A.3.1,A.4.1,A.5.1,A.1.2,A.2.2,A.3.2,A.4.2,A.5.2,A.1.3,A.2.3,A.3.3,A.4.3,A.5.3,A.1.4,A.2.4,A.3.4,A.4.4,A.5.4,A.1.5,A.2.5,A.3.5,A.4.5,A.5.5,Q.1.1,Q.2.1,Q.3.1,Q.4.1,Q.5.1,Q.1.2,Q.2.2,Q.3.2,Q.4.2,Q.5.2,Q.1.3,Q.2.3,Q.3.3,Q.4.3,Q.5.3,Q.1.4,Q.2.4,Q.3.4,Q.4.4,Q.5.4,Q.1.5,Q.2.5,Q.3.5,Q.4.5,Q.5.5,Q_inf.1.1,Q_inf.2.1,Q_inf.3.1,Q_inf.4.1,Q_inf.5.1,Q_inf.1.2,Q_inf.2.2,Q_inf.3.2,Q_inf.4.2,Q_inf.5.2,Q_inf.1.3,Q_inf.2.3,Q_inf.3.3,Q_inf.4.3,Q_inf.5.3,Q_inf.1.4,Q_inf.2.4,Q_inf.3.4,Q_inf.4.4,Q_inf.5.4,Q_inf.1.5,Q_inf.2.5,Q_inf.3.5,Q_inf.4.5,Q_inf.5.5,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.8.1,drift_tips.9.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,drift_tips.8.2,drift_tips.9.2,drift_tips.1.3,drift_tips.2.3,drift_tips.3.3,drift_tips.4.3,drift_tips.5.3,drift_tips.6.3,drift_tips.7.3,drift_tips.8.3,drift_tips.9.3,drift_tips.1.4,drift_tips.2.4,drift_tips.3.4,drift_tips.4.4,drift_tips.5.4,drift_tips.6.4,drift_tips.7.4,drift_tips.8.4,drift_tips.9.4,drift_tips.1.5,drift_tips.2.5,drift_tips.3.5,drift_tips.4.5,drift_tips.5.5,drift_tips.6.5,drift_tips.7.5,drift_tips.8.5,drift_tips.9.5,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.8.1,sigma_tips.9.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,sigma_tips.8.2,sigma_tips.9.2,sigma_tips.1.3,sigma_tips.2.3,sigma_tips.3.3,sigma_tips.4.3,sigma_tips.5.3,sigma_tips.6.3,sigma_tips.7.3,sigma_tips.8.3,sigma_tips.9.3,sigma_tips.1.4,sigma_tips.2.4,sigma_tips.3.4,sigma_tips.4.4,sigma_tips.5.4,sigma_tips.6.4,sigma_tips.7.4,sigma_tips.8.4,sigma_tips.9.4,sigma_tips.1.5,sigma_tips.2.5,sigma_tips.3.5,sigma_tips.4.5,sigma_tips.5.5,sigma_tips.6.5,sigma_tips.7.5,sigma_tips.8.5,sigma_tips.9.5,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,log_lik.9,log_lik.10,log_lik.11,log_lik.12,log_lik.13,log_lik.14,log_lik.15,log_lik.16,log_lik.17,log_lik.18,log_lik.19,log_lik.20,log_lik.21,log_lik.22,log_lik.23,log_lik.24,log_lik.25,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.5.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2,yrep.5.2,yrep.1.3,yrep.2.3,yrep.3.3,yrep.4.3,yrep.5.3,yrep.1.4,yrep.2.4,yrep.3.4,yrep.4.4,yrep.5.4,yrep.1.5,yrep.2.5,yrep.3.5,yrep.4.5,yrep.5.5 # Adaptation terminated -# Step size = 0.0873317 +# Step size = 0.123879 # Diagonal elements of inverse mass matrix: # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 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zH_GlyrB`NPQ-6RrxmQH90{D3dKmUqHRb;q%6mEgTWkZcECvBR*S2i&CD-z19)>J@i zDk3!%oto6W5HYGM5^}3Wthy3XUCF7AZLP@Cu)tY5oS-^I>dv(ss~x>^0#4DCHL+4p ztb3ztcWny{I^5CxG1AA7+A3sR?es9@`M5X6#^w}=?SFFTX9<&R(g%`(Np;<-zStKdhOhdzw(Gw`!|drXg<_ed~ol(O*Nm zu{K6cG{vAyX=brPvAqG+otgLdnKzR!l7!ZU($=pn zQHZ1IJ`r=`qplc}I>pF93h@f#kr=_3f{_7IKHq-nZ#$VE_}{Cith@flCm&e;wWaa{ z|L8A2@H78t-_I@o#ec>(jz94I&i}jh=l{naiSNIA@%#Vho&Up^5|RP&2*j^UUyI5O E0B=myr2qf` diff --git a/tests/testthat/fixtures/coevfit_example2-1.csv b/tests/testthat/fixtures/coevfit_example2-1.csv index 8a72bbd..50c5255 100644 --- a/tests/testthat/fixtures/coevfit_example2-1.csv +++ b/tests/testthat/fixtures/coevfit_example2-1.csv @@ -1,8 +1,8 @@ # stan_version_major = 2 # stan_version_minor = 34 # stan_version_patch = 1 -# model = model_cfb4f5a6fdcf94b392af434ec431b111_model -# start_datetime = 2024-08-09 14:24:04 UTC +# model = model_02f93d79fb6696482660a181ff9e22f5_model +# start_datetime = 2024-08-09 20:01:21 UTC # method = sample (Default) # sample # num_samples = 50 @@ -31,77 +31,77 @@ # num_chains = 1 (Default) # id = 1 (Default) # data -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/standata-158cdda461b.json +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/standata-515094c3206.json # init = 2 (Default) # random # seed = 1 # output -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_cfb4f5a6fdcf94b392af434ec431b111-202408091524-1-73c949.csv +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_02f93d79fb6696482660a181ff9e22f5-202408092101-1-112d7e.csv # diagnostic_file = (Default) # refresh = 100 (Default) # sig_figs = -1 (Default) -# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_cfb4f5a6fdcf94b392af434ec431b111-profile-202408091524-1-0e39fa.csv +# profile_file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_02f93d79fb6696482660a181ff9e22f5-profile-202408092101-1-77ebe2.csv # save_cmdstan_config = 0 (Default) # num_threads = 1 (Default) # stanc_version = stanc3 v2.34.0 -# stancflags = --name=model_cfb4f5a6fdcf94b392af434ec431b111_model +# stancflags = --name=model_02f93d79fb6696482660a181ff9e22f5_model lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_offdiag.1,A_offdiag.2,Q_diag.1,Q_diag.2,b.1,b.2,eta_anc.1,eta_anc.2,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.7.1,z_drift.8.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,z_drift.7.2,z_drift.8.2,c2.1,dist_z.1.1,dist_z.2.1,dist_z.3.1,dist_z.4.1,dist_z.5.1,dist_z.1.2,dist_z.2.2,dist_z.3.2,dist_z.4.2,dist_z.5.2,rho_dist.1,rho_dist.2,sigma_dist.1,sigma_dist.2,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.8.1,eta.9.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,eta.8.2,eta.9.2,A.1.1,A.2.1,A.1.2,A.2.2,Q.1.1,Q.2.1,Q.1.2,Q.2.2,Q_inf.1.1,Q_inf.2.1,Q_inf.1.2,Q_inf.2.2,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.8.1,drift_tips.9.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,drift_tips.8.2,drift_tips.9.2,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.8.1,sigma_tips.9.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,sigma_tips.8.2,sigma_tips.9.2,dist_v.1.1,dist_v.2.1,dist_v.3.1,dist_v.4.1,dist_v.5.1,dist_v.1.2,dist_v.2.2,dist_v.3.2,dist_v.4.2,dist_v.5.2,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,log_lik.9,log_lik.10,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.5.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2,yrep.5.2 # Adaptation terminated -# Step size = 0.297352 +# Step size = 0.313564 # Diagonal elements of inverse mass matrix: # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --25.4651,0.751039,0.297352,4,15,0,54.6919,-0.387999,-0.0533445,0.0698338,-0.257881,0.691032,1.09248,-0.466844,-0.401914,1.22372,0.0845984,0.295632,1.22054,-0.491216,0.0735119,-0.840883,0.45221,0.129423,-0.415048,-0.157629,1.42557,-1.47933,-0.944152,0.0201753,-0.988043,-0.850573,-1.02892,-1.40637,0.279086,-0.378215,-1.0682,-0.396547,0.824093,0.0301113,-0.240777,0.18386,0.575216,-0.693839,1.73583,3.10663,0.335562,0.965692,0.42863,0.552204,0.552204,0.642598,0.642598,1.22372,0.721576,0.754024,0.645543,-0.512191,-0.899242,-0.899242,-0.629929,-0.629929,0.0845984,-0.599503,-0.551196,-0.836588,-0.387999,-0.257881,0.0698338,-0.0533445,0.691032,0,0,1.09248,0.996368,0.588164,0.588164,7.39652,0.0508988,-0.239283,0.128682,0.0395877,-0.126954,-99,-99,-99,-99,-0.965864,0.00948547,-0.361953,-0.335403,-0.403933,-99,-99,-99,-99,0.691032,0.691032,0.691032,0.691032,0.691032,-99,-99,-99,-99,1.09248,1.09248,1.09248,1.09248,1.09248,-99,-99,-99,-99,0.16406,0.0801397,-0.528172,-0.160683,0.295895,0.029743,-0.0546451,0.182816,0.536234,-0.411901,-0.500473,-1.45804,-1.02959,-0.666459,-0.270595,-0.672397,-0.488605,-0.542538,-0.319424,-0.673646,1,1,0,0,1,1,2,2,2,1 --30.8794,0.845779,0.297352,4,15,0,45.1602,-1.03274,-1.63786,1.06826,-1.05974,1.40409,0.91988,1.26756,-0.698731,1.24002,-0.221868,0.404247,-0.847586,0.817688,0.497668,1.5418,0.609741,0.115999,-0.279942,0.633653,-1.79858,1.46073,-1.28955,-0.612041,1.87337,0.642617,1.5213,-2.27405,-0.105336,1.67051,0.773316,-0.00606112,-0.0418456,-0.970974,0.552933,0.422426,-0.854463,-0.484382,0.896141,0.445269,0.0353957,1.50542,0.747541,1.18016,1.18016,1.00059,1.00059,1.24002,1.10849,1.07503,1.23806,-0.874169,-0.530267,-0.530267,-0.71733,-0.71733,-0.221868,-0.559383,-0.61698,-0.378978,-1.03274,-1.05974,1.06826,-1.63786,1.40409,0,0,0.91988,0.582236,-0.0943041,-0.0943041,0.341835,0.368464,0.601243,0.237776,0.0483124,-0.116593,-99,-99,-99,-99,-0.751364,-0.200409,0.566698,0.207331,0.496875,-99,-99,-99,-99,1.40409,1.40409,1.40409,1.40409,1.40409,-99,-99,-99,-99,0.91988,0.91988,0.91988,0.91988,0.91988,-99,-99,-99,-99,-0.0224432,0.210852,0.189109,0.0662433,0.0903659,-1.19529,-1.06381,-0.58124,-1.50049,-1.22805,-0.167632,-1.30803,-1.22255,-0.108167,-0.119739,-0.456673,-0.481845,-0.163325,-0.570028,-1.18879,0,1,1,0,0,1,2,2,2,1 --27.6486,0.861118,0.297352,4,15,0,49.3168,-1.47891,-0.436262,-0.210165,0.670258,0.997483,0.818599,0.370841,-0.139775,-0.220603,-1.06605,-0.945322,-0.374263,1.36445,-1.10804,1.01469,0.0452517,-0.366132,-0.275531,-0.0583304,-0.351819,-0.119451,0.721777,0.234112,0.449149,-0.637124,1.89194,-1.35872,1.35648,-0.692764,0.447564,0.0776079,1.13094,-0.671,-0.0480646,0.936636,1.37288,0.601306,0.26415,1.86763,0.0185739,0.423342,0.240507,-0.0157152,-0.0157152,-0.18867,-0.18867,-0.220603,-0.311821,-0.322978,-0.0946953,-0.755611,-0.900441,-0.900441,-0.911716,-0.911716,-1.06605,-0.914397,-0.924792,-0.927845,-1.47891,0.670258,-0.210165,-0.436262,0.997483,0,0,0.818599,0.335478,0.0123683,0.0123683,0.957198,-0.622208,0.325181,0.014502,-0.124848,-0.0939541,-99,-99,-99,-99,0.460288,0.081775,0.139188,-0.215706,0.624968,-99,-99,-99,-99,0.997483,0.997483,0.997483,0.997483,0.997483,-99,-99,-99,-99,0.818599,0.818599,0.818599,0.818599,0.818599,-99,-99,-99,-99,0.229297,0.0590846,0.158267,0.120764,0.257291,-0.441711,-0.428043,0.422937,0.622633,-0.0687619,-0.15023,-1.31568,-0.951208,-0.507448,-0.293782,-1.05154,-0.638711,-0.307816,-0.354691,-1.31561,1,0,0,1,1,1,2,2,1,1 --27.3899,0.906207,0.297352,4,15,0,41.5018,-0.387536,-1.15934,0.674371,-0.160566,1.09178,0.429872,-0.428538,-1.46097,0.463659,1.84537,1.6409,0.573185,0.304736,0.401715,-0.433756,0.20373,0.237357,-0.178578,0.792775,-0.90165,1.91974,-1.57284,-0.332723,-1.05709,-0.0937724,-1.80676,-0.760145,-0.179732,-0.687458,-0.725196,0.600639,-0.469211,-0.160065,-0.171525,0.404091,-1.54832,-0.739799,0.688327,0.508506,1.91796,2.4182,0.22896,1.59868,1.59868,1.55784,1.55784,0.463659,1.68882,1.70743,1.71432,-0.326777,0.0755651,0.0755651,-0.0975998,-0.0975998,1.84537,0.174376,0.152902,0.315159,-0.387536,-0.160566,0.674371,-1.15934,1.09178,0,0,0.429872,1.31693,-0.0526915,-0.0526915,0.192693,0.350874,-0.155178,0.0728852,0.0912822,-0.0686771,-99,-99,-99,-99,-0.641077,-0.0721274,-0.226207,-0.0209152,-0.413668,-99,-99,-99,-99,1.09178,1.09178,1.09178,1.09178,1.09178,-99,-99,-99,-99,0.429872,0.429872,0.429872,0.429872,0.429872,-99,-99,-99,-99,-0.24956,-0.420525,-0.925555,0.268117,-0.59363,-0.249424,-0.288944,0.221925,-1.6733,-0.508488,-0.586817,-0.89431,-0.52501,-0.170962,-0.41163,-0.490477,-0.483728,-0.361462,-1.33651,-0.571743,0,0,1,0,0,2,2,1,1,2 --28.4593,0.859489,0.297352,4,15,0,47.3758,-1.60982,-0.653699,-0.789118,0.871932,0.394298,0.970977,2.33855,-0.307297,0.411544,-0.774448,-0.314449,0.831446,-0.166284,0.271086,0.64804,-1.0275,-0.163913,0.0705056,-0.226465,-0.135573,-0.916209,-0.943118,-0.862862,0.397534,-0.301328,-0.127958,-0.356334,0.234336,-0.762215,0.18234,0.0231751,0.0181641,0.645879,-0.57419,-1.42474,1.88434,0.402885,0.0541628,2.11108,0.929701,0.152969,1.34312,1.2593,1.2593,1.27444,1.27444,0.411544,1.20092,1.2186,1.19651,0.0752828,-0.138441,-0.138441,-0.0192951,-0.0192951,-0.774448,-0.134936,-0.138437,-0.247935,-1.60982,0.871932,-0.789118,-0.653699,0.394298,0,0,0.970977,0.185225,-0.12803,-0.12803,0.571907,0.103156,0.129908,-0.205975,-0.0349525,0.0150345,-99,-99,-99,-99,-0.691365,-0.29533,0.146662,-0.103988,-0.0467136,-99,-99,-99,-99,0.394298,0.394298,0.394298,0.394298,0.394298,-99,-99,-99,-99,0.970977,0.970977,0.970977,0.970977,0.970977,-99,-99,-99,-99,0.227161,0.11246,0.216292,0.169062,0.185343,0.260737,0.137284,-0.482507,0.770456,0.207731,-0.936654,-0.630842,-1.18975,-1.11405,-0.800049,-0.693642,-0.663672,-0.753861,-0.31232,-0.972868,0,0,1,1,1,1,2,1,2,2 --32.0842,0.951447,0.297352,4,15,0,45.259,-0.536244,-1.73393,-0.906978,0.0117749,0.145402,0.726223,-0.441037,-0.726365,1.28373,-0.193357,-0.725042,0.20377,-0.783186,-0.386041,0.370668,0.335012,-1.31658,0.20317,-1.69314,1.19268,-1.03107,0.452702,-1.00063,1.1306,-1.246,0.382397,-2.17372,-0.169405,0.587494,-0.664223,0.822812,-0.605265,-0.770884,0.412695,0.0586376,1.28641,0.504725,0.0260023,2.26318,0.787738,0.0779469,0.631257,0.440141,0.440141,0.467373,0.467373,1.28373,0.453018,0.455749,0.413072,-0.374447,-0.923998,-0.923998,-0.797914,-0.797914,-0.193357,-0.939802,-0.905499,-1.04461,-0.536244,0.0117749,-0.906978,-1.73393,0.145402,0,0,0.726223,0.274299,-0.0820201,-0.0820201,0.208858,-0.140112,0.0483816,0.0437276,-0.184984,0.028546,-99,-99,-99,-99,0.255048,-0.279183,0.292725,-0.305501,0.101105,-99,-99,-99,-99,0.145402,0.145402,0.145402,0.145402,0.145402,-99,-99,-99,-99,0.726223,0.726223,0.726223,0.726223,0.726223,-99,-99,-99,-99,-0.151306,-0.0650704,-0.23567,0.0260224,-0.19916,-0.228612,-0.130783,-0.0348392,0.299309,-0.0408994,-0.389067,-1.31794,-1.25192,-0.408661,-0.367929,-1.97508,-0.358943,-0.20003,-0.226502,-1.64941,1,0,1,1,0,2,2,2,2,2 --34.2467,0.941769,0.297352,4,15,0,52.3252,-0.476616,-1.04935,0.116708,0.130452,0.384753,0.858076,0.776655,-0.0452445,-1.09689,0.788869,-0.0330278,0.683629,0.561209,0.0781688,-0.424698,-0.197896,1.72272,-0.316314,0.411395,-1.85908,1.20742,-1.34508,0.938202,-1.54332,1.78655,-1.53849,0.827924,-0.957555,-0.619999,-0.0821001,0.250171,1.04376,0.0338876,0.217426,-0.552697,-1.72677,-0.505523,0.00243521,0.211386,0.724252,0.286779,-0.0243667,0.0143018,0.0143018,-0.0233039,-0.0233039,-1.09689,-0.161795,-0.146897,-0.0919942,0.21412,0.56144,0.56144,0.370293,0.370293,0.788869,0.496045,0.43892,0.645264,-0.476616,0.130452,0.116708,-1.04935,0.384753,0,0,0.858076,0.420242,0.0678411,0.0678411,0.417295,0.0391009,-0.0897086,-0.0418013,0.390829,-0.0717614,-99,-99,-99,-99,-0.799728,0.283985,-0.471374,0.591752,-0.502792,-99,-99,-99,-99,0.384753,0.384753,0.384753,0.384753,0.384753,-99,-99,-99,-99,0.858076,0.858076,0.858076,0.858076,0.858076,-99,-99,-99,-99,-0.820515,-0.896821,-0.862196,-0.824433,-0.730462,0.0184611,0.0497931,-0.121751,-0.405608,-0.0969363,-0.838186,-0.466005,-0.513863,-0.88662,-0.799016,-0.221394,-0.660066,-1.21261,-0.838076,-0.298156,1,0,1,0,1,1,2,2,1,1 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z@x~B5teK^&vadAS8}+=RA+H-l)Chk;f(`?xxi)GIR2V_i14^IMw^EX_Hycv-H z8urXLaPiGsbOYAz-+l3S-OTs?Z`EVg?f=6Q_n!Twm4$o%!C$-gYyV*PH_m_l)qlcw z7T^2*&i}Lh-~PiNi|@a4=KKHqZU6lu|K{*A5q|<+#5dc7wj<+j?el{g$eYU8ho-w% zLBKt9_~tkL8@{!WZ3~;CH0{yhuA}W_2LG1MmCH2j)X>1Ue`&FmF&y6C@G#7%`$U1h zy4Zh+*Lm+(fA8aa?*80Ay7zxierff$5C7|xd;i~mZRJbfoBz_if8#GM{^r(i{2G5R z^8e}l#uq<*lX>s|>uY!YZ`pr#;@-dY$^UsJ_x^t=%Cq+(|6j20fA-_Q^o2KG-Ywws z@_!-o;NCx3_|;>-clI?=KZnoDv-c5P+o{pb{x&W4Uy^US1bcX;ZI6F9@{P8h(I)`2mTJaro(sJ(r5QVSf0RV|GC2X+rxLz1y-Jo o4z4To7E~z#Kt^;tR9hC&ZNt~l-Mw{p)Au|7e{+l$-Tc!H0Kd2XnE(I) diff --git a/tests/testthat/fixtures/coevfit_example3-1.csv b/tests/testthat/fixtures/coevfit_example3-1.csv index 3f7a5c5..6d4d092 100644 --- a/tests/testthat/fixtures/coevfit_example3-1.csv +++ b/tests/testthat/fixtures/coevfit_example3-1.csv @@ -1,8 +1,8 @@ # stan_version_major = 2 # stan_version_minor = 34 # stan_version_patch = 1 -# model = model_cc9f9ceba42698ffc18201f5b3515c43_model -# start_datetime = 2024-08-09 14:26:14 UTC +# model = model_5caa36b63f071e70dad1155510c970b5_model +# start_datetime = 2024-08-09 20:06:32 UTC # method = sample (Default) # sample # num_samples = 50 @@ -31,77 +31,77 @@ # num_chains = 1 (Default) # id = 1 (Default) # data -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/standata-158c2d5924af.json +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/standata-51504645122d.json # init = 2 (Default) # random # seed = 1 # output -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_cc9f9ceba42698ffc18201f5b3515c43-202408091526-1-1e648e.csv +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_5caa36b63f071e70dad1155510c970b5-202408092106-1-80be5d.csv # diagnostic_file = (Default) # refresh = 100 (Default) # sig_figs = -1 (Default) -# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_cc9f9ceba42698ffc18201f5b3515c43-profile-202408091526-1-65a781.csv +# profile_file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_5caa36b63f071e70dad1155510c970b5-profile-202408092106-1-3facef.csv # save_cmdstan_config = 0 (Default) # num_threads = 1 (Default) # stanc_version = stanc3 v2.34.0 -# stancflags = --name=model_cc9f9ceba42698ffc18201f5b3515c43_model +# stancflags = --name=model_5caa36b63f071e70dad1155510c970b5_model lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_offdiag.1,A_offdiag.2,Q_diag.1,Q_diag.2,b.1,b.2,eta_anc.1,eta_anc.2,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.7.1,z_drift.8.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,z_drift.7.2,z_drift.8.2,c2.1,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.8.1,eta.9.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,eta.8.2,eta.9.2,A.1.1,A.2.1,A.1.2,A.2.2,Q.1.1,Q.2.1,Q.1.2,Q.2.2,Q_inf.1.1,Q_inf.2.1,Q_inf.1.2,Q_inf.2.2,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.8.1,drift_tips.9.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,drift_tips.8.2,drift_tips.9.2,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.8.1,sigma_tips.9.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,sigma_tips.8.2,sigma_tips.9.2,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,log_lik.9,log_lik.10,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.5.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2,yrep.5.2 # Adaptation terminated # Step size = 0.359451 # Diagonal elements of inverse mass matrix: # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 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# stan_version_major = 2 # stan_version_minor = 34 # stan_version_patch = 1 -# model = model_cc9f9ceba42698ffc18201f5b3515c43_model -# start_datetime = 2024-08-09 14:28:35 UTC +# model = model_5caa36b63f071e70dad1155510c970b5_model +# start_datetime = 2024-08-09 20:08:41 UTC # method = sample (Default) # sample # num_samples = 50 @@ -31,77 +31,77 @@ # num_chains = 1 (Default) # id = 1 (Default) # data -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/standata-158c61a45e8e.json +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/standata-5150cbe4e4b.json # init = 2 (Default) # random # seed = 1 # output -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_cc9f9ceba42698ffc18201f5b3515c43-202408091528-1-56c81f.csv +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_5caa36b63f071e70dad1155510c970b5-202408092108-1-598c68.csv # diagnostic_file = (Default) # refresh = 100 (Default) # sig_figs = -1 (Default) -# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_cc9f9ceba42698ffc18201f5b3515c43-profile-202408091528-1-8e0e1e.csv +# profile_file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_5caa36b63f071e70dad1155510c970b5-profile-202408092108-1-7a4e24.csv # save_cmdstan_config = 0 (Default) # num_threads = 1 (Default) # stanc_version = stanc3 v2.34.0 -# stancflags = --name=model_cc9f9ceba42698ffc18201f5b3515c43_model +# stancflags = --name=model_5caa36b63f071e70dad1155510c970b5_model lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_offdiag.1,A_offdiag.2,Q_diag.1,Q_diag.2,b.1,b.2,eta_anc.1,eta_anc.2,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,c2.1,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,A.1.1,A.2.1,A.1.2,A.2.2,Q.1.1,Q.2.1,Q.1.2,Q.2.2,Q_inf.1.1,Q_inf.2.1,Q_inf.1.2,Q_inf.2.2,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2 # Adaptation terminated -# Step size = 0.262988 +# Step size = 0.332181 # Diagonal elements of inverse mass matrix: # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --19.4847,0.966434,0.262988,4,15,0,29.6274,-0.210787,-0.688994,0.270933,1.73009,0.485689,0.204987,0.744242,-0.0248016,0.442065,-0.304144,0.0571378,-0.22975,-1.79807,-0.142662,1.13818,-1.2708,1.49295,0.553188,0.153225,-0.867522,-1.82972,-0.818498,1.49299,1.04488,1.04488,1.10356,1.10356,0.442065,0.466852,0.44976,0.80668,0.80668,0.857336,0.857336,-0.304144,-0.0763134,-0.238721,-0.210787,1.73009,0.270933,-0.688994,0.485689,0,0,0.204987,-0.151991,-1.01458,-1.01458,-2.39888,-1.11473,-0.0884446,0.762985,-0.851884,-99,-99,-99,-0.656155,-0.379837,-0.186735,-0.944319,-99,-99,-99,0.485689,0.485689,0.485689,0.485689,-99,-99,-99,0.204987,0.204987,0.204987,0.204987,-99,-99,-99,0,-0.397172,-1.80084,-0.695877,-1.57453,0,-1.1866,-0.187293,0,1,1,0,1,1,1,1 --21.5741,0.97171,0.262988,4,15,0,33.0859,-0.665522,-2.00275,0.0061308,-2.06824,0.271778,0.559953,-0.404067,-0.789129,0.266658,0.360241,0.110149,0.521046,1.74768,0.862512,-1.8147,0.768679,-1.86866,-0.389507,-0.354252,0.572489,2.09612,0.323138,-3.55409,-0.102646,-0.102646,-0.156311,-0.156311,0.266658,0.28453,0.267291,-0.341151,-0.341151,-0.28108,-0.28108,0.360241,-0.00296851,0.224937,-0.665522,-2.06824,0.0061308,-2.00275,0.271778,0,0,0.559953,0.202743,-0.156458,-0.156458,0.30137,0.649903,0.32074,-0.701362,0.297087,-99,-99,-99,-0.477213,0.0520342,1.21849,-0.0418515,-99,-99,-99,0.271778,0.271778,0.271778,0.271778,-99,-99,-99,0.559953,0.559953,0.559953,0.559953,-99,-99,-99,0,-0.703405,-0.318014,-0.701182,-0.0628313,0,-0.0111416,-3.26991,1,1,0,0,2,2,2,2 --17.586,1,0.262988,4,15,0,27.6385,-1.24465,-0.221442,-0.926525,0.990224,0.298244,1.09634,0.34974,0.56161,0.161878,-0.0159348,0.452977,-1.66919,-2.07641,0.173998,0.560305,-0.666461,1.41317,0.644682,0.848927,-0.735013,-1.56805,-0.617409,0.925974,-0.144087,-0.144087,-0.00497742,-0.00497742,0.161878,-0.146106,0.184362,0.675755,0.675755,0.691173,0.691173,-0.0159348,0.407743,0.11712,-1.24465,0.990224,-0.926525,-0.221442,0.298244,0,0,1.09634,0.374924,-0.342706,-0.342706,0.942982,-0.841113,0.0704831,0.243638,-0.289798,-99,-99,-99,1.42069,-0.605716,-1.4145,-0.171606,-99,-99,-99,0.298244,0.298244,0.298244,0.298244,-99,-99,-99,1.09634,1.09634,1.09634,1.09634,-99,-99,-99,0,-0.388079,-1.26603,-0.913838,-0.270194,0,-1.82508,-0.510449,0,1,1,1,2,2,1,1 --11.6236,0.994102,0.262988,4,15,0,23.9147,-0.516361,-1.03896,0.270683,0.0921275,0.656645,0.439495,-0.386842,0.0791343,-0.627425,0.518687,0.736135,-0.95248,0.5761,0.492786,0.706561,1.00818,0.855218,0.7011,-0.704938,1.31591,0.209748,0.101509,-0.465407,-0.775504,-0.775504,-0.572087,-0.572087,-0.627425,-0.903469,-0.575374,0.252986,0.252986,0.217717,0.217717,0.518687,0.608321,0.563715,-0.516361,0.0921275,0.270683,-1.03896,0.656645,0,0,0.439495,0.676781,0.0781035,0.0781035,0.218433,0.353429,0.302317,0.454332,0.648276,-99,-99,-99,-0.268623,0.572151,0.126155,0.0952821,-99,-99,-99,0.656645,0.656645,0.656645,0.656645,-99,-99,-99,0.439495,0.439495,0.439495,0.439495,-99,-99,-99,0,-0.453556,-1.0846,-1.2171,-0.493338,0,-0.368233,-1.15625,1,0,1,1,2,1,1,1 --13.2517,0.983781,0.262988,4,15,0,19.8597,-1.35844,-0.683179,-1.32653,0.0278634,1.5876,0.875722,0.0442005,-0.188471,1.01147,-1.19796,-1.11169,0.935955,-1.10862,0.238341,-1.74085,-1.34975,-0.946111,0.10785,0.796567,-0.468691,-0.273009,-1.26126,-0.790346,1.04391,1.04391,0.929766,0.929766,1.01147,1.45012,0.892881,-0.708966,-0.708966,-0.760571,-0.760571,-1.19796,-1.09064,-1.26909,-1.35844,0.0278634,-1.32653,-0.683179,1.5876,0,0,0.875722,0.967933,-0.392812,-0.392812,0.624896,-0.870558,0.18716,-1.41716,-1.09878,-99,-99,-99,0.733855,-0.344472,0.243609,-0.48682,-99,-99,-99,1.5876,1.5876,1.5876,1.5876,-99,-99,-99,0.875722,0.875722,0.875722,0.875722,-99,-99,-99,0,-0.987706,-0.107327,-0.14477,-0.366402,0,-0.565768,-0.490512,0,0,0,0,2,1,1,1 --14.4695,0.967801,0.262988,4,15,0,21.3443,-0.676551,-1.55864,0.480344,0.303483,0.403539,1.20736,-0.305769,-0.347796,-1.20186,1.28331,0.112527,-1.03853,0.334592,0.357584,1.4915,1.10162,0.84506,-0.465068,0.365795,-0.955609,1.55743,0.584756,-0.954398,-0.874966,-0.874966,-0.683447,-0.683447,-1.20186,-1.2986,-1.18735,-0.114256,-0.114256,-0.0124671,-0.0124671,1.28331,0.726544,1.34302,-0.676551,0.303483,0.480344,-1.55864,0.403539,0,0,1.20736,0.400169,0.143574,0.143574,0.415267,0.158396,0.16928,0.740688,0.547068,-99,-99,-99,0.266864,-0.505692,1.17717,0.533749,-99,-99,-99,0.403539,0.403539,0.403539,0.403539,-99,-99,-99,1.20736,1.20736,1.20736,1.20736,-99,-99,-99,0,-0.666013,-1.12213,-0.995755,-0.28559,0,-0.113453,-1.68157,1,0,0,0,2,2,2,2 --18.483,0.949328,0.262988,4,15,0,26.6411,-1.27741,-0.593186,-1.05417,-0.267009,1.69512,0.198898,-1.63427,-0.341144,1.11762,-1.30179,-1.71506,0.842261,1.02616,0.662536,-1.95582,-0.618538,0.936078,0.858449,0.741402,1.67798,0.730779,-0.219917,-0.0185194,0.177444,0.177444,0.00659229,0.00659229,1.11762,1.27379,0.912905,-1.02695,-1.02695,-1.0267,-1.0267,-1.30179,-1.14867,-1.26449,-1.27741,-0.267009,-1.05417,-0.593186,1.69512,0,0,0.198898,0.911895,-0.300999,-0.300999,0.30314,0.813717,0.525373,-1.59378,-0.504041,-99,-99,-99,0.124903,0.47277,0.508267,0.00666746,-99,-99,-99,1.69512,1.69512,1.69512,1.69512,-99,-99,-99,0.198898,0.198898,0.198898,0.198898,-99,-99,-99,0,-0.97506,-0.0702419,-0.195875,-1.22947,0,-0.974025,-0.312854,0,0,0,0,1,1,2,2 --17.9432,0.988796,0.262988,4,15,0,26.8751,-0.370774,-0.301172,0.401618,-0.0493955,0.429984,0.789328,1.6238,-0.666318,-1.1261,1.42057,1.73732,-0.456475,-1.36733,0.0432807,1.64031,0.0408224,-0.974659,-0.450169,-0.54847,-0.988196,-0.458061,0.528663,-0.193581,0.761547,0.761547,0.928768,0.928768,-1.1261,-0.867397,-1.00825,0.358731,0.358731,0.416697,0.416697,1.42057,1.11347,1.33828,-0.370774,-0.0493955,0.401618,-0.301172,0.429984,0,0,0.789328,1.26105,0.628884,0.628884,1.20728,-0.733436,0.0232157,0.936185,0.0232989,-99,-99,-99,-0.581541,-0.695804,-0.0651613,0.401859,-99,-99,-99,0.429984,0.429984,0.429984,0.429984,-99,-99,-99,0.789328,0.789328,0.789328,0.789328,-99,-99,-99,0,-0.520299,-1.5828,-0.937152,-0.707868,0,-0.457282,-1.32215,0,1,1,0,2,2,1,2 --17.9351,0.990532,0.262988,4,15,0,26.6417,-1.51788,-1.08437,0.285368,1.17089,1.49473,1.20927,-0.323118,0.0219282,1.35886,-1.02737,-2.5393,0.306385,1.87366,0.70145,-0.548716,-0.502626,1.77765,1.74116,0.850769,0.576066,0.877805,-1.47211,-1.36278,0.199474,0.199474,0.0437721,0.0437721,1.35886,1.14105,1.07207,0.340282,0.340282,-0.0352705,-0.0352705,-1.02737,0.00870178,-0.840214,-1.51788,1.17089,0.285368,-1.08437,1.49473,0,0,1.20927,0.559058,0.354695,0.354695,0.940582,1.29687,0.485515,-0.387108,-0.354592,-99,-99,-99,1.14446,0.61061,0.465036,-1.23787,-99,-99,-99,1.49473,1.49473,1.49473,1.49473,-99,-99,-99,1.20927,1.20927,1.20927,1.20927,-99,-99,-99,0,-0.363174,-0.504095,-0.517096,-0.0563688,0,-0.154038,-0.738973,1,1,0,0,1,2,2,1 --18.6249,0.971328,0.262988,4,15,0,26.76,-0.502586,-1.06345,-0.155849,-1.17588,1.38519,0.284468,0.215612,-0.433123,-1.46616,0.820785,2.45641,-0.155553,-1.91786,-0.257917,-0.0466854,-0.181015,-1.78627,-1.65328,-0.45369,-0.534261,-0.470626,0.843879,0.104352,-0.847707,-0.847707,-0.653451,-0.653451,-1.46616,-1.41729,-1.21052,0.687333,0.687333,0.614613,0.614613,0.820785,0.558219,0.740701,-0.502586,-1.17588,-0.155849,-1.06345,1.38519,0,0,0.284468,1.87262,-1.59488,-1.59488,1.89723,-1.73586,-0.233443,-0.0444591,-0.172383,-99,-99,-99,0.486751,-0.118697,-0.176254,0.423162,-99,-99,-99,1.38519,1.38519,1.38519,1.38519,-99,-99,-99,0.284468,0.284468,0.284468,0.284468,-99,-99,-99,0,-0.491736,-1.01832,-0.938512,-0.294981,0,-0.540024,-1.26503,0,0,0,0,2,1,2,2 --16.6775,0.864813,0.262988,4,15,0,34.2928,-1.0064,-1.24246,2.01366,-0.012607,0.501255,0.283058,0.414566,-0.623495,-1.26837,1.33456,0.18763,-0.758664,0.602711,-0.0219458,1.12619,-1.74637,-0.590701,0.964147,1.65305,-0.436424,1.54595,0.290695,-0.34093,0.39321,0.39321,0.352684,0.352684,-1.26837,-0.905755,-1.22444,0.0854289,0.0854289,0.0202327,0.0202327,1.33456,1.19333,1.2907,-1.0064,-0.012607,2.01366,-1.24246,0.501255,0,0,0.283058,0.446317,0.0985987,0.0985987,0.11291,0.309768,-0.0112792,0.608607,-0.943756,-99,-99,-99,0.555818,-0.130749,0.600243,-0.133535,-99,-99,-99,0.501255,0.501255,0.501255,0.501255,-99,-99,-99,0.283058,0.283058,0.283058,0.283058,-99,-99,-99,0,-0.656759,-1.0562,-0.334411,-0.318086,0,-0.323789,-0.813424,1,1,0,1,2,1,2,2 --18.0888,0.989327,0.262988,4,15,0,27.4062,-0.262544,-0.942375,0.188522,0.732165,1.52722,2.43836,0.704128,-1.03166,-1.01628,0.67811,-1.12592,-1.35617,1.04055,2.60354,-0.347581,1.04993,0.845415,-0.673683,1.50158,-0.254649,-0.279176,0.0156066,-0.193862,-0.757436,-0.757436,-0.274596,-0.274596,-1.01628,-1.49383,-1.12827,-1.0861,-1.0861,-0.64342,-0.64342,0.67811,-0.0692796,0.773421,-0.262544,0.732165,0.188522,-0.942375,1.52722,0,0,2.43836,6.10772,4.45535,4.45535,4.75525,1.08434,2.71312,-0.386207,1.1666,-99,-99,-99,1.86614,0.581656,-0.425378,0.416299,-99,-99,-99,1.52722,1.52722,1.52722,1.52722,-99,-99,-99,2.43836,2.43836,2.43836,2.43836,-99,-99,-99,0,-0.179412,-0.30538,-0.98857,-0.320348,0,-1.2234,-0.676656,0,1,1,1,2,2,1,2 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UTC # method = sample (Default) # sample # num_samples = 50 @@ -31,77 +31,77 @@ # num_chains = 1 (Default) # id = 1 (Default) # data -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/standata-158c1785570c.json +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/standata-51505165d30.json # init = 2 (Default) # random # seed = 1 # output -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_f7fa7e85dd3a32aa7e998a96e88c67db-202408091531-1-18f7fc.csv +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_5042ae4e67582b4f8ab6560b7bfe8a01-202408092111-1-2243f6.csv # diagnostic_file = (Default) # refresh = 100 (Default) # sig_figs = -1 (Default) -# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_f7fa7e85dd3a32aa7e998a96e88c67db-profile-202408091531-1-11d5ad.csv +# profile_file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_5042ae4e67582b4f8ab6560b7bfe8a01-profile-202408092111-1-337851.csv # save_cmdstan_config = 0 (Default) # num_threads = 1 (Default) # stanc_version = stanc3 v2.34.0 -# stancflags = --name=model_f7fa7e85dd3a32aa7e998a96e88c67db_model +# stancflags = --name=model_5042ae4e67582b4f8ab6560b7bfe8a01_model lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_offdiag.1,A_offdiag.2,Q_diag.1,Q_diag.2,b.1,b.2,eta_anc.1,eta_anc.2,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.7.1,z_drift.8.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,z_drift.7.2,z_drift.8.2,c2.1,c2.2,c2.3,group_z.1.1,group_z.2.1,group_z.1.2,group_z.2.2,group_z.1.3,group_z.2.3,group_z.1.4,group_z.2.4,group_z.1.5,group_z.2.5,sigma_group.1,sigma_group.2,L_group.1.1,L_group.2.1,L_group.1.2,L_group.2.2,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.8.1,eta.9.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,eta.8.2,eta.9.2,A.1.1,A.2.1,A.1.2,A.2.2,Q.1.1,Q.2.1,Q.1.2,Q.2.2,Q_inf.1.1,Q_inf.2.1,Q_inf.1.2,Q_inf.2.2,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.8.1,drift_tips.9.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,drift_tips.8.2,drift_tips.9.2,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.8.1,sigma_tips.9.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,sigma_tips.8.2,sigma_tips.9.2,group_v.1.1,group_v.2.1,group_v.3.1,group_v.4.1,group_v.5.1,group_v.1.2,group_v.2.2,group_v.3.2,group_v.4.2,group_v.5.2,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,log_lik.9,log_lik.10,log_lik.11,log_lik.12,log_lik.13,log_lik.14,log_lik.15,log_lik.16,log_lik.17,log_lik.18,log_lik.19,log_lik.20,log_lik.21,log_lik.22,log_lik.23,log_lik.24,log_lik.25,log_lik.26,log_lik.27,log_lik.28,log_lik.29,log_lik.30,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.5.1,yrep.6.1,yrep.7.1,yrep.8.1,yrep.9.1,yrep.10.1,yrep.11.1,yrep.12.1,yrep.13.1,yrep.14.1,yrep.15.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2,yrep.5.2,yrep.6.2,yrep.7.2,yrep.8.2,yrep.9.2,yrep.10.2,yrep.11.2,yrep.12.2,yrep.13.2,yrep.14.2,yrep.15.2,cor_group.1.1,cor_group.2.1,cor_group.1.2,cor_group.2.2 # Adaptation terminated -# Step size = 0.121652 +# Step size = 0.184676 # Diagonal elements of inverse mass matrix: # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 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--51.3017,0.984587,0.121652,5,31,0,71.2069,-0.298805,-0.00866066,-0.687244,0.497533,0.560943,2.06568,0.561446,0.398841,-1.68338,-0.667085,-0.283607,0.264728,-0.528104,-0.843539,0.977753,-0.10583,0.719667,0.0789661,0.0589538,0.165689,-2.03342,1.06758,0.785104,0.308099,-1.0116,-1.34015,-5.50547,-0.590571,1.54194,0.394272,-0.508311,1.08411,1.3942,0.0662569,-1.28629,-1.04932,-0.193636,-0.800171,0.808682,0.336986,0.849619,1,0.374296,0,0.927309,-0.309542,-0.543402,-0.543402,-0.517824,-0.517824,-1.68338,-0.684783,-0.677038,-0.717936,-0.742379,-0.96952,-0.96952,-0.534432,-0.534432,-0.667085,-0.557567,-0.549689,-0.981047,-0.298805,0.497533,-0.687244,-0.00866066,0.560943,0,0,2.06568,5.51763,-1.99088,-1.99088,4.88521,-0.666389,0.252937,-0.0273774,0.200548,0.0220054,-99,-99,-99,-99,1.82502,0.361951,0.15818,-0.577274,-0.728543,-99,-99,-99,-99,0.560943,0.560943,0.560943,0.560943,0.560943,-99,-99,-99,-99,2.06568,2.06568,2.06568,2.06568,2.06568,-99,-99,-99,-99,0.132864,0.365329,0.0223277,-0.353607,-0.269646,-0.275095,1.44319,-0.992347,-0.486251,0.382667,-0.170296,-0.170296,-0.170296,-0.394437,-1.12104,-1.12104,-0.128216,-0.128216,-0.128216,-0.364901,-0.364901,-0.364901,-0.760131,-0.760131,-0.760131,-1.1263,-1.62808,-0.738871,-1.10741,-0.74256,-1.10741,-0.292005,-1.63418,-0.292005,-3.18227,-1.48722,-0.338535,-0.575853,-1.05965,-4.63492,0,0,0,1,1,1,0,0,1,0,0,0,1,0,1,3,2,3,3,4,3,2,2,2,3,2,2,3,3,2,1,0.374296,0.374296,1 --59.5056,0.998784,0.121652,5,31,0,74.5652,-0.199144,-0.00619311,1.57526,0.234619,1.19863,0.721114,1.36434,-0.582485,-1.37712,-0.7424,-0.823353,-0.0721827,-0.492595,-0.917952,1.24533,0.44427,-2.07423,1.01444,0.54933,0.378612,-0.160303,1.04336,-0.248669,-0.331019,0.212858,-2.09411,-4.92327,-1.9944,-0.693842,3.13565,-0.931207,0.167908,0.583003,-0.46338,-0.356913,-2.36652,-0.477268,-1.81365,-0.0174255,0.244572,1.15581,1,-0.135908,0,0.990721,-1.60893,-2.60352,-2.60352,-2.5229,-2.5229,-1.37712,-2.42964,-2.4328,-2.51968,-1.64122,-1.69515,-1.69515,-1.66153,-1.66153,-0.7424,-1.50585,-1.49653,-1.55104,-0.199144,0.234619,1.57526,-0.00619311,1.19863,0,0,0.721114,-8.91057,-1.50693,-1.50693,1.13071,-1.18418,0.474325,0.169216,-0.852225,0.416796,-99,-99,-99,-99,0.231517,-0.0391329,-0.0858242,-0.00433666,-0.63398,-99,-99,-99,-99,1.19863,1.19863,1.19863,1.19863,1.19863,-99,-99,-99,-99,0.721114,0.721114,0.721114,0.721114,0.721114,-99,-99,-99,-99,0.766893,0.0410655,-0.11333,-0.578786,-0.443569,-1.55887,0.641212,-0.335906,-0.17477,0.264941,-0.0123949,-0.0123949,-0.0123949,-1.02437,-0.444761,-0.444761,-0.144435,-0.144435,-0.144435,-0.0657701,-0.0657701,-0.0657701,-1.29853,-1.29853,-1.29853,-2.3726,-0.507651,-1.71029,-0.912551,-1.17207,-0.912551,-0.747391,-1.29014,-0.747391,-1.42265,-1.21305,-0.87274,-0.784111,-1.26263,-2.94665,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,2,2,3,4,2,4,4,2,3,2,3,4,4,2,2,1,-0.135908,-0.135908,1 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    ev&7Jy}zMx ztY=1HBi(%PtwU9*LHsQT1$E=nyJb~>=Hz4Z#R3vy=(ttorO7q^!2hyyFe($hd%m~J zBB-UAutdaRx*k6!YL96|tyE$y%&ODMIgUfEzGNT&Pb;kaOR#*m5>Uf%qN{YD%k38;^E{U&76q6qNTp-#u08gx zoW4XhwNC0owPjPPfV5=R{$BGKNz6N@n+lZfuN#d;lPMb5{l@La%>I6Kik+8uwRgle z9vImIn?TV#Y%2#$S0z5FxSMl6_Nu$&F5XS{~8SZpL ziqN16OT$jrMMoU(o5YxCv_Xv0Z|goH-#LdTFkz*4_7P|nJVQmVCTF>(*tn(0?xJc( z$KO>|FCRH2YjN`WLh%DfGjb}%0uOb2DJ_ax$_oXjC}I73hg?>|aZ$mU#6d;pq425_ zdT=owtK$e@V^u!fMuJ!re(#kZblO|wLV$b(8hEI3NIq z7(v0uWQ(`5DqsV($j1-2|NTmY6(*UKo5aUZstd}ZHIG9&A5S}P1Fgm~Z9J%^UCgVS z6Lh}yY-=w7Q{h&9s%x-2sc@eP+HY-a2&}>X@s0wxj&mVhkBeY|bb-tH&O;pETcDh% zmgG@L=;f$iUpvj`mh@v>vhyj{&F5uD|K-qI@iez{@fqO!_$T!K=kfgo*Y^_W{OC;I z-6YbZ+EMI^d^AnXXfVkmw)TcisS6MHIr4e<3S~cu)3Ge`vDY3|hinlwa~n>r<@q*y zy#?$E-j9!@uNALp0Hyn^DgW@>+MIW^4zAbn(+0iy;uBN1iQ0TVo(F}iZ!lNDr@R*5 zpUem!tzk*`y~FF;ZJ9QmkH@#e#+~ZC_L|b}F8}@peQPsIM1YB(&sH+ zSTR2#3kr;c-#0ef=0-}l;jL%s7F_zq+ky?jWxA Date: Fri, 9 Aug 2024 23:37:58 +0100 Subject: [PATCH 12/12] Update README and vignettes --- README.md | 66 +- .../README-authority-delta-theta-1.png | Bin 10994 -> 10958 bytes vignettes/authority-delta-theta-1.png | Bin 10994 -> 10958 bytes .../authority-delta-theta-with-distance-1.png | Bin 10874 -> 10963 bytes vignettes/authority-flowfield-1.png | Bin 16080 -> 15857 bytes vignettes/authority-selection-gradient-1.png | Bin 24406 -> 22739 bytes vignettes/coevolve.Rmd | 606 +++++++++++++++++- vignettes/primates-delta-theta-1.png | Bin 10182 -> 10199 bytes 8 files changed, 638 insertions(+), 34 deletions(-) diff --git a/README.md b/README.md index 3ec3395..98cb3f6 100644 --- a/README.md +++ b/README.md @@ -76,15 +76,15 @@ fit <- ) #> Running MCMC with 4 parallel chains... #> -#> Chain 4 finished in 4944.0 seconds. -#> Chain 3 finished in 5209.1 seconds. -#> Chain 2 finished in 5267.9 seconds. -#> Chain 1 finished in 5364.4 seconds. +#> Chain 4 finished in 1223.3 seconds. +#> Chain 3 finished in 1464.1 seconds. +#> Chain 2 finished in 1510.5 seconds. +#> Chain 1 finished in 1517.7 seconds. #> #> All 4 chains finished successfully. -#> Mean chain execution time: 5196.4 seconds. -#> Total execution time: 5364.8 seconds. -#> Warning: 14 of 2000 (1.0%) transitions ended with a divergence. +#> Mean chain execution time: 1428.9 seconds. +#> Total execution time: 1518.0 seconds. +#> Warning: 61 of 2000 (3.0%) transitions ended with a divergence. #> See https://mc-stan.org/misc/warnings for details. ``` @@ -100,36 +100,36 @@ summary(fit) #> #> Autoregressive selection effects: #> Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS -#> political_authority -0.64 0.51 -1.90 -0.02 1.00 1720 1149 -#> religious_authority -0.67 0.52 -1.97 -0.03 1.00 1334 1183 +#> political_authority -0.61 0.51 -1.91 -0.03 1.01 1589 964 +#> religious_authority -0.68 0.53 -1.98 -0.03 1.01 1170 665 #> #> Cross selection effects: #> Estimate Est.Error 2.5% 97.5% Rhat -#> political_authority ⟶ religious_authority 2.85 1.08 0.82 5.12 1.00 -#> religious_authority ⟶ political_authority 2.56 1.06 0.54 4.73 1.00 +#> political_authority ⟶ religious_authority 2.99 1.20 0.84 5.58 1.02 +#> religious_authority ⟶ political_authority 2.43 1.08 0.65 4.88 1.01 #> Bulk_ESS Tail_ESS -#> political_authority ⟶ religious_authority 780 889 -#> religious_authority ⟶ political_authority 846 1032 +#> political_authority ⟶ religious_authority 189 74 +#> religious_authority ⟶ political_authority 666 833 #> #> Drift scale parameters: #> Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS -#> political_authority 1.47 0.78 0.11 3.14 1.00 811 733 -#> religious_authority 1.20 0.74 0.06 2.81 1.00 675 661 +#> political_authority 1.52 0.74 0.23 3.05 1.01 772 685 +#> religious_authority 1.18 0.71 0.09 2.72 1.01 668 972 #> #> Continuous time intercept parameters: #> Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS -#> political_authority 0.11 0.95 -1.74 2.00 1.01 3925 1345 -#> religious_authority 0.14 0.94 -1.74 2.00 1.00 3321 1421 +#> political_authority 0.14 0.93 -1.61 1.91 1.01 2427 1288 +#> religious_authority 0.15 0.91 -1.69 1.93 1.01 2063 1300 #> #> Ordinal cutpoint parameters: #> Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS -#> political_authority[1] -1.00 0.85 -2.61 0.66 1.00 2427 1263 -#> political_authority[2] -0.31 0.84 -1.88 1.32 1.00 2406 1263 -#> political_authority[3] 1.64 0.86 -0.01 3.30 1.00 2171 1532 -#> religious_authority[1] -1.33 0.88 -3.03 0.39 1.00 2391 1467 -#> religious_authority[2] -0.69 0.88 -2.44 1.03 1.00 2508 1317 -#> religious_authority[3] 1.59 0.91 -0.15 3.42 1.00 2336 1398 -#> Warning: There were 14 divergent transitions after warmup. +#> political_authority[1] -1.03 0.84 -2.67 0.73 1.00 1590 1088 +#> political_authority[2] -0.33 0.83 -1.95 1.42 1.00 1693 1288 +#> political_authority[3] 1.63 0.86 0.02 3.40 1.01 1640 1397 +#> religious_authority[1] -1.32 0.87 -3.00 0.35 1.00 1428 1415 +#> religious_authority[2] -0.68 0.84 -2.23 0.94 1.00 1479 1532 +#> religious_authority[3] 1.64 0.92 -0.09 3.44 1.01 770 714 +#> Warning: There were 61 divergent transitions after warmup. #> http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup ``` @@ -170,12 +170,12 @@ evolutionary time. When using the **coevolve** package, please cite the following papers: -- Ringen, E., Martin, J. S., & Jaeggi, A. (2021). Novel phylogenetic - methods reveal that resource-use intensification drives the evolution - of “complex” societies. *EcoEvoRXiv*. - -- Sheehan, O., Watts, J., Gray, R. D., Bulbulia, J., Claessens, S., - Ringen, E. J., & Atkinson, Q. D. (2023). Coevolution of religious and - political authority in Austronesian societies. *Nature Human - Behaviour*, *7*(1), 38-45. - +- Ringen, E., Martin, J. S., & Jaeggi, A. (2021). Novel phylogenetic + methods reveal that resource-use intensification drives the + evolution of “complex” societies. *EcoEvoRXiv*. + +- Sheehan, O., Watts, J., Gray, R. D., Bulbulia, J., Claessens, S., + Ringen, E. J., & Atkinson, Q. D. (2023). 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list( + political_authority = "ordered_logistic", + religious_authority = "ordered_logistic" + ), + id = "language", + tree = authority$phylogeny, + # set manual prior + prior = list(A_offdiag = "normal(0, 2)"), + # additional arguments for cmdstanr + parallel_chains = 4, + iter_sampling = 500, + refresh = 0, + seed = 1 + ) +``` + +``` +Running MCMC with 4 parallel chains... + +Chain 4 finished in 1250.9 seconds. +Chain 3 finished in 1496.0 seconds. +Chain 2 finished in 1540.3 seconds. +Chain 1 finished in 1550.7 seconds. + +All 4 chains finished successfully. +Mean chain execution time: 1459.5 seconds. +Total execution time: 1550.9 seconds. +``` + +The function takes several arguments, including a dataset, a named list of +variables that we would like to coevolve in the model (along with their +associated response distributions), the column in the dataset that links to +the phylogeny tip labels, and a phylogeny of class `phylo`. The function sets +priors for the parameters by default, but it is possible for the user to +manually set these priors. The user can also pass additional arguments to +cmdstanr's `sample()` method which runs under the hood. + +Once the model has fitted, we can print a summary of the parameters. + + +``` r +summary(fit1) +``` + +``` +Variables: political_authority = ordered_logistic + religious_authority = ordered_logistic + Data: authority$data (Number of observations: 97) + Draws: 4 chains, each with iter = 500; warmup = 1000; thin = 1 + total post-warmup draws = 2000 + +Autoregressive selection effects: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +political_authority -0.61 0.51 -1.91 -0.03 1.01 1589 964 +religious_authority -0.68 0.53 -1.98 -0.03 1.01 1170 665 + +Cross selection effects: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +political_authority ⟶ religious_authority 2.99 1.20 0.84 5.58 1.02 189 74 +religious_authority ⟶ political_authority 2.43 1.08 0.65 4.88 1.01 666 833 + +Drift scale parameters: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +political_authority 1.52 0.74 0.23 3.05 1.01 772 685 +religious_authority 1.18 0.71 0.09 2.72 1.01 668 972 + +Continuous time intercept parameters: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +political_authority 0.14 0.93 -1.61 1.91 1.01 2427 1288 +religious_authority 0.15 0.91 -1.69 1.93 1.01 2063 1300 + +Ordinal cutpoint parameters: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +political_authority[1] -1.03 0.84 -2.67 0.73 1.00 1590 1088 +political_authority[2] -0.33 0.83 -1.95 1.42 1.00 1693 1288 +political_authority[3] 1.63 0.86 0.02 3.40 1.01 1640 1397 +religious_authority[1] -1.32 0.87 -3.00 0.35 1.00 1428 1415 +religious_authority[2] -0.68 0.84 -2.23 0.94 1.00 1479 1532 +religious_authority[3] 1.64 0.92 -0.09 3.44 1.01 770 714 +``` + +We can see a printed summary of the model parameters, including the +autoregressive effects (i.e., the effects of variables on themselves in the +future), the cross effects (i.e., the effects of variables on the other +variables in the future), the amount of drift, the continuous time intercepts +for the stochastic differential equation, and the ordinal cutpoints for both +variables. + +While the summary output is useful, it is difficult to interpret the +parameters directly to make inferences about coevolutionary patterns. An +alternative approach is to directly "intervene" in the system. By doing this, +we can better understand how increases or decreases in a variable change the +optimal trait values of other variables in the system. For example, we can hold +one variable at its average value and then increase it by a standardised amount +to see how the optimal value for the other trait changes. + +The function `coev_calculate_delta_theta()` allows the user to calculate +$\Delta\theta_{z}$, which is defined as the change in the optimal trait value +for one variable which results from a median absolute deviation increase in +another variable. This function returns a posterior distribution. We can easily +visualise the posterior distributions for all cross effects at once using the +function `coev_plot_delta_theta()`. + + +``` r +coev_plot_delta_theta(fit1) +``` + +

    + +This plot shows the posterior distribution, the posterior median, and the 50% +and 89% credible intervals for $\Delta\theta_{z}$. We can conclude that +political and religious authority both influence each other in their evolution. +A one median absolute deviation increase in political authority results in an +increase in the optimal trait value for religious authority, and vice versa. In +other words, these two variables coevolve reciprocally over time. + +One way of visualising this runaway coevolutionary process is by plotting a flow +field of evolutionary change. This plot visualises the strength and direction +of evolutionary change at different locations in trait space. + + +``` r +coev_plot_flowfield( + object = fit1, + var1 = "political_authority", + var2 = "religious_authority" + ) +``` + +
    +plot of chunk authority-flowfield +
    + +The arrows in this plot tend to point towards the upper right-hand corner, +suggesting that political and religious authority evolve towards higher levels +in a runaway coevolutionary process. + +Another way of visualising coevolutionary dynamics is with a selection gradient +plot. This heatmap shows how selection acts on both variables at different +locations in trait space, with green indicating positive selection and red +indicating negative selection. + + +``` r +coev_plot_selection_gradient( + object = fit1, + var1 = "political_authority", + var2 = "religious_authority" +) +``` +
    +plot of chunk authority-selection-gradient +
    +We can see from this plot that as each variable increases, the selection on the +other variable increases. +## Available response distributions +In the above example, both variables were ordinal. As such, we declared both of +them to follow the "ordered_logistic" response distribution. But the +**coevolve** package supports several more response distributions. +| Response distribution | Data type | Link function | +|----------------------------|-----------------|----------------| +| bernoulli_logit | Binary | Logit | +| ordered_logistic | Ordinal | Logit | +| poisson_softplus | Count | Softplus | +| negative_binomial_softplus | Count | Softplus | +| normal | Continuous real | - | +| student_t | Continuous real | - | +| lognormal | Positive real | - | +Different variables need not follow the same response distribution. This can be +useful when users would like to assess the coevolution between variables of +different types. +## Handling missing data +Often in comparative datasets, data will be missing for some taxa. Rather +than remove cases if they have any missing data, the `coev_fit()` function will +only remove cases if they have missing data for *all* coevolving variables. If +data are missing for some variables and not others, the function will impute the +missing values in the coevolutionary process. +We show this by modelling the coevolutionary relationships between brain weight +and group size across 21 primate species from the Lemuriformes clade. Data on +primate species were compiled by DeCasien et al. (2017). There are data for 143 +primate species in total, but we focus on one clade to keep the example small +and simple. +``` r +# filter dataset to Lemuriformes only +primates_data <- primates$data[primates$data$clade == "Lemuriformes",] + +# prune phylogeny to Lemuriformes only +library(ape) +primates_phylogeny <- keep.tip(primates$phylogeny, primates_data$species) + +# view data +head(primates_data[, c("species", "brain_weight", "group_size")]) +``` + +``` + species brain_weight group_size +13 Avahi_laniger 10.251355 2.666667 +14 Avahi_occidentalis 8.236200 NA +44 Cheirogaleus_major 6.119797 5.500000 +45 Cheirogaleus_medius 2.912291 2.000000 +54 Daubentonia_madagascariensis 46.344725 1.750000 +56 Eulemur_coronatus 21.394398 6.950000 +``` + +Both variables are positive reals and are lognormally distributed. While there +are no missing data for the brain weight variable, some data are missing for the +group size variable. + +In this case, the model will retain all available data and impute the missing +group size values. +``` r +fit2 <- + coev_fit( + data = primates_data, + variables = list( + brain_weight = "lognormal", + group_size = "lognormal" + ), + id = "species", + tree = primates_phylogeny, + # additional arguments for cmdstanr + parallel_chains = 4, + iter_sampling = 500, + refresh = 0, + seed = 1 + ) +``` +``` +Running MCMC with 4 parallel chains... +Chain 3 finished in 150.8 seconds. +Chain 1 finished in 163.0 seconds. +Chain 2 finished in 166.9 seconds. +Chain 4 finished in 172.2 seconds. +All 4 chains finished successfully. +Mean chain execution time: 163.2 seconds. +Total execution time: 172.4 seconds. +``` +``` r +summary(fit2) +``` +``` +Variables: brain_weight = lognormal + group_size = lognormal + Data: primates_data (Number of observations: 21) + Draws: 4 chains, each with iter = 500; warmup = 1000; thin = 1 + total post-warmup draws = 2000 + +Autoregressive selection effects: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +brain_weight -0.28 0.26 -0.98 -0.01 1.00 1662 1230 +group_size -0.86 0.57 -2.22 -0.06 1.00 1246 1111 + +Cross selection effects: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +brain_weight ⟶ group_size 0.54 0.58 -0.61 1.63 1.00 854 1275 +group_size ⟶ brain_weight 1.68 0.92 -0.32 3.24 1.00 541 1080 + +Drift scale parameters: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +brain_weight 0.55 0.14 0.32 0.86 1.00 614 573 +group_size 0.65 0.14 0.44 0.98 1.00 1597 845 + +Continuous time intercept parameters: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +brain_weight 0.82 0.87 -0.88 2.54 1.00 1291 1425 +group_size 0.54 0.86 -1.18 2.21 1.00 1459 1512 +``` +Notice that the number of observations is still 21 in the summary output, +informing us that all observations were retained and any missing data were +imputed. +We can then use plotting functions as usual. +``` r +coev_plot_delta_theta(fit2) +``` + +
    +plot of chunk primates-delta-theta +
    + +## Repeated observations + +Another common feature of comparative datasets is repeated observations. In the +previous examples, we had only one observation per taxon. But often there will +be more than one observation for each taxon, such as when we have observed +multiple individuals of the same species. In these cases, it can be useful to +include all of these observations in the model and estimate the between-taxa +variation that is not due to the coevolutionary process. + +We show this using an example dataset from de Villemeruil & Nakagawa (2014). +Suppose we have measured two continuous variables ($x$ and $y$) for 20 species, +with five observations for each species. + + +``` r +head(repeated$data) +``` + +``` + species x y +1 sp_1 11.223724 107.41919 +2 sp_1 9.805934 109.16403 +3 sp_1 10.308423 91.88672 +4 sp_1 8.355349 121.54341 +5 sp_1 11.854510 105.31638 +6 sp_2 4.314015 64.99859 +``` + +We can fit the dynamic coevolutionary model to this dataset. + + +``` r +fit3 <- + coev_fit( + data = repeated$data, + variables = list( + x = "normal", + y = "normal" + ), + id = "species", + tree = repeated$phylogeny, + # additional arguments for cmdstanr + parallel_chains = 4, + iter_warmup = 2000, + iter_sampling = 2000, + refresh = 0, + seed = 1 + ) +``` + +``` +Running MCMC with 4 parallel chains... + +Chain 1 finished in 458.8 seconds. +Chain 4 finished in 461.2 seconds. +Chain 2 finished in 462.1 seconds. +Chain 3 finished in 472.9 seconds. + +All 4 chains finished successfully. +Mean chain execution time: 463.8 seconds. +Total execution time: 473.1 seconds. +``` + +``` r +summary(fit3) +``` + +``` +Variables: x = normal + y = normal + Data: repeated$data (Number of observations: 100) + Draws: 4 chains, each with iter = 2000; warmup = 2000; thin = 1 + total post-warmup draws = 8000 + +Autoregressive selection effects: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +x -0.76 0.59 -2.17 -0.03 1.00 4184 3203 +y -0.52 0.51 -1.85 -0.01 1.00 4104 3636 + +Cross selection effects: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +x ⟶ y -0.72 1.29 -3.12 1.87 1.00 1194 3599 +y ⟶ x -0.40 0.82 -1.89 1.34 1.00 1279 3583 + +Drift scale parameters: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +x 1.34 0.10 1.15 1.56 1.00 9653 5523 +y 6.43 0.36 5.77 7.18 1.00 11545 5430 + +Continuous time intercept parameters: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +x -0.44 1.01 -2.42 1.54 1.00 3440 5478 +y 0.69 1.02 -1.33 2.64 1.00 10566 5961 + +Group-level hyperparameters: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +sd(x) 6.55 0.98 4.90 8.63 1.00 878 1844 +sd(y) 47.74 3.62 41.00 55.10 1.00 1502 2663 +cor(x,y) 0.91 0.06 0.76 0.98 1.01 648 1662 +``` + +In the model output, we can see that `coev_fit()` has detected the presence of +repeated observations and has consequently modelled additional standard +deviation and correlation parameters for $x$ and $y$. These parameters represent +the between-taxa variation and correlation that remains after accounting for +the coevolutionary process. + +In this case, it appears that there is a high correlation between $x$ and $y$, +but this is due to species-specific effects rather than the coevolutionary +process. + + +``` r +# 89% credible interval for delta theta x -> y +dt_xy <- coev_calculate_delta_theta(fit3, predictor = "x", response = "y") +quantile(dt_xy, c(0.055, 0.945)) +``` + +``` + 5.5% 94.5% +-19.829244 2.929213 +``` + +``` r +# 89% credible interval for delta theta y -> x +dt_yx <- coev_calculate_delta_theta(fit3, predictor = "y", response = "x") +quantile(dt_yx, c(0.055, 0.945)) +``` + +``` + 5.5% 94.5% +-20.868104 6.443599 +``` + +## Controlling for spatial location + +If we have data on the spatial location of taxa, sometimes it is useful to +control for this spatial location to ensure that our model is capturing deep +ancestral relationships rather than more recent diffusion among neighbours. +For example, when studying the coevolution of political and religious authority +in Austronesian societies, we would like to ensure that our results are due to +coevolution over deep cultural time rather than more recent borrowing among +societies with close geographic proximity. + +The `dist_mat` argument in the `coev_fit()` function allows us to easily control +for spatial proximity. This argument takes a distance matrix between all taxa in +the phylogeny. If the distance matrix is specified by the user, the function +includes in the Stan code a separate Gaussian Process over spatial location for +each variable in the model. + +Here is the geographic distance matrix (measured in metres) for the first five +Austronesian societies: + + +``` r +authority$distance_matrix[1:5, 1:5] +``` + +``` + Aiwoo Alune AnejomAneityum Anuta Atoni +Aiwoo 0.0 4250625.9 1158010.2 408692.9 4602028.5 +Alune 4250625.9 0.0 4863176.7 4649868.1 865094.2 +AnejomAneityum 1158010.2 4863176.7 0.0 951671.2 5013641.1 +Anuta 408692.9 4649868.1 951671.2 0.0 4977783.9 +Atoni 4602028.5 865094.2 5013641.1 4977783.9 0.0 +``` + +We can include this distance matrix in the model. The matrix is standardised to +vary between 0 and 1 under the hood to improve model sampling. + + +``` r +fit4 <- + coev_fit( + data = authority$data, + variables = list( + political_authority = "ordered_logistic", + religious_authority = "ordered_logistic" + ), + id = "language", + tree = authority$phylogeny, + dist_mat = authority$distance_matrix, + # set manual prior + prior = list(A_offdiag = "normal(0, 2)"), + # additional arguments for cmdstanr + parallel_chains = 4, + iter_sampling = 500, + refresh = 0, + seed = 1 + ) +``` + +``` +Running MCMC with 4 parallel chains... + +Chain 1 finished in 2228.2 seconds. +Chain 4 finished in 2240.1 seconds. +Chain 3 finished in 2294.8 seconds. +Chain 2 finished in 2571.2 seconds. + +All 4 chains finished successfully. +Mean chain execution time: 2333.6 seconds. +Total execution time: 2571.4 seconds. +``` + +``` r +summary(fit4) +``` + +``` +Variables: political_authority = ordered_logistic + religious_authority = ordered_logistic + Data: authority$data (Number of observations: 97) + Draws: 4 chains, each with iter = 500; warmup = 1000; thin = 1 + total post-warmup draws = 2000 + +Autoregressive selection effects: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +political_authority -0.66 0.53 -1.96 -0.02 1.00 1109 941 +religious_authority -0.70 0.54 -1.99 -0.03 1.00 1398 1046 + +Cross selection effects: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +political_authority ⟶ religious_authority 2.44 1.16 0.19 4.73 1.00 500 464 +religious_authority ⟶ political_authority 2.32 1.12 0.23 4.70 1.01 500 509 + +Drift scale parameters: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +political_authority 1.27 0.76 0.07 2.96 1.00 487 504 +religious_authority 1.12 0.69 0.09 2.63 1.01 622 878 + +Continuous time intercept parameters: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +political_authority 0.16 0.95 -1.68 1.95 1.00 1770 1283 +religious_authority 0.24 0.95 -1.68 2.10 1.00 1898 1240 + +Ordinal cutpoint parameters: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +political_authority[1] -1.18 1.02 -3.13 0.79 1.00 1858 1550 +political_authority[2] -0.50 1.01 -2.47 1.40 1.00 1913 1438 +political_authority[3] 1.45 1.04 -0.57 3.56 1.00 1799 1564 +religious_authority[1] -1.44 1.06 -3.54 0.62 1.00 1241 1288 +religious_authority[2] -0.81 1.05 -2.83 1.23 1.00 1344 1386 +religious_authority[3] 1.47 1.08 -0.55 3.66 1.00 1419 1521 + +Gaussian Process parameters for distances: + Estimate Est.Error 2.5% 97.5% Rhat Bulk_ESS Tail_ESS +rho(political_authority) 1.87 1.50 0.05 5.54 1.00 593 712 +rho(religious_authority) 1.91 1.47 0.11 5.59 1.01 527 881 +sdgp(political_authority) 1.77 1.42 0.06 5.43 1.00 672 964 +sdgp(religious_authority) 1.97 1.51 0.05 5.78 1.01 385 553 +``` + +The summary output shows that the model has estimated the parameters for two +Gaussian Process functions over geographic locations, one for each variable. + +Plotting $\Delta\theta_{z}$ shows that our inferences are robust to controlling +for geographic location. + + +``` r +coev_plot_delta_theta(fit4) +``` + +
    +plot of chunk authority-delta-theta-with-distance +
    + +## Model comparison + +Since the underlying Stan code for these models returns a log likelihood vector +for all observations, it is possible to compare different models using methods +like approximate leave-one-out cross-validation. For example, we can use the +`loo_compare()` function from the [loo](https://mc-stan.org/loo/index.html) +package to see whether adding the distance matrix in the previous example +improved our out-of-sample predictive accuracy. + + +``` r +library(loo) +loo_compare( + list( + fit1 = fit1$fit$loo(), # authority model without distance matrix + fit4 = fit4$fit$loo() # authority model with distance matrix + ) +) +``` + +``` + elpd_diff se_diff +fit4 0.0 0.0 +fit1 -1.3 1.9 +``` +This model comparison suggests that adding spatial location to the model did not +improve out-of-sample predictive accuracy. +This model comparison approach may also be useful for comparing models with +different cross selection effects constrained to zero (see the `effects_mat` +argument in the `coev_fit()` function). The user can then test whether "turning +on" a particular cross selection effect improves model fit. However, currently +it is not possible to compare models that include different coevolving +variables, as the datasets and resulting log likelihood vectors vary between +models. +## Conclusion +We hope that this package is a useful addition to the phylogenetic comparative +methods toolkit. If you have any questions about the package, please feel free +to email Scott Claessens (scott.claessens@gmail.com) or Erik Ringen +(erikjacob.ringen@uzh.ch) or raise an issue over on GitHub: +https://github.com/ScottClaessens/coevolve/issues +## References +DeCasien, A. R., Williams, S. A., & Higham, J. P. (2017). Primate brain size is +predicted by diet but not sociality. *Nature Ecology & Evolution*, *1*(5), 0112. +de Villemeruil P. & Nakagawa, S. (2014). General quantitative genetic methods +for comparative biology. In L. Garamszegi (Ed.), *Modern phylogenetic +comparative methods and their application in evolutionary biology: concepts and +practice* (pp. 287-303). Springer, New York. +Sheehan, O., Watts, J., Gray, R. D., Bulbulia, J., Claessens, S., Ringen, E. J., +& Atkinson, Q. D. (2023). Coevolution of religious and political authority in +Austronesian societies. *Nature Human Behaviour*, *7*(1), 38-45. diff --git a/vignettes/primates-delta-theta-1.png b/vignettes/primates-delta-theta-1.png index c8fc79c810951b1b59c74449f025870b192b6da3..415f70c4e9b73305884d853dc5cc4c42f1c8022e 100644 GIT binary patch literal 10199 zcmbt)2_TeR+y9iJ2x(E4p?XS-3fVG~5R!^gGD)pII!*hzB}aZy=O z1Og#$dc^1y0K4(`5ff~`1QIX(5|KxE?WAKPTpb!Ww0);}Lz&#RyCCkX5WMnWh zD3lBe+}&j`ppax`W!2TyK~wM!ygwh7m{vo%sUzmpk}}aR+x2#ytZ= za>tNJ7!vq{hUJ?Jq$7YzpaYAF#0s9V{3{Y02?`k-$-Iuf(0?PRel40EI@v$dE87k~=6sr#p#%WsovJp^<1L5-o#7qlJSp z;6MRVtHP^#!mAb{QHV(X_<;p{gZOsvKjcU0hmi8xzjR0 z0j2>Nz&dcJRfU^Xh2yHSK>-FXR4pv9B2lbJEQ<_EBnwCZM)4(qYk{?}(36epsS59@ z0%{k=$H#&01#;v{$WH0mpF+e9@80fLEuwXKHngSTK^2QNscLZXC6#uj2 zqtb*P0^4zxKiKBe6!XFL!q{}NPQQyU%83!VCy>*ZKT+|aGK@2l6gydrgi5sK*tq+7 z9ObA^A=jaK#$BC}49A+Lm^Bcl+jXd9`&L)U_iJzWB~AO-V4h{#`R!~@#Klc;`VWMA zT&Nu?`KnJKmXU>N!#}2ev^u#C^-tb?r~jp;IVg2HW`4VVY${K02(NFp- 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zCrKd7*C)6d=pT%3a~%S`dy%?cb&!YbuQc%FT&pzgQPT|%hRz3;DZQxjoxp@7-rK^3 gO$e{F4oF*^t|VK#qKxDs-vHpqj|;M>S~#Hp2Tqo(_5c6? diff --git a/tests/testthat/fixtures/coevfit_example5-1.csv b/tests/testthat/fixtures/coevfit_example5-1.csv index 1f21ff3..35e712b 100644 --- a/tests/testthat/fixtures/coevfit_example5-1.csv +++ b/tests/testthat/fixtures/coevfit_example5-1.csv @@ -1,8 +1,8 @@ # stan_version_major = 2 # stan_version_minor = 34 # stan_version_patch = 1 -# model = model_cc9f9ceba42698ffc18201f5b3515c43_model -# start_datetime = 2024-08-09 14:28:34 UTC +# model = model_5caa36b63f071e70dad1155510c970b5_model +# start_datetime = 2024-08-09 20:08:40 UTC # method = sample (Default) # sample # num_samples = 50 @@ -31,77 +31,77 @@ # num_chains = 1 (Default) # id = 1 (Default) # data -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/standata-158c291426f1.json +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/standata-51506ccd11ef.json # init = 2 (Default) # random # seed = 1 # output -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_cc9f9ceba42698ffc18201f5b3515c43-202408091528-1-2e3642.csv +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_5caa36b63f071e70dad1155510c970b5-202408092108-1-8bd316.csv # diagnostic_file = (Default) # refresh = 100 (Default) # sig_figs = -1 (Default) -# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_cc9f9ceba42698ffc18201f5b3515c43-profile-202408091528-1-7af4ec.csv +# profile_file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_5caa36b63f071e70dad1155510c970b5-profile-202408092108-1-0456ce.csv # save_cmdstan_config = 0 (Default) # num_threads = 1 (Default) # stanc_version = stanc3 v2.34.0 -# stancflags = --name=model_cc9f9ceba42698ffc18201f5b3515c43_model +# stancflags = --name=model_5caa36b63f071e70dad1155510c970b5_model lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_offdiag.1,Q_diag.1,Q_diag.2,b.1,b.2,eta_anc.1,eta_anc.2,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.7.1,z_drift.8.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,z_drift.7.2,z_drift.8.2,c2.1,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.8.1,eta.9.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,eta.8.2,eta.9.2,A.1.1,A.2.1,A.1.2,A.2.2,Q.1.1,Q.2.1,Q.1.2,Q.2.2,Q_inf.1.1,Q_inf.2.1,Q_inf.1.2,Q_inf.2.2,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.8.1,drift_tips.9.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,drift_tips.8.2,drift_tips.9.2,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.8.1,sigma_tips.9.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,sigma_tips.8.2,sigma_tips.9.2,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,log_lik.9,log_lik.10,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.5.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2,yrep.5.2 # Adaptation terminated -# Step size = 0.335076 +# Step size = 0.314648 # Diagonal elements of inverse mass matrix: # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --22.1853,0.898107,0.335076,4,15,0,30.3891,-0.236421,-0.993221,0.310786,0.854995,1.93199,0.472289,-1.03322,-1.33098,1.00793,0.480796,-1.0162,-0.0100336,-2.52785,0.833483,-1.57104,0.0999486,1.43754,0.657233,-0.603302,-1.24079,-0.506323,-0.630096,1.61899,0.585679,0.439111,-1.12766,-0.565579,-0.183575,-0.183575,-0.204382,-0.204382,-1.33098,-0.265523,-0.295628,-0.252504,-0.281659,0.0507882,0.0507882,0.247243,0.247243,1.00793,0.472961,0.44382,0.192394,-0.236421,0,0.310786,-0.993221,0.854995,0,0,1.93199,2.13134,0.245816,0.245816,0.972586,-2.12615,0.266397,-0.502133,0.0343958,0.494709,-99,-99,-99,-99,-0.795352,-0.278694,0.723183,0.288666,0.237331,-99,-99,-99,-99,0.854995,0.854995,0.854995,0.854995,0.854995,-99,-99,-99,-99,1.93199,1.93199,1.93199,1.93199,1.93199,-99,-99,-99,-99,-0.0861889,-0.547078,-0.94407,-0.843849,-1.13141,-0.718794,-0.341224,-0.139174,-0.1735,-1.79409,0,0,1,0,1,2,2,2,2,2 --20.2074,0.9843,0.335076,4,15,0,30.7761,-0.913553,-1.34559,-0.100142,0.716714,0.137828,-0.307715,0.201447,0.735225,-0.965831,-0.0818181,0.908404,-0.801007,1.94759,-1.11649,1.99253,-0.506011,-1.41687,0.982721,-0.210389,1.15951,-0.158846,1.2266,-0.589615,-0.472656,-0.614665,-0.0529933,0.119721,-0.0110379,-0.0110379,0.0993534,0.0993534,0.735225,0.13578,0.160546,0.0287048,-0.140763,0.0856746,0.0856746,0.0310767,0.0310767,-0.965831,0.00825143,0.00589412,0.0741631,-0.913553,0,-0.100142,-1.34559,0.716714,0,0,0.137828,0.392516,-0.00227022,-0.00227022,0.0512147,1.11754,-0.313561,0.559592,-0.151996,-0.4256,-99,-99,-99,-99,-0.0401295,0.147627,-0.071405,-0.060277,-0.0781043,-99,-99,-99,-99,0.716714,0.716714,0.716714,0.716714,0.716714,-99,-99,-99,-99,0.137828,0.137828,0.137828,0.137828,0.137828,-99,-99,-99,-99,-1.29634,-0.813568,-0.421681,-0.634514,-0.515217,-0.631241,-0.56021,-0.660081,-0.681321,-0.696134,1,0,1,1,0,2,2,2,2,2 --19.608,0.93317,0.335076,4,15,0,30.508,-0.41497,-1.12499,0.548586,0.356111,1.29705,0.0647951,-0.65095,1.6748,-1.01331,-0.505177,-0.71342,-0.162431,0.855989,1.29645,1.46793,0.553548,-1.66805,0.478068,0.0779319,2.38553,-1.0527,0.440606,0.0359393,-0.417038,-0.0238322,0.227372,0.786451,0.575052,0.575052,0.551468,0.551468,1.6748,0.632263,0.617772,0.607963,-0.719747,-0.187172,-0.187172,-0.525458,-0.525458,-1.01331,-0.518998,-0.516174,-0.129148,-0.41497,0,0.548586,-1.12499,0.356111,0,0,1.29705,0.70056,0.205357,0.205357,0.576469,0.45772,0.265076,0.300137,0.12173,-0.366821,-99,-99,-99,-99,-0.542082,0.19331,0.0464538,-0.150377,-0.055672,-99,-99,-99,-99,0.356111,0.356111,0.356111,0.356111,0.356111,-99,-99,-99,-99,1.29705,1.29705,1.29705,1.29705,1.29705,-99,-99,-99,-99,-0.570691,-0.654954,-0.638259,-0.511521,-0.343392,-0.203392,-0.80987,-0.894033,-1.24343,-0.368473,1,1,1,0,0,1,1,1,1,1 --17.5634,0.885864,0.335076,3,15,0,33.0012,-0.313645,-1.75854,0.166242,0.437923,1.96389,-0.31046,-0.447494,1.50345,-0.479082,-0.523949,-0.598142,-0.400626,1.02011,1.03583,1.36675,0.548529,-0.745712,0.267628,0.265651,1.68816,-1.16628,0.413251,-0.381179,0.0451583,0.543427,0.339848,0.781458,0.482625,0.482625,0.501445,0.501445,1.50345,0.586869,0.573321,0.537346,-0.293169,0.108291,0.108291,-0.154877,-0.154877,-0.479082,-0.138372,-0.126388,0.195778,-0.313645,0,0.166242,-1.75854,0.437923,0,0,1.96389,0.721861,0.0447966,0.0447966,0.558386,0.585875,0.235775,0.311098,0.13432,-0.182604,-99,-99,-99,-99,-0.797291,0.191836,-0.156872,0.0269765,0.247438,-99,-99,-99,-99,0.437923,0.437923,0.437923,0.437923,0.437923,-99,-99,-99,-99,1.96389,1.96389,1.96389,1.96389,1.96389,-99,-99,-99,-99,-0.850172,-0.53584,-0.50528,-0.682922,-0.538576,-0.214494,-0.713206,-0.906105,-0.954124,-0.577128,0,0,1,1,0,1,2,1,2,1 --20.0348,0.931337,0.335076,4,15,0,28.5734,-0.0777942,-0.7598,-0.335775,1.0614,0.350378,0.321564,-1.38974,-1.57219,-0.422292,0.599451,0.56789,0.327255,-1.48318,-0.860737,-1.12934,-0.756909,0.419386,-0.326664,-0.359689,-1.33502,0.472391,-0.329409,0.352464,0.114564,-0.933758,-1.05263,-0.871067,-0.26289,-0.26289,-0.296735,-0.296735,-1.57219,-0.428232,-0.408196,-0.362645,-1.17104,-1.40818,-1.40818,-1.31025,-1.31025,-0.422292,-1.24321,-1.25068,-1.367,-0.0777942,0,-0.335775,-0.7598,1.0614,0,0,0.350378,7.22083,-0.0924322,-0.0924322,0.230573,-1.47592,-0.309375,-0.405919,-0.293346,0.162536,-99,-99,-99,-99,0.24199,-0.0633401,0.0723758,0.0264177,-0.19934,-99,-99,-99,-99,1.0614,1.0614,1.0614,1.0614,1.0614,-99,-99,-99,-99,0.350378,0.350378,0.350378,0.350378,0.350378,-99,-99,-99,-99,-0.0684682,-1.88265,-1.96509,-0.183297,-0.27563,-0.756844,-0.92437,-0.844727,-0.815417,-0.490544,0,0,1,0,1,2,2,2,2,1 --18.279,0.94621,0.335076,4,15,0,34.2733,-1.04688,-1.42822,0.26032,0.395944,0.220659,-0.337417,1.02665,1.62248,0.519321,-0.333034,-0.691492,-0.247498,0.96943,1.06495,1.39012,0.341677,-0.809077,0.695996,0.515369,1.38162,-0.538448,0.442801,-0.235858,-0.054652,0.758136,1.12775,0.461216,0.335733,0.335733,0.340885,0.340885,1.62248,0.42936,0.414473,0.39493,0.670999,0.895814,0.895814,0.824078,0.824078,0.519321,0.840185,0.847937,0.929761,-1.04688,0,0.26032,-1.42822,0.395944,0,0,0.220659,0.191127,0.00812478,0.00812478,0.0772498,0.395803,0.220577,0.287928,0.0756014,-0.179021,-99,-99,-99,-99,-0.130746,0.0687488,-0.0333156,-0.00809006,0.120357,-99,-99,-99,-99,0.395944,0.395944,0.395944,0.395944,0.395944,-99,-99,-99,-99,0.220659,0.220659,0.220659,0.220659,0.220659,-99,-99,-99,-99,-1.36253,-0.283267,-0.26707,-1.24093,-1.06681,-0.441936,-0.778067,-0.834543,-0.861129,-0.605684,1,1,1,0,1,1,2,1,1,1 --18.0261,0.970213,0.335076,4,15,0,27.6017,-0.76504,-0.768551,0.349639,1.05042,1.20689,-0.119704,-0.865087,-1.21707,0.848712,0.278452,0.251438,-0.594769,-2.32921,-0.213592,-1.18121,1.6329,-0.52176,-0.791969,-0.586525,-0.357049,0.644862,-0.090607,0.555605,-0.574599,-0.415684,-1.52651,-0.604392,-0.533959,-0.533959,-0.444812,-0.444812,-1.21707,-0.448704,-0.440261,-0.537168,-0.210146,-0.771704,-0.771704,-0.738668,-0.738668,0.848712,-0.672182,-0.693697,-0.736658,-0.76504,0,0.349639,-0.768551,1.05042,0,0,1.20689,0.768326,0.179009,0.179009,0.785172,-1.73397,-0.0732874,-0.405297,0.600153,-0.191767,-99,-99,-99,-99,0.247116,-0.0350477,0.194504,-0.209545,-0.168898,-99,-99,-99,-99,1.05042,1.05042,1.05042,1.05042,1.05042,-99,-99,-99,-99,1.20689,1.20689,1.20689,1.20689,1.20689,-99,-99,-99,-99,-0.133755,-1.20236,-1.44565,-0.626286,-0.332451,-1.75361,-0.396672,-0.327148,-0.44523,-1.04976,0,0,1,1,0,2,2,2,2,1 --20.3561,0.964063,0.335076,4,15,0,27.3076,-0.380009,-0.988158,-0.212021,1.23152,1.23093,0.358863,-0.955582,-1.88461,0.309583,0.510184,0.150241,-0.368568,-1.62816,-1.44053,-0.996128,1.84408,-0.146501,-0.924778,-0.994192,-1.00931,0.0656133,0.616716,-0.00866621,0.146016,-0.792847,-1.53168,-0.958354,-0.565811,-0.565811,-0.506794,-0.506794,-1.88461,-0.629277,-0.62291,-0.67109,-0.491798,-1.22507,-1.22507,-1.11345,-1.11345,0.309583,-1.09964,-1.13568,-1.25838,-0.380009,0,-0.212021,-0.988158,1.23152,0,0,1.23093,1.67424,-0.0965202,-0.0965202,0.622841,-1.51828,-0.547567,-0.378643,0.753568,-0.0598662,-99,-99,-99,-99,0.129593,0.232486,0.00133171,0.0468883,-0.309671,-99,-99,-99,-99,1.23152,1.23152,1.23152,1.23152,1.23152,-99,-99,-99,-99,1.23093,1.23093,1.23093,1.23093,1.23093,-99,-99,-99,-99,-0.125732,-1.92954,-1.78699,-0.529308,-0.269522,-1.43991,-0.459495,-0.550982,-0.48739,-0.748899,0,0,0,0,0,1,2,2,2,2 --21.325,0.965091,0.335076,4,15,0,31.7536,-1.24923,-0.801332,1.31007,0.461997,0.265582,0.108316,-0.689244,1.90382,-0.484985,-1.26865,-0.842324,0.415767,0.734278,1.15673,0.585875,-1.89759,1.22668,1.89374,0.780964,1.14618,-0.591399,0.278873,0.458891,-0.895988,-1.05192,-0.665925,0.142097,-0.193284,-0.193284,-0.253917,-0.253917,1.90382,-0.259593,-0.278162,-0.214938,-0.691787,-0.176401,-0.176401,-0.242341,-0.242341,-0.484985,-0.179911,-0.167301,-0.105661,-1.24923,0,1.31007,-0.801332,0.461997,0,0,0.265582,0.29594,0.105871,0.105871,0.165713,0.332971,0.256342,0.129835,-0.448766,0.290101,-99,-99,-99,-99,-0.10957,0.0597288,0.084822,-0.188755,-0.177824,-99,-99,-99,-99,0.461997,0.461997,0.461997,0.461997,0.461997,-99,-99,-99,-99,0.265582,0.265582,0.265582,0.265582,0.265582,-99,-99,-99,-99,-0.529747,-0.653975,-0.716701,-0.406146,-0.717312,-0.627722,-0.455766,-0.446654,-0.58261,-0.823558,0,1,1,0,1,2,2,2,2,2 --24.7986,0.906586,0.335076,4,15,0,32.6706,-2.60176,-0.597963,1.50491,0.353938,0.286299,0.583517,-0.469132,2.16478,-0.0586038,-1.48008,-0.88383,-0.078754,0.707608,1.42882,0.62091,-1.95562,1.31141,1.76539,0.513922,1.19456,-0.777025,0.0632132,0.172992,-0.770027,-1.00363,-0.0211017,0.207401,-0.0261493,-0.0261493,-0.0457418,-0.0457418,2.16478,-0.147258,-0.163436,-0.133705,-0.38533,0.0316573,0.0316573,-0.0460555,-0.0460555,-0.0586038,0.0113996,0.019899,0.093879,-2.60176,0,1.50491,-0.597963,0.353938,0,0,0.286299,0.133145,0.112593,0.112593,0.239395,0.219107,0.257302,0.111813,-0.371883,0.249381,-99,-99,-99,-99,-0.138949,0.0330145,0.0405747,-0.185625,-0.169039,-99,-99,-99,-99,0.353938,0.353938,0.353938,0.353938,0.353938,-99,-99,-99,-99,0.286299,0.286299,0.286299,0.286299,0.286299,-99,-99,-99,-99,-0.613486,-0.559069,-0.623983,-0.505855,-0.799968,-0.472879,-0.65118,-0.647569,-0.803969,-0.600847,0,1,1,1,1,2,1,2,2,2 --23.5813,1,0.335076,4,15,0,35.8128,-0.125239,-1.73406,-1.54161,1.12211,0.930356,-0.517411,0.128082,-2.08412,-0.178994,1.63328,0.861138,0.0287378,-1.16436,-1.27967,-0.372795,1.81416,-1.39328,-1.185,-0.261423,-0.926236,0.536022,0.170154,0.0166803,0.872298,0.684119,-0.728156,-2.26114,-0.456151,-0.456151,-0.451165,-0.451165,-2.08412,-0.568813,-0.538056,-0.548766,0.0292167,-0.695887,-0.695887,-0.598927,-0.598927,-0.178994,-0.781661,-0.788353,-0.878661,-0.125239,0,-1.54161,-1.73406,1.12211,0,0,0.930356,7.2178,-0.222424,-0.222424,0.268259,-1.28141,-0.473512,-0.137944,0.72444,-0.556372,-99,-99,-99,-99,0.427301,0.0819667,0.0138751,0.229461,0.260655,-99,-99,-99,-99,1.12211,1.12211,1.12211,1.12211,1.12211,-99,-99,-99,-99,0.930356,0.930356,0.930356,0.930356,0.930356,-99,-99,-99,-99,-0.251441,-1.43985,-1.19457,-0.757871,-0.273809,-1.45152,-0.63766,-0.670342,-0.529799,-0.906971,0,0,0,1,0,2,2,1,1,1 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diff --git a/tests/testthat/fixtures/coevfit_example5-1.csv b/tests/testthat/fixtures/coevfit_example5-1.csv index e9b22a9..1f21ff3 100644 --- a/tests/testthat/fixtures/coevfit_example5-1.csv +++ b/tests/testthat/fixtures/coevfit_example5-1.csv @@ -1,8 +1,8 @@ # stan_version_major = 2 # stan_version_minor = 34 # stan_version_patch = 1 -# model = model_6cf6bba52136c763c066366c453b41f1_model -# start_datetime = 2024-07-30 14:30:44 UTC +# model = model_cc9f9ceba42698ffc18201f5b3515c43_model +# start_datetime = 2024-08-09 14:28:34 UTC # method = sample (Default) # sample # num_samples = 50 @@ -31,77 +31,77 @@ # num_chains = 1 (Default) # id = 1 (Default) # data -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4K8hBb/standata-3438403c2d1f.json +# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/standata-158c291426f1.json # init = 2 (Default) # random # seed = 1 # output -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4K8hBb/model_6cf6bba52136c763c066366c453b41f1-202407301530-1-8bb5fe.csv +# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_cc9f9ceba42698ffc18201f5b3515c43-202408091528-1-2e3642.csv # diagnostic_file = (Default) # refresh = 100 (Default) # sig_figs = -1 (Default) -# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4K8hBb/model_6cf6bba52136c763c066366c453b41f1-profile-202407301530-1-0439b6.csv +# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4evDr1/model_cc9f9ceba42698ffc18201f5b3515c43-profile-202408091528-1-7af4ec.csv # save_cmdstan_config = 0 (Default) # num_threads = 1 (Default) # stanc_version = stanc3 v2.34.0 -# stancflags = --name=model_6cf6bba52136c763c066366c453b41f1_model -lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_offdiag.1,Q_diag.1,Q_diag.2,b.1,b.2,eta_anc.1,eta_anc.2,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.7.1,z_drift.8.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,z_drift.7.2,z_drift.8.2,c2.1,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.8.1,eta.9.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,eta.8.2,eta.9.2,Q.1.1,Q.2.1,Q.1.2,Q.2.2,I.1.1,I.2.1,I.1.2,I.2.2,A.1.1,A.2.1,A.1.2,A.2.2,Q_offdiag.1,Q_offdiag.2,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.8.1,drift_tips.9.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,drift_tips.8.2,drift_tips.9.2,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.8.1,sigma_tips.9.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,sigma_tips.8.2,sigma_tips.9.2,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,log_lik.9,log_lik.10,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.5.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2,yrep.5.2 +# stancflags = --name=model_cc9f9ceba42698ffc18201f5b3515c43_model +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,A_diag.1,A_diag.2,A_offdiag.1,Q_diag.1,Q_diag.2,b.1,b.2,eta_anc.1,eta_anc.2,z_drift.1.1,z_drift.2.1,z_drift.3.1,z_drift.4.1,z_drift.5.1,z_drift.6.1,z_drift.7.1,z_drift.8.1,z_drift.1.2,z_drift.2.2,z_drift.3.2,z_drift.4.2,z_drift.5.2,z_drift.6.2,z_drift.7.2,z_drift.8.2,c2.1,eta.1.1,eta.2.1,eta.3.1,eta.4.1,eta.5.1,eta.6.1,eta.7.1,eta.8.1,eta.9.1,eta.1.2,eta.2.2,eta.3.2,eta.4.2,eta.5.2,eta.6.2,eta.7.2,eta.8.2,eta.9.2,A.1.1,A.2.1,A.1.2,A.2.2,Q.1.1,Q.2.1,Q.1.2,Q.2.2,Q_inf.1.1,Q_inf.2.1,Q_inf.1.2,Q_inf.2.2,drift_tips.1.1,drift_tips.2.1,drift_tips.3.1,drift_tips.4.1,drift_tips.5.1,drift_tips.6.1,drift_tips.7.1,drift_tips.8.1,drift_tips.9.1,drift_tips.1.2,drift_tips.2.2,drift_tips.3.2,drift_tips.4.2,drift_tips.5.2,drift_tips.6.2,drift_tips.7.2,drift_tips.8.2,drift_tips.9.2,sigma_tips.1.1,sigma_tips.2.1,sigma_tips.3.1,sigma_tips.4.1,sigma_tips.5.1,sigma_tips.6.1,sigma_tips.7.1,sigma_tips.8.1,sigma_tips.9.1,sigma_tips.1.2,sigma_tips.2.2,sigma_tips.3.2,sigma_tips.4.2,sigma_tips.5.2,sigma_tips.6.2,sigma_tips.7.2,sigma_tips.8.2,sigma_tips.9.2,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,log_lik.9,log_lik.10,yrep.1.1,yrep.2.1,yrep.3.1,yrep.4.1,yrep.5.1,yrep.1.2,yrep.2.2,yrep.3.2,yrep.4.2,yrep.5.2 # Adaptation terminated -# Step size = 0.349327 +# Step size = 0.335076 # Diagonal elements of inverse mass matrix: # 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 --19.4054,0.952955,0.349327,4,15,0,37.651,-2.29029,-1.21923,-0.188837,0.0810534,0.64185,-1.71168,0.472296,-0.452767,-0.706611,-0.488402,0.229619,1.63679,-0.799707,0.303127,1.71637,0.26811,0.285079,-1.06525,-0.866444,0.732109,0.247053,-0.622078,-0.129676,0.0608713,-0.510129,-0.536193,-0.708784,-0.69742,-0.69742,-0.743911,-0.743911,-0.452767,-0.758023,-0.755758,-0.689381,0.0641464,-0.257345,-0.257345,-0.348261,-0.348261,-0.706611,-0.46679,-0.488441,-0.361529,0.0810534,0,0,0.64185,1,0,0,1,-2.29029,0,-0.188837,-1.21923,0,0,-0.108072,0.0264707,0.149883,0.0247499,0.0263163,-99,-99,-99,-99,0.184933,-0.164806,-0.0479847,0.0142086,-0.144835,-99,-99,-99,-99,0.0810534,0.0810534,0.0810534,0.0810534,0.0810534,-99,-99,-99,-99,0.64185,0.64185,0.64185,0.64185,0.64185,-99,-99,-99,-99,-0.365905,-1.08387,-1.00393,-0.396869,-0.397382,-1.16096,-0.637751,-0.584363,-0.597176,-0.714927,1,0,0,0,0,1,2,2,2,1 --20.0472,0.965515,0.349327,4,15,0,32.5185,-0.161893,-1.0845,-0.314645,1.98966,0.360233,0.833383,-0.111799,-0.289182,1.45909,-0.346811,-0.257142,-1.44809,0.777788,-0.163976,-1.52018,-0.636212,-0.5009,1.64228,0.887329,-0.622631,-1.00933,0.943971,0.257127,0.30272,0.179268,0.649528,0.280388,-0.448623,-0.448623,-0.171337,-0.171337,-0.289182,-0.236487,-0.247837,-0.522875,0.42504,0.907582,0.907582,0.969551,0.969551,1.45909,1.13354,1.14957,1.05163,1.98966,0,0,0.360233,1,0,0,1,-0.161893,0,-0.314645,-1.0845,0,0,1.0155,-0.080277,-0.744226,-0.335554,-0.264187,-99,-99,-99,-99,-0.398755,0.18631,0.0529939,0.0649079,0.0386705,-99,-99,-99,-99,1.98966,1.98966,1.98966,1.98966,1.98966,-99,-99,-99,-99,0.360233,0.360233,0.360233,0.360233,0.360233,-99,-99,-99,-99,-1.53778,-0.992164,-1.45779,-0.471481,-0.49891,-0.429313,-0.495447,-0.549669,-0.51909,-0.888491,1,0,0,0,0,1,2,1,2,1 --24.0604,0.931919,0.349327,4,15,0,32.6896,-1.70489,-1.05473,-0.668185,0.32885,0.413141,-1.46354,0.407288,0.190464,-0.896475,1.61173,-0.00844761,1.58006,-1.31752,0.943678,1.49498,0.517631,-0.0622705,-0.994797,-1.32572,0.977788,0.679446,-0.458042,-0.36185,0.0669794,-1.37598,-3.22885,-0.57211,-0.0343768,-0.0343768,-0.131379,-0.131379,0.190464,0.00364682,0.00238999,0.111984,-0.0605688,-0.445669,-0.445669,-0.548319,-0.548319,-0.896475,-0.673762,-0.700513,-0.560757,0.32885,0,0,0.413141,1,0,0,1,-1.70489,0,-0.668185,-1.05473,0,0,-0.4219,0.171678,0.271973,0.100056,-0.0120367,-99,-99,-99,-99,0.407899,-0.105638,-0.0905039,0.00908998,-0.30966,-99,-99,-99,-99,0.32885,0.32885,0.32885,0.32885,0.32885,-99,-99,-99,-99,0.413141,0.413141,0.413141,0.413141,0.413141,-99,-99,-99,-99,-0.314876,-0.626851,-0.581389,-0.677608,-0.624008,-3.60377,-0.0664729,-0.0655065,-0.0657006,-2.46016,1,0,0,1,0,2,2,2,2,2 --21.6355,0.966339,0.349327,4,15,0,37.7311,-0.47922,-0.277479,0.196478,1.83615,0.950698,-0.474564,-0.851818,-0.313244,-0.898911,-0.352222,-0.0882901,-1.70106,0.651199,0.92342,1.66425,1.03467,0.390239,0.00420015,-0.544413,-1.87168,-2.06413,0.932861,0.671823,-0.843782,0.369684,-0.939065,-0.757544,-1.42052,-1.42052,-1.10315,-1.10315,-0.313244,-1.06562,-1.0698,-1.40508,-1.42495,-1.69405,-1.69405,-1.45394,-1.45394,-0.898911,-1.37058,-1.38851,-1.64634,1.83615,0,0,0.950698,1,0,0,1,-0.47922,0,0.196478,-0.277479,0,0,0.709813,0.426014,0.76779,0.512655,0.193355,-99,-99,-99,-99,-1.72282,0.316126,0.23056,-0.301337,0.135082,-99,-99,-99,-99,1.83615,1.83615,1.83615,1.83615,1.83615,-99,-99,-99,-99,0.950698,0.950698,0.950698,0.950698,0.950698,-99,-99,-99,-99,-0.669566,-1.30925,-1.07185,-0.440865,-0.338331,-0.104219,-0.936463,-0.989353,-1.18232,-0.521173,1,1,1,0,0,1,1,1,2,1 --17.7919,0.993486,0.349327,4,15,0,35.1287,-0.178294,-0.159949,-1.40368,1.14546,0.960525,-1.588,-0.598245,1.64374,0.326002,-0.255882,0.255854,0.473641,-0.815631,0.267382,1.32074,-0.82045,-0.519994,0.732326,-0.0795876,0.340825,-0.44799,0.402798,0.798451,-0.195491,0.636404,0.997143,-0.0901725,-0.423867,-0.423867,-0.478753,-0.478753,1.64374,-0.184852,-0.178232,-0.157508,-0.275038,0.409274,0.409274,0.359983,0.359983,0.326002,0.458176,0.454886,0.491624,1.14546,0,0,0.960525,1,0,0,1,-0.178294,0,-1.40368,-0.159949,0,0,-0.983526,0.0996259,0.492105,-0.329756,-0.208997,-99,-99,-99,-99,0.0147423,0.129496,0.235673,-0.0440548,0.249572,-99,-99,-99,-99,1.14546,1.14546,1.14546,1.14546,1.14546,-99,-99,-99,-99,0.960525,0.960525,0.960525,0.960525,0.960525,-99,-99,-99,-99,-0.293969,-0.868352,-0.659611,-0.36847,-0.407268,-0.250277,-0.94837,-0.884671,-1.09067,-0.518015,0,0,1,0,0,1,1,2,1,1 --23.5669,0.865145,0.349327,3,7,0,32.7428,-0.202163,-2.59226,1.01396,0.501661,0.342962,0.804313,1.06056,-1.10784,-1.47268,-0.0934648,-0.912998,-0.213302,0.0710206,-0.973144,0.83535,1.48033,0.583508,0.182189,0.029842,1.7785,0.82214,-0.645052,-1.42005,0.872405,0.879159,-1.13388,-0.393688,-0.450299,-0.450299,-0.46495,-0.46495,-1.10784,-0.615891,-0.635844,-0.612737,0.268272,0.405425,0.405425,0.298427,0.298427,-1.47268,0.247712,0.248727,0.404037,0.501661,0,0,0.342962,1,0,0,1,-0.202163,0,1.01396,-2.59226,0,0,0.046775,-0.238963,0.205127,0.391625,0.154368,-99,-99,-99,-99,0.21149,-0.12133,-0.243951,0.175942,0.168857,-99,-99,-99,-99,0.501661,0.501661,0.501661,0.501661,0.501661,-99,-99,-99,-99,0.342962,0.342962,0.342962,0.342962,0.342962,-99,-99,-99,-99,-0.534659,-1.09602,-0.823229,-0.657157,-0.549866,-1.79526,-0.216887,-0.242005,-0.18252,-1.78487,1,1,1,0,0,2,2,2,2,2 --24.0717,1,0.349327,4,15,0,37.3059,-2.35865,-0.294893,-0.775984,1.43081,0.902991,-0.878023,-1.00457,1.0052,1.44817,0.687618,-0.0425953,-0.914765,-2.77458,-0.420632,0.13494,-1.29033,0.218835,0.819787,-0.210765,-0.407928,-1.16999,-0.460454,-2.37238,-1.35806,-0.263081,-0.410694,-0.413873,-0.291802,-0.291802,-0.180381,-0.180381,1.0052,-0.00175445,-0.00507075,-0.18897,0.208325,0.61514,0.61514,0.662392,0.662392,1.44817,0.845645,0.837722,0.763522,1.43081,0,0,0.902991,1,0,0,1,-2.35865,0,-0.775984,-0.294893,0,0,-1.60451,-0.153793,0.049337,-0.498505,0.0845443,-99,-99,-99,-99,-0.265425,-0.145178,-0.777222,-0.456918,-0.0959916,-99,-99,-99,-99,1.43081,1.43081,1.43081,1.43081,1.43081,-99,-99,-99,-99,0.902991,0.902991,0.902991,0.902991,0.902991,-99,-99,-99,-99,-0.124755,-0.940561,-0.82171,-0.410241,-0.646376,-0.885492,-0.346784,-0.576548,-0.431789,-1.29657,0,1,1,1,1,1,1,2,2,2 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