diff --git a/R/coev_make_stancode.R b/R/coev_make_stancode.R index 4604db7..924562b 100644 --- a/R/coev_make_stancode.R +++ b/R/coev_make_stancode.R @@ -101,69 +101,73 @@ 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", - " 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", - " // 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 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", + " // 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", - " irow_mat[row_ticker,col_ticker] = irow_vec[i];\n", " }\n", " }\n", - " return(irow_mat);\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", + " }\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 AQ;\n", " }\n", " \n", " // return number of matches of y in vector x\n", @@ -192,19 +196,19 @@ 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)) { @@ -218,7 +222,7 @@ 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" @@ -264,7 +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 @@ -319,11 +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] 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" + " 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)) { @@ -343,77 +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", + " 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 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", - " }\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", - " // identity matrix\n", - " I = diag_matrix(rep_vector(1,J));\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 = A_dt(A, ts[i]);\n", - " VCV = cov_drift(A, Q, ts[i]);\n", - " drift_seg = cholesky_decompose(VCV) * to_vector( z_drift[i-1,] );\n", + " A_delta = matrix_exp(A * ts[i]);\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],]) + (inverse(A) * (A_delta - I) * 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],]) + (inverse(A) * (A_delta - I) * 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" ) @@ -443,7 +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" @@ -503,69 +483,71 @@ coev_make_stancode <- function(data, variables, id, tree, " if (!prior_only) {\n", " for (i in 1:N_obs) {\n" ) + # 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, "]"), + "" + ) + ) + } 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, "]"), ""), - " + drift_tips[tip_id[i],", j, "]);\n" + " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", + "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(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" + " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", + "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(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" + " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", + "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(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" + " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", + "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, ", 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" + " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", + "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(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" + " if (miss[i,", j, "] == 0) y[i,", j, "] ~ ", + "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(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" + " if (miss[i,", j, "] == 0) to_int(y[i,", j, "]) ~ ", + "neg_binomial_2(mean(obs", j, ") * log1p_exp(", lmod(j), + " + drift_tips[tip_id[i]][", j, "]), phi", j, ");\n" ) } } @@ -603,101 +585,73 @@ 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, "]"), ""), - " + drift_tips[tip_id[i],", j, "]);\n", + "bernoulli_logit_lpmf(to_int(y[i,", j, "]) | ", lmod(j), + " + 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, "]"), ""), - " + drift_tips[tip_id[i],", j, "]);\n" + "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, "]) | 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", + "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(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" + "ordered_logistic_rng(", lmod(j), + " + 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, "]", - 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", + ") * log1p_exp(", lmod(j), + " + 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, "]"), ""), - " + drift_tips[tip_id[i],", j, "]));\n" + "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, "] | 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", + "normal_lpdf(y[i,", j, "] | ", lmod(j), + ", 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, "]"), ""), - ", sigma_tips[tip_id[i],", j, "]);\n" + "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, ", 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", + "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, ", 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" + "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, "] | 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", + "lognormal_lpdf(y[i,", j, "] | ", lmod(j), + ", 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, "]"), ""), - ", sigma_tips[tip_id[i],", j, "]);\n" + "lognormal_rng(", lmod(j), + ", 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, "]", - 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", + ") * 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(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" + "neg_binomial_2_rng(mean(obs", j, ") * log1p_exp(", lmod(j), + " + drift_tips[tip_id[i]][", j, "]), phi", j, ");\n" ) } } 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). Coevolution of religious + and political authority in Austronesian societies. *Nature Human + Behaviour*, *7*(1), 38-45. + diff --git a/man/figures/README-authority-delta-theta-1.png b/man/figures/README-authority-delta-theta-1.png index d797ab1..888edf0 100644 Binary files a/man/figures/README-authority-delta-theta-1.png and b/man/figures/README-authority-delta-theta-1.png differ diff --git a/tests/testthat/fixtures/coevfit_example1-1.csv b/tests/testthat/fixtures/coevfit_example1-1.csv index 8a48b3c..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_1451c5e91c245a69a02e26744e116f2b_model -# start_datetime = 2024-07-30 14:22:36 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/Rtmp4K8hBb/standata-3438389d7b2e.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/Rtmp4K8hBb/model_1451c5e91c245a69a02e26744e116f2b-202407301522-1-047454.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/Rtmp4K8hBb/model_1451c5e91c245a69a02e26744e116f2b-profile-202407301522-1-8e0328.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_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_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 = 0.0738804 +# 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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# -# Elapsed Time: 41.373 seconds (Warm-up) -# 43.071 seconds (Sampling) -# 84.444 seconds (Total) +# Elapsed Time: 0.711 seconds (Warm-up) +# 0.564 seconds (Sampling) +# 1.275 seconds (Total) # diff --git a/tests/testthat/fixtures/coevfit_example1.rds b/tests/testthat/fixtures/coevfit_example1.rds index 46e84fb..863cb20 100644 Binary files a/tests/testthat/fixtures/coevfit_example1.rds and b/tests/testthat/fixtures/coevfit_example1.rds differ diff --git a/tests/testthat/fixtures/coevfit_example2-1.csv b/tests/testthat/fixtures/coevfit_example2-1.csv index fdd9f39..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_3e350a7f684b5fd4a5d0b406f1be86f9_model -# start_datetime = 2024-07-30 14:26:18 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/Rtmp4K8hBb/standata-34381f5e453a.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/Rtmp4K8hBb/model_3e350a7f684b5fd4a5d0b406f1be86f9-202407301526-1-111066.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/Rtmp4K8hBb/model_3e350a7f684b5fd4a5d0b406f1be86f9-profile-202407301526-1-77ceca.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_3e350a7f684b5fd4a5d0b406f1be86f9_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,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,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,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_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 = 0.270845 +# 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 --36.1652,0.975925,0.270845,4,15,0,55.1364,-0.540023,-0.181134,1.58319,0.235453,0.548588,0.876209,0.972811,0.450903,-0.484964,-1.4253,-1.36161,1.54018,0.329188,-0.28942,-0.467915,0.197163,-2.05824,0.33763,1.27129,-0.281705,1.10771,0.78376,0.405778,0.200808,0.29309,-0.507154,-0.628015,1.92325,-0.283776,2.60208,0.847161,1.96604,-0.652882,1.15237,1.70905,0.138328,0.563146,2.03886,0.287924,0.773764,0.570423,-0.938199,-1.87508,-1.87508,-1.90365,-1.90365,-0.484964,-2.01627,-1.97824,-1.96906,-0.94907,-0.777965,-0.777965,-0.935178,-0.935178,-1.4253,-0.950412,-0.958851,-0.795278,0.548588,0,0,0.876209,1,0,0,1,-0.540023,0.235453,1.58319,-0.181134,0,0,1.70266,-0.0303243,2.53243,0.441876,2.44832,-0.497401,0.226951,0.408733,0.264926,0.430705,-0.26469,-0.118843,0.0500763,-0.563244,0.0923933,-99,-99,-99,-99,0.319631,0.110476,0.0731434,-0.00946228,-0.157204,-99,-99,-99,-99,0.548588,0.548588,0.548588,0.548588,0.548588,-99,-99,-99,-99,0.876209,0.876209,0.876209,0.876209,0.876209,-99,-99,-99,-99,-0.973934,-2.14831,-0.400728,-0.123979,-1.06158,-0.474521,-0.603795,-0.540893,-0.719331,-0.676458,1,0,1,0,1,2,1,1,2,2 --35.3419,0.980771,0.270845,4,15,0,55.4462,-1.61131,-2.13985,-1.56952,1.06247,0.940818,1.02054,-1.64792,-0.125116,-0.252123,-0.350753,1.24875,-2.07613,0.79865,0.450902,-0.550104,-0.182774,0.166141,0.550723,0.540121,-0.236965,-0.212842,-1.11367,0.226366,-1.67689,-1.19816,-0.200521,-0.479319,-1.62926,-0.216386,-1.29626,-2.31824,-1.95803,-0.447546,-0.500108,-0.698482,0.94514,-1.04877,0.2419,0.123044,0.705266,0.450068,-0.625913,-0.0486491,-0.0486491,-0.193694,-0.193694,-0.252123,0.0624476,-0.00531142,0.140169,-0.332206,-0.107166,-0.107166,-0.108109,-0.108109,-0.350753,-0.0990302,-0.106692,-0.127608,0.940818,0,0,1.02054,1,0,0,1,-1.61131,1.06247,-1.56952,-2.13985,0,0,-1.37792,-1.18257,-1.66729,-1.62977,-2.01152,-0.303563,-0.432789,-0.509021,-0.300479,-0.582406,0.244232,-0.170303,-0.0565839,0.0547032,0.18133,-99,-99,-99,-99,-0.542795,0.0759555,-0.520901,-0.39692,-0.0725451,-99,-99,-99,-99,0.940818,0.940818,0.940818,0.940818,0.940818,-99,-99,-99,-99,1.02054,1.02054,1.02054,1.02054,1.02054,-99,-99,-99,-99,-0.158809,-1.62164,-1.92944,-0.157469,-0.124111,-0.403437,-0.685517,-1.07517,-0.869483,-0.561307,0,0,1,1,0,1,1,1,1,2 --40.9029,0.86064,0.270845,4,15,0,61.0463,-1.64352,-0.273716,-0.717906,0.107952,1.17016,0.686116,1.38111,0.611993,0.502812,-0.688566,-0.706572,1.96667,0.0566028,-0.423773,0.865689,1.76293,0.00278104,-0.969445,0.401899,-0.832123,1.44816,0.749127,0.484738,0.973197,2.22278,0.309253,-1.54529,1.76617,0.382545,0.552094,1.34858,2.83694,1.21372,-0.0317635,-0.172807,-1.20798,0.69135,2.06293,0.958478,0.388748,0.0778197,0.840804,0.469471,0.469471,0.533126,0.533126,0.502812,0.424159,0.494396,0.441916,0.0841384,0.597035,0.597035,0.407563,0.407563,-0.688566,0.348462,0.326434,0.534825,1.17016,0,0,0.686116,1,0,0,1,-1.64352,0.107952,-0.717906,-0.273716,0,0,1.11527,0.377878,0.7611,0.745863,1.86163,0.359678,0.113369,0.152637,-0.0660953,0.198181,-0.257968,0.297903,0.606665,0.00101714,-0.354565,-99,-99,-99,-99,0.596618,0.13182,0.264465,0.682494,0.104166,-99,-99,-99,-99,1.17016,1.17016,1.17016,1.17016,1.17016,-99,-99,-99,-99,0.686116,0.686116,0.686116,0.686116,0.686116,-99,-99,-99,-99,-1.86619,-0.276225,-0.147779,-1.52533,-2.16241,-2.65837,-0.0878803,-0.0745033,-0.0738011,-2.35491,1,0,0,0,1,2,2,2,2,2 --37.6627,0.960719,0.270845,4,15,0,65.9279,-0.29993,-2.53009,-0.408916,0.353744,0.573279,0.756482,-0.572543,-0.58866,-2.84319,-1.18936,0.306431,0.187028,1.34722,-2.55482,0.439801,1.40659,0.63436,0.662957,1.2799,0.750608,-0.293241,-0.419895,-0.743465,0.889061,-0.959435,0.730971,1.7215,-1.04957,-0.885742,-0.776148,0.663784,-0.675771,0.436936,1.38181,0.800696,-0.733776,-1.92399,1.20327,2.46107,0.517405,1.90705,-2.31862,-2.01737,-2.01737,-2.1583,-2.1583,-2.84319,-2.1859,-2.18109,-2.03441,-0.624692,-0.291756,-0.291756,-0.255415,-0.255415,-1.18936,-0.153398,-0.133046,-0.21004,0.573279,0,0,0.756482,1,0,0,1,-0.29993,0.353744,-0.408916,-2.53009,0,0,-0.762224,-0.839207,-1.08543,-0.677711,-1.11317,0.604972,1.95767,1.72229,1.43211,-0.5294,-1.66979,0.114615,0.366568,0.177863,0.185881,-99,-99,-99,-99,-0.236458,-0.196198,0.231878,-0.267191,0.201818,-99,-99,-99,-99,0.573279,0.573279,0.573279,0.573279,0.573279,-99,-99,-99,-99,0.756482,0.756482,0.756482,0.756482,0.756482,-99,-99,-99,-99,-0.008609,-2.80442,-2.79903,-0.0677316,-0.0446894,-0.129615,-0.826942,-0.723127,-1.17939,-0.0951363,0,0,0,0,0,1,1,1,2,2 --37.2702,0.896373,0.270845,4,15,0,59.3454,-0.113071,-0.926809,-1.88866,-1.00286,0.248113,1.00632,-0.294588,-0.825027,-0.734394,-2.18549,0.380048,-0.0858883,0.133958,-1.01547,-0.772342,0.866561,0.00991848,-0.240221,2.0347,0.931065,0.114774,1.03598,-0.875445,-1.31769,-0.345358,0.363745,1.23857,-0.0203459,-1.65614,0.413661,-1.85617,0.511704,-0.265706,1.98378,0.648486,0.467154,-1.80697,1.59565,1.20354,0.98716,0.989666,2.46762,2.62555,2.62555,2.61011,2.61011,-0.734394,2.32676,2.32726,2.37497,-2.14211,-1.51102,-1.51102,-1.49656,-1.49656,-2.18549,-1.23275,-1.20427,-1.25989,0.248113,0,0,1.00632,1,0,0,1,-0.113071,-1.00286,-1.88866,-0.926809,0,0,-0.020317,-1.6242,-0.295902,-2.13525,-0.838836,-0.265662,1.81494,1.15797,1.87741,0.515156,-1.10547,-0.138962,0.155915,0.0019468,-0.0471505,-99,-99,-99,-99,1.28058,-0.220782,-0.497836,-0.119763,0.149101,-99,-99,-99,-99,0.248113,0.248113,0.248113,0.248113,0.248113,-99,-99,-99,-99,1.00632,1.00632,1.00632,1.00632,1.00632,-99,-99,-99,-99,-1.57403,-0.352171,-0.0799922,-0.959703,-1.88822,-0.0897284,-1.42921,-2.20613,-1.29685,-0.118739,0,0,1,1,0,1,1,1,1,1 --29.8939,1,0.270845,4,15,0,55.402,-2.29205,-0.567039,1.05974,1.30546,1.20457,0.855481,0.797243,0.265106,0.0691144,1.24432,-0.609356,0.672903,1.88555,-0.57789,0.255365,0.787835,-1.06197,-0.18803,-1.0993,-0.273655,0.115588,-0.243731,0.985968,0.830912,0.938147,-1.01142,0.0206543,-0.854349,1.59448,-0.865218,1.01918,0.22663,-0.450136,-1.05086,-1.14397,-0.446396,1.64757,0.333525,0.365218,0.285331,0.724338,0.896701,0.733899,0.733899,0.523309,0.523309,0.0691144,0.480761,0.505145,0.778905,1.54057,0.589932,0.589932,0.525346,0.525346,1.24432,0.43789,0.430306,0.473546,1.20457,0,0,0.855481,1,0,0,1,-2.29205,1.30546,1.05974,-0.567039,0,0,-0.46429,0.304066,-0.281732,0.367556,0.0809427,-0.385737,-0.920484,-1.08169,-1.01245,-0.571385,-0.353322,0.0863475,0.266393,-0.380173,-0.0673125,-99,-99,-99,-99,-0.481822,0.319508,0.294212,0.257596,-0.349414,-99,-99,-99,-99,1.20457,1.20457,1.20457,1.20457,1.20457,-99,-99,-99,-99,0.855481,0.855481,0.855481,0.855481,0.855481,-99,-99,-99,-99,-0.733473,-0.281319,-0.397066,-0.980746,-0.99723,-1.07161,-0.709121,-0.808187,-0.826031,-0.506582,1,1,0,0,0,2,2,2,1,2 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+-31.4327,0.926677,0.313564,4,15,0,50.7418,-1.62495,-0.743311,0.196349,1.08758,0.0723486,0.792443,0.639896,-0.407798,-1.59054,-1.59189,1.32858,0.325071,1.4619,-0.243118,-0.303814,0.810246,0.718801,0.751472,0.591923,-1.3375,-0.29618,-1.22735,0.221458,-0.341454,-0.548175,-0.698515,-0.941878,1.15985,-0.663398,1.40032,0.0778227,-1.24834,-0.920428,-0.978765,-0.205575,-0.141614,-0.928347,1.45197,4.22252,0.199953,0.310607,-0.156595,0.063426,0.063426,0.019279,0.019279,-1.59054,-0.0507656,-0.047327,0.0167183,-1.51097,-0.96265,-0.96265,-0.97089,-0.97089,-1.59189,-0.977479,-1.01626,-1.00799,-1.62495,1.08758,0.196349,-0.743311,0.0723486,0,0,0.792443,0.0302496,0.066105,0.066105,0.629772,-0.0378027,-0.0260487,0.0694696,0.0655317,0.0685102,-99,-99,-99,-99,-0.817126,0.0607575,-0.0877794,-0.159586,-0.20687,-99,-99,-99,-99,0.0723486,0.0723486,0.0723486,0.0723486,0.0723486,-99,-99,-99,-99,0.792443,0.792443,0.792443,0.792443,0.792443,-99,-99,-99,-99,0.531451,-0.177831,0.64169,-0.078614,-0.106062,-0.521167,-0.561545,-0.240641,-0.518431,-0.712448,-0.875808,-0.765838,-0.379049,-0.69625,-0.684053,-0.138429,-0.987551,-0.882908,-1.10789,-0.327422,1,1,1,1,0,1,1,1,1,2 +-33.423,0.891292,0.313564,4,15,0,61.0219,-0.889725,-0.75827,0.407183,0.672037,0.175218,0.546919,0.633824,0.000643099,-0.877186,-0.401428,1.52948,1.14455,0.608232,-0.55904,0.769168,0.554654,1.32768,1.19823,0.932028,-1.55942,-1.24867,-0.358434,1.20265,-0.60116,-0.671974,0.0312372,-0.231023,0.889668,-0.829471,2.65245,0.103838,-1.01434,0.652728,0.0300643,-1.15709,1.90307,0.042165,3.57433,2.26907,0.17578,0.576172,-0.0525362,0.412925,0.412925,0.397806,0.397806,-0.877186,0.318894,0.335147,0.36614,-0.35556,0.236207,0.236207,0.322057,0.322057,-0.401428,0.359406,0.321521,0.225414,-0.889725,0.672037,0.407183,-0.75827,0.175218,0,0,0.546919,0.19799,0.217464,0.217464,0.553369,-0.173606,0.107161,0.0772749,0.198014,0.178707,-99,-99,-99,-99,-0.303224,0.309885,-0.139704,-0.1513,0.0323167,-99,-99,-99,-99,0.175218,0.175218,0.175218,0.175218,0.175218,-99,-99,-99,-99,0.546919,0.546919,0.546919,0.546919,0.546919,-99,-99,-99,-99,0.383467,-0.347223,1.11356,-0.201313,-0.0623667,0.499741,0.0736946,-0.642079,0.635444,0.0165168,-0.7749,-0.610446,-0.183271,-0.90973,-0.982906,-0.729784,-0.355623,-0.862741,-0.303386,-1.03872,0,1,1,0,1,1,2,2,1,2 # -# Elapsed Time: 0.22 seconds (Warm-up) +# Elapsed Time: 0.18 seconds (Warm-up) # 0.196 seconds (Sampling) -# 0.416 seconds (Total) +# 0.376 seconds (Total) # diff --git a/tests/testthat/fixtures/coevfit_example2.rds b/tests/testthat/fixtures/coevfit_example2.rds index fc3cfa8..2b3fc3e 100644 Binary files a/tests/testthat/fixtures/coevfit_example2.rds and b/tests/testthat/fixtures/coevfit_example2.rds differ diff --git a/tests/testthat/fixtures/coevfit_example3-1.csv b/tests/testthat/fixtures/coevfit_example3-1.csv index c77d354..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_6cf6bba52136c763c066366c453b41f1_model -# start_datetime = 2024-07-30 14:28:34 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/Rtmp4K8hBb/standata-3438365b14db.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/Rtmp4K8hBb/model_6cf6bba52136c763c066366c453b41f1-202407301528-1-80a145.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/Rtmp4K8hBb/model_6cf6bba52136c763c066366c453b41f1-profile-202407301528-1-3f8fd7.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_6cf6bba52136c763c066366c453b41f1_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,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_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 --13.6823,0.921794,0.359451,4,15,0,28.6531,-0.786092,-0.164032,1.48271,0.0561885,1.39862,0.193302,-0.865132,0.360399,0.579182,-0.246046,-1.26237,0.604333,0.879217,-0.649229,-2.08098,-0.768519,-0.378742,0.163278,1.47617,0.300748,-0.698343,-0.417592,1.16379,1.25729,0.117487,-0.572944,-0.580304,-0.364425,-1.10986,-1.10986,-1.21706,-1.21706,0.579182,-1.36892,-1.34447,-1.19879,0.122991,0.473318,0.473318,0.520188,0.520188,-0.246046,0.486218,0.490781,0.446272,1.39862,0,0,0.193302,1,0,0,1,-0.786092,0.0561885,1.48271,-0.164032,0,0,-0.578081,-0.823407,-0.304089,-0.160532,0.0692063,-99,-99,-99,-99,-0.247481,0.163819,0.186665,0.0161867,-0.0927462,-99,-99,-99,-99,1.39862,1.39862,1.39862,1.39862,1.39862,-99,-99,-99,-99,0.193302,0.193302,0.193302,0.193302,0.193302,-99,-99,-99,-99,-0.329052,-2.06839,-1.63162,-0.22489,-0.275597,-0.946803,-0.259272,-0.2541,-0.283196,-1.31893,0,0,0,1,0,2,2,2,2,1 --10.4315,0.976096,0.359451,4,15,0,21.3195,-1.2885,-0.511566,0.128095,0.438261,0.309273,0.550547,0.683538,0.891481,0.804253,-0.00674248,0.677431,1.24828,0.49479,-0.896336,0.695141,0.809099,-0.779558,0.806123,-0.212443,-1.10991,1.67385,-0.871869,-0.78587,-0.385008,-0.942229,0.262882,-0.336415,0.649461,0.870119,0.870119,0.852279,0.852279,0.804253,0.878811,0.901205,0.911071,0.932212,1.0597,1.0597,0.864393,0.864393,-0.00674248,0.766822,0.740758,0.965952,0.309273,0,0,0.550547,1,0,0,1,-1.2885,0.438261,0.128095,-0.511566,0,0,-0.301196,0.125479,0.14605,-0.150003,0.155114,-99,-99,-99,-99,-0.601263,-0.192991,-0.0910913,-0.262162,0.0785314,-99,-99,-99,-99,0.309273,0.309273,0.309273,0.309273,0.309273,-99,-99,-99,-99,0.550547,0.550547,0.550547,0.550547,0.550547,-99,-99,-99,-99,-0.882365,-0.314447,-0.308939,-1.10471,-1.31867,-1.0815,-0.26256,-0.239935,-0.330136,-1.52481,0,1,1,0,1,2,2,2,2,2 --13.3976,0.942435,0.359451,4,15,0,22.1624,-0.764302,-0.701279,-0.155346,-0.801476,1.47616,0.427552,-0.652987,-1.32087,-0.689663,-0.3763,0.050015,-1.55801,-0.350346,1.26238,-1.29622,-1.49199,0.331379,-1.19654,-0.0526499,2.09321,-0.305022,0.291212,1.14711,0.410432,1.37125,0.39509,-0.481571,-0.712981,-0.729753,-0.729753,-0.730776,-0.730776,-0.689663,-0.670867,-0.733006,-0.73301,-0.727914,-0.787222,-0.787222,-0.719639,-0.719639,-0.3763,-0.729736,-0.68509,-0.763788,1.47616,0,0,0.427552,1,0,0,1,-0.764302,-0.801476,-0.155346,-0.701279,0,0,1.11396,-0.527252,-0.606883,0.144385,-0.521345,-99,-99,-99,-99,-0.21448,0.278925,0.120925,0.3149,0.123681,-99,-99,-99,-99,1.47616,1.47616,1.47616,1.47616,1.47616,-99,-99,-99,-99,0.427552,0.427552,0.427552,0.427552,0.427552,-99,-99,-99,-99,-0.913603,-1.50738,-1.56991,-0.442331,-0.251457,-0.489049,-0.706599,-0.789769,-0.655469,-0.637588,1,0,0,1,0,1,2,1,1,2 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+-14.9591,0.923912,0.359451,4,15,0,26.3064,-0.222029,-1.35074,-3.38736,0.00925241,0.574427,1.19672,1.36399,0.0574758,-0.336197,0.52752,-1.31113,-0.305963,-0.914436,0.286734,-0.566649,0.57007,0.406051,0.834525,1.41357,0.578561,-0.706352,-1.35284,-0.0520556,-0.197592,-0.301016,0.581847,-0.305746,0.0388904,-2.12027,-2.12027,-2.07753,-2.07753,-0.336197,-1.69315,-1.70401,-1.85691,0.167203,1.07449,1.07449,1.17945,1.17945,0.52752,1.4058,1.42459,1.26325,-0.222029,0.00925241,-3.38736,-1.35074,0.574427,0,0,1.19672,14.3672,-0.856929,-0.856929,0.437119,0.407021,-0.156244,0.157188,0.122456,0.251674,-99,-99,-99,-99,-0.794945,0.0350562,-0.120554,-0.153881,0.115501,-99,-99,-99,-99,0.574427,0.574427,0.574427,0.574427,0.574427,-99,-99,-99,-99,1.19672,1.19672,1.19672,1.19672,1.19672,-99,-99,-99,-99,-0.940754,-2.37422,-2.09448,-0.13239,-0.14935,-0.545054,-0.217411,-0.249781,-0.234383,-1.78448,1,0,0,0,0,2,2,2,2,1 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+-16.4812,0.991341,0.359451,4,15,0,35.9353,-0.832069,-0.652699,1.77711,-0.75318,0.158054,1.37504,-0.36084,0.306941,-0.826306,0.560809,0.0966838,-1.74117,-1.19342,-1.60898,1.05384,-0.0206111,-0.666369,-0.00485963,0.415214,-0.38892,0.878615,0.148702,-1.8315,-0.999193,-2.31576,0.346571,-2.14013,0.195189,0.276862,0.276862,0.287473,0.287473,-0.826306,0.158854,0.13741,0.114791,0.625394,1.00545,1.00545,0.861854,0.861854,0.560809,0.940227,0.924623,1.06935,-0.832069,-0.75318,1.77711,-0.652699,0.158054,0,0,1.37504,0.842481,0.349992,0.349992,0.649479,-1.08484,0.148226,-0.00289902,-0.102252,-0.000745692,-99,-99,-99,-99,-0.73351,-0.57562,-0.37997,-1.01554,0.137887,-99,-99,-99,-99,0.158054,0.158054,0.158054,0.158054,0.158054,-99,-99,-99,-99,1.37504,1.37504,1.37504,1.37504,1.37504,-99,-99,-99,-99,-0.344156,-0.503023,-0.565518,-0.79004,-0.846752,-2.15518,-0.0737511,-0.0610367,-0.128554,-3.18225,1,1,1,1,1,2,2,2,2,2 # -# Elapsed Time: 0.179 seconds (Warm-up) -# 0.179 seconds (Sampling) -# 0.358 seconds (Total) +# Elapsed Time: 0.15 seconds (Warm-up) +# 0.14 seconds (Sampling) +# 0.29 seconds (Total) # diff --git a/tests/testthat/fixtures/coevfit_example3.rds b/tests/testthat/fixtures/coevfit_example3.rds index 15866ad..57e4347 100644 Binary files a/tests/testthat/fixtures/coevfit_example3.rds and b/tests/testthat/fixtures/coevfit_example3.rds differ diff --git a/tests/testthat/fixtures/coevfit_example4-1.csv b/tests/testthat/fixtures/coevfit_example4-1.csv index 42529b3..2879ce3 100644 --- a/tests/testthat/fixtures/coevfit_example4-1.csv +++ b/tests/testthat/fixtures/coevfit_example4-1.csv @@ -1,8 +1,8 @@ # stan_version_major = 2 # stan_version_minor = 34 # stan_version_patch = 1 -# model = model_e3f3a5585b46b19c84ddc74fabb40eda_model -# start_datetime = 2024-07-30 14:30:43 UTC +# model = model_856207cf8bc257fd9e31f4f45b08793f_model +# start_datetime = 2024-08-09 20:08:39 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-3438bdf60a1.json +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/standata-515014a4e41.json # init = 2 (Default) # random # seed = 1 # output -# file = C:/Users/scla896/AppData/Local/Temp/Rtmp4K8hBb/model_e3f3a5585b46b19c84ddc74fabb40eda-202407301530-1-9055b4.csv +# file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_856207cf8bc257fd9e31f4f45b08793f-202408092108-1-9072cc.csv # diagnostic_file = (Default) # refresh = 100 (Default) # sig_figs = -1 (Default) -# profile_file = C:/Users/scla896/AppData/Local/Temp/Rtmp4K8hBb/model_e3f3a5585b46b19c84ddc74fabb40eda-profile-202407301530-1-29d855.csv +# profile_file = C:/Users/scla896/AppData/Local/Temp/RtmpkhVljh/model_856207cf8bc257fd9e31f4f45b08793f-profile-202408092108-1-29f56d.csv # save_cmdstan_config = 0 (Default) # num_threads = 1 (Default) # stanc_version = stanc3 v2.34.0 -# stancflags = --name=model_e3f3a5585b46b19c84ddc74fabb40eda_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,phi2,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_856207cf8bc257fd9e31f4f45b08793f_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,phi2,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 = 0.268236 +# Step size = 0.313701 # 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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seconds (Total) +# Elapsed Time: 0.14 seconds (Warm-up) +# 0.133 seconds (Sampling) +# 0.273 seconds (Total) # diff --git a/tests/testthat/fixtures/coevfit_example4.rds b/tests/testthat/fixtures/coevfit_example4.rds index d59a9b6..eb1e167 100644 Binary files a/tests/testthat/fixtures/coevfit_example4.rds and b/tests/testthat/fixtures/coevfit_example4.rds differ diff --git a/tests/testthat/fixtures/coevfit_example5-1.csv b/tests/testthat/fixtures/coevfit_example5-1.csv index e9b22a9..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_6cf6bba52136c763c066366c453b41f1_model -# start_datetime = 2024-07-30 14:30:44 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/Rtmp4K8hBb/standata-3438403c2d1f.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/Rtmp4K8hBb/model_6cf6bba52136c763c066366c453b41f1-202407301530-1-8bb5fe.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/Rtmp4K8hBb/model_6cf6bba52136c763c066366c453b41f1-profile-202407301530-1-0439b6.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_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_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 = 0.349327 +# 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 --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 --17.9531,1,0.349327,3,7,0,31.2243,-0.371508,-1.48267,1.78846,0.510985,0.608759,-0.388552,-0.112558,-1.74155,1.12489,-0.00134487,1.05396,-0.28404,-1.12272,-0.955423,0.652487,0.25058,-0.427749,-0.332588,-0.294277,-0.279701,-0.86835,-0.316354,-1.01841,-1.39103,-0.351846,-0.43973,-0.744871,-0.810264,-0.810264,-0.754035,-0.754035,-1.74155,-0.789531,-0.764661,-0.816307,0.196705,0.0625161,0.0625161,0.0831018,0.0831018,1.12489,0.128503,0.120675,0.0901761,0.510985,0,0,0.608759,1,0,0,1,-0.371508,0,1.78846,-1.48267,0,0,-0.827436,-0.236004,0.161174,0.0667274,-0.113906,-99,-99,-99,-99,-0.551573,-0.104893,-0.235576,-0.359298,-0.107272,-99,-99,-99,-99,0.510985,0.510985,0.510985,0.510985,0.510985,-99,-99,-99,-99,0.608759,0.608759,0.608759,0.608759,0.608759,-99,-99,-99,-99,-0.188607,-1.3473,-1.06946,-0.407415,-0.350526,-0.736478,-0.514078,-0.568675,-0.614719,-0.92236,0,1,0,0,0,2,2,1,1,2 --20.9224,0.966161,0.349327,4,15,0,30.3119,-1.18367,-0.607717,-0.112881,1.72348,0.226132,0.811357,-0.67597,0.983523,-0.425564,0.0701023,-0.409235,0.904147,-0.176055,1.4427,-0.743044,0.00124649,-0.430453,-0.641027,1.89849,-1.10795,-0.760309,-0.653754,1.90402,0.712161,1.6873,-1.19355,0.818221,1.01719,1.01719,0.853104,0.853104,0.983523,0.885851,0.868084,1.05404,-0.738314,-1.01321,-1.01321,-0.915164,-0.915164,-0.425564,-0.926761,-0.897258,-1.00552,1.72348,0,0,0.226132,1,0,0,1,-1.18367,0,-0.112881,-0.607717,0,0,-0.143034,0.618408,-0.318503,0.000570057,-0.196859,-99,-99,-99,-99,-0.273919,-0.105598,0.306173,0.122718,0.290966,-99,-99,-99,-99,1.72348,1.72348,1.72348,1.72348,1.72348,-99,-99,-99,-99,0.226132,0.226132,0.226132,0.226132,0.226132,-99,-99,-99,-99,-1.08667,-0.178009,-0.403623,-1.20844,-1.07416,-0.787911,-0.656472,-0.479189,-0.512572,-1.01781,1,1,1,1,0,1,2,2,1,1 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Elapsed Time: 0.108 seconds (Warm-up) +# 0.128 seconds (Sampling) +# 0.236 seconds (Total) # diff --git a/tests/testthat/fixtures/coevfit_example5.rds b/tests/testthat/fixtures/coevfit_example5.rds index f347f9d..0719047 100644 Binary files a/tests/testthat/fixtures/coevfit_example5.rds and b/tests/testthat/fixtures/coevfit_example5.rds differ diff --git a/tests/testthat/fixtures/coevfit_example6-1.csv b/tests/testthat/fixtures/coevfit_example6-1.csv index 481cdd5..62e2b4e 100644 --- a/tests/testthat/fixtures/coevfit_example6-1.csv +++ b/tests/testthat/fixtures/coevfit_example6-1.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 14:30:45 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/Rtmp4K8hBb/standata-3438656e1c0d.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/Rtmp4K8hBb/model_6cf6bba52136c763c066366c453b41f1-202407301530-1-596f50.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/Rtmp4K8hBb/model_6cf6bba52136c763c066366c453b41f1-profile-202407301530-1-7a310c.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_6cf6bba52136c763c066366c453b41f1_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,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.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 +# 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 = 0.322621 +# 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 --13.2728,0.930574,0.322621,4,15,0,17.8563,-0.357051,-1.50897,-0.241563,-0.779263,1.17914,0.979681,0.317455,1.35875,-0.690629,0.248516,1.86977,-1.38269,0.0919617,-0.363899,0.201945,-0.944641,-0.485696,-0.89475,1.15434,-0.398084,-0.413396,-0.302661,0.264018,-0.771885,-0.771885,-0.226919,-0.226919,-0.690629,-1.18418,-0.511566,1.04139,1.04139,0.874353,0.874353,0.248516,0.177386,0.217848,1.17914,0,0,0.979681,1,0,0,1,-0.357051,-0.779263,-0.241563,-1.50897,0,0,0.0812605,-0.321553,0.189233,-0.88518,-99,-99,-99,0.618709,-0.138045,-0.286052,0.0766023,-99,-99,-99,1.17914,1.17914,1.17914,1.17914,-99,-99,-99,0.979681,0.979681,0.979681,0.979681,-99,-99,-99,0,-1.38242,-0.674482,-0.284327,-0.221195,0,-0.544093,-1.09448,1,0,1,0,2,1,1,2 --13.1758,0.959777,0.322621,4,15,0,20.547,-2.10469,-0.398411,-0.0338958,0.507644,0.385791,1.01972,-0.504571,-1.10869,0.333053,-0.786268,-1.18603,0.809972,0.672101,0.123255,-0.603395,0.878012,1.80183,0.0830958,0.106986,-0.59581,0.547819,-0.811745,-1.73452,-0.125521,-0.125521,-0.161974,-0.161974,0.333053,0.350494,0.261115,-1.39211,-1.39211,-1.3499,-1.3499,-0.786268,-0.845692,-0.633978,0.385791,0,0,1.01972,1,0,0,1,-2.10469,0.507644,-0.0338958,-0.398411,0,0,0.200513,0.0367715,-0.181141,0.263582,-99,-99,-99,0.104252,-0.470692,0.441835,-0.655165,-99,-99,-99,0.385791,0.385791,0.385791,0.385791,-99,-99,-99,1.01972,1.01972,1.01972,1.01972,-99,-99,-99,0,-0.738506,-0.536234,-0.745241,-0.494549,0,-0.362975,-0.566994,1,0,0,1,1,1,1,1 --15.061,0.949357,0.322621,4,15,0,24.3168,-0.186724,-1.10737,-0.759117,1.12043,1.01372,1.06619,0.153652,0.854939,0.87352,1.2446,-0.239054,-0.903909,0.385863,0.267811,0.100016,-2.41038,0.259636,1.20488,1.37789,0.980901,-0.683602,-0.592171,1.06465,-0.388919,-0.388919,-0.0340694,-0.0340694,0.87352,0.38693,0.845587,1.0783,1.0783,1.14295,1.14295,1.2446,1.72806,1.27101,1.01372,0,0,1.06619,1,0,0,1,-0.186724,1.12043,-0.759117,-1.10737,0,0,0.319678,0.221875,0.087169,-2.10076,-99,-99,-99,0.974632,0.692765,-0.451776,-0.859005,-99,-99,-99,1.01372,1.01372,1.01372,1.01372,-99,-99,-99,1.06619,1.06619,1.06619,1.06619,-99,-99,-99,0,-0.780153,-0.720049,-0.111778,-0.316428,0,-0.89722,-0.377123,0,1,1,0,2,1,2,1 --11.6234,0.956257,0.322621,4,15,0,24.6861,-0.747593,-1.07092,-0.0435366,-0.178974,0.33165,0.353555,-0.641975,-0.760626,-1.03916,-0.409884,0.21619,1.54073,-0.899645,-0.019035,-1.2979,0.456564,-0.150955,-0.534671,-0.719786,-0.112189,0.679363,0.254737,-0.572929,-0.756614,-0.756614,-0.924566,-0.924566,-1.03916,-0.694825,-1.02728,-0.571272,-0.571272,-0.504372,-0.504372,-0.409884,-0.54684,-0.418634,0.33165,0,0,0.353555,1,0,0,1,-0.747593,-0.178974,-0.0435366,-1.07092,0,0,-0.359908,-0.00761506,-0.538043,0.189269,-99,-99,-99,-0.243429,-0.0412501,0.29966,0.0829645,-99,-99,-99,0.33165,0.33165,0.33165,0.33165,-99,-99,-99,0.353555,0.353555,0.353555,0.353555,-99,-99,-99,0,-1.14656,-0.208339,-0.391612,-0.821322,0,-0.525892,-0.771775,0,0,0,1,1,1,1,2 --22.3585,0.783205,0.322621,3,7,0,33.7234,-1.10812,-0.371255,0.783236,-0.939712,0.424734,0.163018,-2.32702,0.549777,-0.277239,0.421856,-1.19971,1.73626,-0.734119,-0.855446,0.94908,-2.25436,0.762464,-1.14173,-0.0606783,-2.35492,-0.303341,-1.04942,-0.0361507,-0.996086,-0.996086,-1.04292,-1.04292,-0.277239,-0.100669,-0.357123,1.12052,1.12052,1.42974,1.42974,0.421856,0.340913,0.45352,0.424734,0,0,0.163018,1,0,0,1,-1.10812,-0.939712,0.783236,-0.371255,0,0,-0.286924,-0.334344,0.377669,-0.897081,-99,-99,-99,0.0256359,-0.746128,-0.17165,-0.233865,-99,-99,-99,0.424734,0.424734,0.424734,0.424734,-99,-99,-99,0.163018,0.163018,0.163018,0.163018,-99,-99,-99,0,-1.565,-0.414851,-0.134272,-0.267407,0,-0.242245,-1.48799,1,0,1,1,1,2,1,2 --21.656,0.99315,0.322621,4,15,0,34.1933,-1.80754,-0.16227,-1.97063,0.295492,0.155052,0.403273,1.28453,-0.949203,-0.749302,0.502533,0.619024,-1.06573,1.48442,1.16492,-1.26042,0.6142,-0.498912,0.735584,-0.0328287,2.432,1.41133,0.248118,-0.13382,0.421576,0.421576,0.632969,0.632969,-0.749302,-0.658725,-0.716057,-0.292864,-0.292864,-0.466657,-0.466657,0.502533,0.554419,0.465437,0.155052,0,0,0.403273,1,0,0,1,-1.80754,0.295492,-1.97063,-0.16227,0,0,0.548303,0.430287,-0.516331,0.251607,-99,-99,-99,-0.581075,0.419313,1.0342,-0.169098,-99,-99,-99,0.155052,0.155052,0.155052,0.155052,-99,-99,-99,0.403273,0.403273,0.403273,0.403273,-99,-99,-99,0,-0.355308,-0.753166,-1.23021,-1.13017,0,-0.402735,-0.473347,1,1,1,1,1,2,1,2 --14.0847,0.601976,0.322621,4,15,0,30.7297,-0.821436,-0.883802,-0.44441,1.08402,2.43234,1.58803,0.85377,-1.1407,-0.873213,0.615145,0.0449626,-1.25105,0.0196127,1.09033,-1.50725,0.026697,-1.60313,0.042204,-0.0621494,0.542872,1.0826,-0.268204,-1.24706,-0.00508167,-0.00508167,0.315663,0.315663,-0.873213,-1.38696,-0.857465,-0.934351,-0.934351,-0.682239,-0.682239,0.615145,0.245392,0.421505,2.43234,0,0,1.58803,1,0,0,1,-0.821436,1.08402,-0.44441,-0.883802,0,0,0.0201455,1.11995,-1.58844,0.0281351,-99,-99,-99,-0.0497099,0.759819,0.559321,-0.239264,-99,-99,-99,2.43234,2.43234,2.43234,2.43234,-99,-99,-99,1.58803,1.58803,1.58803,1.58803,-99,-99,-99,0,-0.283642,-0.246901,-0.879749,-0.570269,0,-0.281361,-0.869115,0,1,1,0,1,2,1,2 --15.5238,0.979663,0.322621,4,15,0,25.8636,-0.292329,-0.787849,0.299456,1.66926,1.29493,1.35055,0.689541,-1.37963,-0.933672,1.32306,0.779342,-1.36436,-0.152079,0.804385,-0.697279,0.266751,-1.39222,0.948334,1.29389,0.490933,0.569443,0.0305674,-0.0387025,-0.585978,-0.585978,-0.0670533,-0.0670533,-0.933672,-1.33442,-0.846966,-1.17752,-1.17752,-0.815593,-0.815593,1.32306,1.10968,1.15455,1.29493,0,0,1.35055,1,0,0,1,-0.292329,1.66926,0.299456,-0.787849,0,0,-0.14994,0.793069,-0.74063,0.283335,-99,-99,-99,0.981811,0.924329,-0.0388098,0.232402,-99,-99,-99,1.29493,1.29493,1.29493,1.29493,-99,-99,-99,1.35055,1.35055,1.35055,1.35055,-99,-99,-99,0,-0.594953,-0.368725,-0.807124,-0.774728,0,-1.18196,-0.457513,0,1,0,1,1,1,1,1 --17.5566,0.948102,0.322621,4,15,0,25.7871,-0.361107,-0.669628,-1.63587,0.170544,0.142872,0.76195,-1.2494,0.902504,1.42346,-0.397108,-0.243866,1.78385,1.2501,1.77077,-0.161277,-0.11176,0.617225,-1.01853,0.745868,-0.964729,0.789185,0.881242,0.308475,0.290893,0.290893,-0.332253,-0.332253,1.42346,1.50368,1.40712,0.316099,0.316099,0.542011,0.542011,-0.397108,-0.658171,-0.339875,0.142872,0,0,0.76195,1,0,0,1,-0.361107,0.170544,-1.63587,-0.669628,0,0,0.684586,0.969718,-0.101405,-0.070271,-99,-99,-99,-0.158749,-1.1479,0.43517,0.457364,-99,-99,-99,0.142872,0.142872,0.142872,0.142872,-99,-99,-99,0.76195,0.76195,0.76195,0.76195,-99,-99,-99,0,-0.249576,-0.499643,-0.512003,-0.771562,0,-0.413679,-1.09711,1,1,1,0,2,2,2,1 --18.2376,0.987684,0.322621,4,15,0,26.6408,-0.336661,-0.356623,-1.81322,0.248727,0.16385,0.285473,-1.42219,1.49113,0.532021,-0.597924,0.591574,0.306802,0.529831,1.94501,-1.48469,0.763774,0.796865,-0.50532,1.26663,-0.652736,0.477757,-1.33044,-0.384265,-1.008,-1.008,-1.18844,-1.18844,0.532021,0.470051,0.548746,0.732807,0.732807,0.816899,0.816899,-0.597924,-0.444874,-0.547679,0.16385,0,0,0.285473,1,0,0,1,-0.336661,0.248727,-1.81322,-0.356623,0,0,0.245446,0.901033,-0.778045,0.400253,-99,-99,-99,0.308923,-0.666431,0.534917,-0.650824,-99,-99,-99,0.16385,0.16385,0.16385,0.16385,-99,-99,-99,0.285473,0.285473,0.285473,0.285473,-99,-99,-99,0,-0.748062,-0.130982,-0.374777,-0.215328,0,-0.162297,-1.00571,0,0,0,0,2,2,2,2 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+-21.6193,0.83874,0.332181,4,15,0,30.4373,-0.0998401,-0.108738,1.78778,-0.820386,0.483006,0.737017,1.38588,-0.49349,-1.72497,-0.214729,0.783372,-1.1874,2.50746,0.614789,1.20079,-0.736776,-0.203712,1.27126,0.842246,0.120298,-1.14589,-0.251328,-1.26477,0.120408,0.120408,-0.159771,-0.159771,-1.72497,-1.85421,-1.66909,0.425911,0.425911,0.052859,0.052859,-0.214729,0.281374,-0.222473,-0.0998401,-0.820386,1.78778,-0.108738,0.483006,0,0,0.737017,4.98895,0.143527,0.143527,2.30611,1.97338,0.48384,1.06053,-0.650719,-99,-99,-99,1.18763,0.237049,-0.42058,-0.364884,-99,-99,-99,0.483006,0.483006,0.483006,0.483006,-99,-99,-99,0.737017,0.737017,0.737017,0.737017,-99,-99,-99,0,-0.435985,-1.24169,-0.36786,-0.0547059,0,-0.342008,-1.27894,1,0,1,0,2,2,2,2 # -# Elapsed Time: 0.141 seconds (Warm-up) -# 0.154 seconds (Sampling) -# 0.295 seconds (Total) +# Elapsed Time: 0.08 seconds (Warm-up) +# 0.095 seconds (Sampling) +# 0.175 seconds (Total) # diff --git a/tests/testthat/fixtures/coevfit_example6.rds b/tests/testthat/fixtures/coevfit_example6.rds index 335d376..3513f4e 100644 Binary files a/tests/testthat/fixtures/coevfit_example6.rds and b/tests/testthat/fixtures/coevfit_example6.rds differ diff --git a/tests/testthat/fixtures/coevfit_example7-1.csv b/tests/testthat/fixtures/coevfit_example7-1.csv index 42c4eb2..2a11f29 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_5042ae4e67582b4f8ab6560b7bfe8a01_model +# start_datetime = 2024-08-09 20:11:05 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/RtmpkhVljh/standata-51505165d30.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/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/Rtmp4K8hBb/model_acfbc51126512d5f1eabc55a35956f60-profile-202407301532-1-335b39.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_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_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 = 0.218474 +# 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 --50.4774,0.918248,0.218474,4,15,0,68.4007,-0.530998,-2.07171,-2.39212,0.219643,0.928723,0.196529,0.592889,-0.484358,0.154851,0.263067,-0.598564,-1.17529,-0.417424,-2.02255,0.35797,0.248886,0.550931,-0.984039,0.615027,-0.974111,1.37709,1.29807,-0.100421,-0.941129,-0.271902,-0.245088,-0.915949,0.170538,1.48744,-0.00568948,-0.323331,-0.793324,0.0127211,0.00372662,-0.817223,-0.997759,0.503015,-0.446413,-0.31029,0.446049,0.656022,1,0.638838,0,0.769342,0.595506,0.0777456,0.0777456,0.123816,0.123816,0.154851,0.0709522,0.0343462,0.0238915,-0.133843,0.0193817,0.0193817,-0.0580909,-0.0580909,0.263067,0.0142792,-0.000410561,0.0912159,0.928723,0,0,0.196529,1,0,0,1,-0.530998,0.219643,-2.39212,-2.07171,0,0,-0.00253779,-0.353861,0.00166225,-0.445049,-0.199122,-0.165571,-0.326055,-0.410895,-0.164278,-0.343693,-1.54514,0.117238,0.081512,0.193762,-0.346085,-99,-99,-99,-99,0.280778,-0.0153017,-0.130364,-0.0422845,-0.0307492,-99,-99,-99,-99,0.928723,0.928723,0.928723,0.928723,0.928723,-99,-99,-99,-99,0.196529,0.196529,0.196529,0.196529,0.196529,-99,-99,-99,-99,-0.326352,-1.27852,-0.326352,-0.775738,-0.61686,-0.775738,-0.776841,-0.615921,-0.776841,-0.631441,-0.631441,-0.758913,-0.504485,-0.504485,-0.504485,-1.35654,-1.70639,-1.35654,-1.96107,-1.0336,-1.43299,-1.53587,-0.909471,-1.33269,-1.07091,-1.91201,-1.40607,-1.48821,-2.05679,-0.963787,0,0,0,0,1,0,0,1,1,0,0,0,1,0,1,3,1,4,3,4,1,1,4,1,4,1,3,4,1,1,1,0.638838,0.638838,1 --51.8544,0.896148,0.218474,4,15,0,68.3362,-0.933921,-1.05397,-2.37201,0.253828,2.71512,0.266038,0.93405,-0.00887752,0.208692,-0.192806,-0.535272,0.204647,-0.285203,-2.39186,0.778502,0.677922,0.671844,-1.58531,0.579158,-0.266853,0.683324,0.480351,0.713003,0.0393497,-0.33503,-0.541221,-0.512489,0.219284,0.897452,0.547464,-0.647662,-0.158435,0.586805,-0.42058,-0.824021,-1.24439,0.14929,0.298695,-0.835591,0.634071,0.927988,1,-0.304384,0,0.95255,0.779976,0.154394,0.154394,0.238668,0.238668,0.208692,0.179976,0.191393,0.11911,0.0256756,0.206742,0.206742,0.160656,0.160656,-0.192806,0.184407,0.179722,0.231932,2.71512,0,0,0.266038,1,0,0,1,-0.933921,0.253828,-2.37201,-1.05397,0,0,0.347131,-0.100459,-0.266678,-0.789033,0.189394,-0.727143,0.563462,-0.609599,0.483462,-0.822995,-2.63338,0.424978,0.370072,0.392173,-0.925386,-99,-99,-99,-99,0.132016,0.121598,0.00719133,-0.0601022,-0.0996249,-99,-99,-99,-99,2.71512,2.71512,2.71512,2.71512,2.71512,-99,-99,-99,-99,0.266038,0.266038,0.266038,0.266038,0.266038,-99,-99,-99,-99,-0.200272,-1.70654,-0.200272,-0.482091,-0.961003,-0.482091,-0.830325,-0.572537,-0.830325,-0.617176,-0.617176,-0.775368,-0.475088,-0.475088,-0.475088,-1.7526,-1.67444,-1.7526,-0.695976,-1.62386,-1.81107,-1.99801,-0.753264,-1.72461,-1.38471,-0.862095,-1.78433,-2.1821,-1.83358,-0.576169,0,0,0,0,1,1,1,0,0,0,0,1,0,0,0,1,1,1,1,1,4,4,4,3,3,2,1,1,1,1,1,-0.304384,-0.304384,1 --49.9792,0.757454,0.218474,4,15,0,73.0051,-0.624234,-1.30562,0.0491359,0.00854145,0.226679,1.29563,-0.548949,1.24484,-1.00876,-0.536818,-0.407753,-0.360683,-0.553889,-0.658068,1.56427,0.344154,0.223209,0.415703,-0.902533,-0.378177,0.109434,-0.406221,0.490559,0.0637473,0.189958,1.19116,-2.58498,-0.745559,1.33851,0.156253,0.52575,-1.71,-0.452731,-0.124935,-1.00117,1.1432,1.24594,0.319908,0.612748,0.449833,0.501309,1,0.456662,0,0.88964,-0.942401,-1.11023,-1.11023,-1.08017,-1.08017,-1.00876,-1.09298,-1.09848,-1.12851,0.544959,0.055156,0.055156,0.0283053,0.0283053,-0.536818,-0.147639,-0.160222,-0.0998668,0.226679,0,0,1.29563,1,0,0,1,-0.624234,0.00854145,0.0491359,-1.30562,0,0,0.0702876,-0.769212,-0.0561999,0.514248,0.143905,0.270247,-0.593378,-0.475108,0.817381,0.346512,-0.237039,0.251362,0.0553017,0.0384672,0.071641,-99,-99,-99,-99,-0.290981,0.185038,0.0244073,0.075408,0.469756,-99,-99,-99,-99,0.226679,0.226679,0.226679,0.226679,0.226679,-99,-99,-99,-99,1.29563,1.29563,1.29563,1.29563,1.29563,-99,-99,-99,-99,-0.285057,-1.39421,-0.285057,-1.80732,-0.179239,-1.80732,-0.284568,-1.39569,-0.284568,-0.463798,-0.463798,-0.991258,-0.351507,-0.351507,-0.351507,-1.73407,-1.18098,-1.73407,-1.86077,-2.33375,-0.818026,-0.828848,-2.29558,-1.16259,-3.53565,-0.92348,-0.811943,-0.794806,-0.970309,-3.46144,0,0,0,0,0,0,1,1,1,0,0,1,1,0,0,2,3,4,3,1,2,4,2,2,2,4,4,3,3,3,1,0.456662,0.456662,1 --52.341,0.85358,0.218474,4,15,0,73.7638,-0.437514,-1.44485,1.24386,-0.132049,1.81682,1.17137,-1.01381,-1.17669,0.539992,0.341427,-0.564421,-0.422582,-0.121897,0.578036,0.76081,-1.29305,-0.188096,0.539641,-1.064,-0.431514,1.12865,1.25011,-0.708965,-1.4047,1.72477,0.597402,-1.94785,-0.783031,0.0734663,-1.04793,0.460872,0.409875,-0.230524,2.17498,-0.0463612,1.13859,0.650218,-0.181566,-0.404556,0.677325,0.910957,1,-0.650924,0,0.759143,-0.712696,-1.39574,-1.39574,-1.41694,-1.41694,0.539992,-1.12984,-1.15066,-1.19522,-0.528115,-0.930802,-0.930802,-1.08987,-1.08987,0.341427,-1.1656,-1.18022,-0.97649,1.81682,0,0,1.17137,1,0,0,1,-0.437514,-0.132049,1.24386,-1.44485,0,0,-0.70979,0.277619,1.47317,0.771196,-0.122979,0.9401,-0.40246,-1.32175,-0.225487,-0.172107,0.66679,0.350647,-0.595949,-0.0931967,0.267379,-99,-99,-99,-99,0.814903,-0.234125,-0.509113,0.634116,0.23154,-99,-99,-99,-99,1.81682,1.81682,1.81682,1.81682,1.81682,-99,-99,-99,-99,1.17137,1.17137,1.17137,1.17137,1.17137,-99,-99,-99,-99,-0.385047,-1.14074,-0.385047,-1.14877,-0.381298,-1.14877,-0.467126,-0.985644,-0.467126,-0.390432,-0.390432,-1.12937,-0.246953,-0.246953,-0.246953,-2.55056,-0.274259,-2.55056,-1.81801,-0.901366,-1.89026,-2.71795,-0.36684,-1.68408,-1.51486,-1.14007,-1.58168,-1.66362,-1.39027,-1.25356,0,0,0,0,0,0,1,1,0,1,1,0,1,1,0,4,1,4,2,1,2,1,1,1,4,4,4,3,4,1,1,-0.650924,-0.650924,1 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# -# Elapsed Time: 0.459 seconds (Warm-up) -# 0.291 seconds (Sampling) -# 0.75 seconds (Total) +# Elapsed Time: 0.194 seconds (Warm-up) +# 0.169 seconds (Sampling) +# 0.363 seconds (Total) # diff --git a/tests/testthat/fixtures/coevfit_example7.rds b/tests/testthat/fixtures/coevfit_example7.rds index dd664fa..deb55ed 100644 Binary files a/tests/testthat/fixtures/coevfit_example7.rds and b/tests/testthat/fixtures/coevfit_example7.rds differ diff --git a/vignettes/authority-delta-theta-1.png b/vignettes/authority-delta-theta-1.png index d797ab1..888edf0 100644 Binary files a/vignettes/authority-delta-theta-1.png and b/vignettes/authority-delta-theta-1.png differ diff --git a/vignettes/authority-delta-theta-with-distance-1.png b/vignettes/authority-delta-theta-with-distance-1.png index 6b2e713..fa70a84 100644 Binary files a/vignettes/authority-delta-theta-with-distance-1.png and b/vignettes/authority-delta-theta-with-distance-1.png differ diff --git a/vignettes/authority-flowfield-1.png b/vignettes/authority-flowfield-1.png index e7165fb..5b2e378 100644 Binary files a/vignettes/authority-flowfield-1.png and b/vignettes/authority-flowfield-1.png differ diff --git a/vignettes/authority-selection-gradient-1.png b/vignettes/authority-selection-gradient-1.png index c46df39..68b2829 100644 Binary files a/vignettes/authority-selection-gradient-1.png and b/vignettes/authority-selection-gradient-1.png differ diff --git a/vignettes/coevolve.Rmd b/vignettes/coevolve.Rmd index e61ac4e..399bc76 100644 --- a/vignettes/coevolve.Rmd +++ b/vignettes/coevolve.Rmd @@ -3,7 +3,7 @@ title: "Introduction to the coevolve package" output: rmarkdown::html_vignette: toc: true -date: "2024-08-03" +date: "2024-08-09" author: "Scott Claessens" vignette: > %\VignetteIndexEntry{Introduction to the coevolve package} @@ -90,34 +90,638 @@ and `coev_make_standata()` functions, but for now we just use the `coev_fit()` function. +``` r +fit1 <- + coev_fit( + data = authority$data, + variables = 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) +``` + +
+plot of chunk authority-delta-theta +
+ +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 c8fc79c..415f70c 100644 Binary files a/vignettes/primates-delta-theta-1.png and b/vignettes/primates-delta-theta-1.png differ