def build_results_list_for_binary_bayes(results_dir, N, num_covs, nphi, cr, is_two_group):
'''
Builds and returns a results ArrayList from the BRUGs output (the top level is assumed to be the results_dir parameter).
This list constructed so as to be ready to be passed off to the ReportGenerator on the C# side.
'''
results = arraylist()
results.Add(build_overall_table(results_dir, nphi, num_covs, cr, is_two_group))
# now build the posterior control group table -- this is 'pc'
if is_two_group:
pc_matrix = System.Array.CreateInstance(float, N, 6)
pc_rows = build_arrs_from_file(results_dir + "//pc//buffer.txt")
for i in range(len(pc_rows)):
insert_arr_into_mat(pc_matrix, i, pc_rows[i])
results.Add(pc_matrix)
# finally, build the treatment effects table -- this is d.exp
treat_matrix = System.Array.CreateInstance(float, N, 6)
treat_rows = None
mstr = "or"
if is_two_group and mstr != "rd":
treat_rows = build_arrs_from_file(results_dir + "//d.exp//buffer.txt")
else:
treat_rows = build_arrs_from_file(results_dir + "//p//buffer.txt")
for i in range(len(treat_rows)):
insert_arr_into_mat(treat_matrix, i, treat_rows[i])
results.Add(treat_matrix)
return results
def build_results_list_for_binary_bayes(results_dir, N, num_covs, nphi, cr, is_two_group):
'''
Builds and returns a results ArrayList from the BRUGs output (the top level is assumed to be the results_dir parameter).
This list constructed so as to be ready to be passed off to the ReportGenerator on the C# side.
'''
results = arraylist()
results.Add(build_overall_table(results_dir, nphi, num_covs, cr, is_two_group))
# now build the posterior control group table -- this is 'pc'
if is_two_group:
pc_matrix = System.Array.CreateInstance(float, N, 6)
pc_rows = build_arrs_from_file(results_dir + "//pc//buffer.txt")
for i in range(len(pc_rows)):
insert_arr_into_mat(pc_matrix, i, pc_rows[i])
results.Add(pc_matrix)
# finally, build the treatment effects table -- this is d.exp
treat_matrix = System.Array.CreateInstance(float, N, 6)
treat_rows = None
mstr = "or"
if is_two_group and mstr != "rd":
treat_rows = build_arrs_from_file(results_dir + "//d.exp//buffer.txt")
else:
treat_rows = build_arrs_from_file(results_dir + "//p//buffer.txt")
for i in range(len(treat_rows)):
insert_arr_into_mat(treat_matrix, i, treat_rows[i])
results.Add(treat_matrix)
return results
def build_results_list_for_cont_bayes(results_dir, N, num_covs, nphi, cr, is_two_group):
results = arraylist()
overall_effects = System.Array.CreateInstance(float, nphi, 6)
vars_for_overall_table = None
if is_two_group:
vars_for_overall_table = ["muc", "delta"]
else:
vars_for_overall_table = ["Ysum"]
for c in range(num_covs):
vars_for_overall_table.append("cov%s" % c)
if cr > 0:
vars_for_overall_table.append("beta0")
add_tau = ["tau2"]
if is_two_group:
add_tau.insert(0, "tauc2")
vars_for_overall_table.extend(add_tau)
for i in range(len(vars_for_overall_table)):
insert_arr_into_mat(overall_effects, i, ma_format_arr(get_first_line_arr(results_dir + "//%s//buffer.txt" % vars_for_overall_table[i])))
results.Add(overall_effects)
# build the treatment effects table -- this is delta.star
treat_matrix = System.Array.CreateInstance(float, N, 6)
treat_rows = None
if is_two_group:
treat_rows = build_arrs_from_file(results_dir + "//delta.star//buffer.txt")
else:
treat_rows = build_arrs_from_file(results_dir + "//theta//buffer.txt")
for i in range(len(treat_rows)):
insert_arr_into_mat(treat_matrix, i, treat_rows[i])
results.Add(treat_matrix)
# now build the posterior control group table -- this is 'pc'
if is_two_group:
pc_matrix = System.Array.CreateInstance(float, N, 6)
pc_rows = build_arrs_from_file(results_dir + "//theta.c//buffer.txt")
for i in range(len(pc_rows)):
insert_arr_into_mat(pc_matrix, i, pc_rows[i])
results.Add(pc_matrix)
return results
def build_results_list_for_cont_bayes(results_dir, N, num_covs, nphi, cr, is_two_group):
results = arraylist()
overall_effects = System.Array.CreateInstance(float, nphi, 6)
vars_for_overall_table = None
if is_two_group:
vars_for_overall_table = ["muc", "delta"]
else:
vars_for_overall_table = ["Ysum"]
for c in range(num_covs):
vars_for_overall_table.append("cov%s" % c)
if cr > 0:
vars_for_overall_table.append("beta0")
add_tau = ["tau2"]
if is_two_group:
add_tau.insert(0, "tauc2")
vars_for_overall_table.extend(add_tau)
for i in range(len(vars_for_overall_table)):
insert_arr_into_mat(overall_effects, i, ma_format_arr(get_first_line_arr(results_dir + "//%s//buffer.txt" % vars_for_overall_table[i])))
results.Add(overall_effects)
# build the treatment effects table -- this is delta.star
treat_matrix = System.Array.CreateInstance(float, N, 6)
treat_rows = None
if is_two_group:
treat_rows = build_arrs_from_file(results_dir + "//delta.star//buffer.txt")
else:
treat_rows = build_arrs_from_file(results_dir + "//theta//buffer.txt")
for i in range(len(treat_rows)):
insert_arr_into_mat(treat_matrix, i, treat_rows[i])
results.Add(treat_matrix)
# now build the posterior control group table -- this is 'pc'
if is_two_group:
pc_matrix = System.Array.CreateInstance(float, N, 6)
pc_rows = build_arrs_from_file(results_dir + "//theta.c//buffer.txt")
for i in range(len(pc_rows)):
insert_arr_into_mat(pc_matrix, i, pc_rows[i])
results.Add(pc_matrix)
return results
def build_results_list_for_diagnostic_bayes(results_dir, metrics, N, num_covs, nphis, cr):
all_results = arraylist()
results = arraylist()
forest_plot_stuff = arraylist()
outcome_index = 0
for outcome in metrics:
is_two_group = False
if outcome in ["dor", "lrp", "lrn"]:
is_two_group = True
cur_outcome_table = build_diagnostic_bayes_outcome_table(results_dir, outcome, N, cr, num_covs, is_two_group)
results.Add(cur_outcome_table)
#
# now deal with forest plot stuff
#
# this will include: [lowers, means, uppers, overalls]
stuff_for_this_forest_plot = arraylist()
# here we build double[] arrays to hand off for the forest plot. we need lowers, means and uppers, as well
# as overalls, for each metric. first we add results for each study.
lowers, means, uppers = System.Array.CreateInstance(float, N), System.Array.CreateInstance(float, N), System.Array.CreateInstance(float, N)
lower_index, mean_index, upper_index = 0, 2, 4
for study_i in range(N):
lowers[study_i] = cur_outcome_table[study_i, lower_index]
means[study_i] = cur_outcome_table[study_i, mean_index]
uppers[study_i] = cur_outcome_table[study_i, upper_index]
for double_arr in [lowers, means, uppers]:
stuff_for_this_forest_plot.Add(double_arr)
# now add the overall estimate
overall = System.Array.CreateInstance(float, 3)
# the summary statistic is psum if 'one group' model; delta.exp otherwise
overall_var = "psum"
if is_two_group:
overall_var = "delta.exp"
#overall_var = "delta"
overall_arr = ma_format_arr(get_first_line_arr(results_dir + outcome + "//%s//buffer.txt" % overall_var))
overall[0] = overall_arr[2]
overall[1] = overall_arr[0]
overall[2] = overall_arr[4]
stuff_for_this_forest_plot.Add(overall)
forest_plot_stuff.Add(stuff_for_this_forest_plot)
outcome_index += 1
all_results.Add(results)
all_results.Add(forest_plot_stuff)
return all_results
def generate_model_file_cont(out_path, cr, num_covs, is_two_group, generic):
out_str = None
# build string for covariate regression
cov_str = ""
if num_covs:
cov_str = " + " + build_reg_str_for_covs(num_covs)
if is_two_group:
out_str = cont_bayes_two_group_linear_cr_str
if cr == "none":
out_str = out_str.replace("beta0 ~ dnorm(0.0,precnorm)", "")
if num_covs:
out_str = out_str.replace("mu[i] <- delta + beta0 *(theta.c[i] - muc)", "mu[i] <- delta" + cov_str)
out_str += build_assignments_for_covs(num_covs)
else:
# no control rate, no covariates.
out_str = out_str.replace("delta.star[i] ~ dnorm(mu[i], inv.tau2 )", "delta.star[i] ~ dnorm(delta, inv.tau2 )")
out_str = out_str.replace("mu[i] <- delta + beta0 *(theta.c[i] - muc)", "mu[i] <- delta")
else:
if num_covs:
out_str = out_str.replace("mu[i] <- delta + beta0 *(theta.c[i] - muc)", "mu[i] <- delta + beta0 *(theta.c[i] - muc)" + cov_str)
out_str += build_assignments_for_covs(num_covs)
else:
def build_results_list_for_diagnostic_bayes(results_dir, metrics, N, num_covs, nphis, cr):
all_results = arraylist()
results = arraylist()
forest_plot_stuff = arraylist()
outcome_index = 0
for outcome in metrics:
is_two_group = False
if outcome in ["dor", "lrp", "lrn"]:
is_two_group = True
cur_outcome_table = build_diagnostic_bayes_outcome_table(results_dir, outcome, N, cr, num_covs, is_two_group)
results.Add(cur_outcome_table)
#
# now deal with forest plot stuff
#
# this will include: [lowers, means, uppers, overalls]
stuff_for_this_forest_plot = arraylist()
# here we build double[] arrays to hand off for the forest plot. we need lowers, means and uppers, as well
# as overalls, for each metric. first we add results for each study.
lowers, means, uppers = System.Array.CreateInstance(float, N), System.Array.CreateInstance(float, N), System.Array.CreateInstance(float, N)
lower_index, mean_index, upper_index = 0, 2, 4
for study_i in range(N):
lowers[study_i] = cur_outcome_table[study_i, lower_index]
means[study_i] = cur_outcome_table[study_i, mean_index]
uppers[study_i] = cur_outcome_table[study_i, upper_index]
for double_arr in [lowers, means, uppers]:
stuff_for_this_forest_plot.Add(double_arr)
# now add the overall estimate
overall = System.Array.CreateInstance(float, 3)
# the summary statistic is psum if 'one group' model; delta.exp otherwise
overall_var = "psum"
if is_two_group:
overall_var = "delta.exp"
#overall_var = "delta"
overall_arr = ma_format_arr(get_first_line_arr(results_dir + outcome + "//%s//buffer.txt" % overall_var))
overall[0] = overall_arr[2]
overall[1] = overall_arr[0]
overall[2] = overall_arr[4]
stuff_for_this_forest_plot.Add(overall)
forest_plot_stuff.Add(stuff_for_this_forest_plot)
outcome_index += 1
all_results.Add(results)
all_results.Add(forest_plot_stuff)
return all_results
def generate_model_file_cont(out_path, cr, num_covs, is_two_group, generic):
out_str = None
# build string for covariate regression
cov_str = ""
if num_covs:
cov_str = " + " + build_reg_str_for_covs(num_covs)
if is_two_group:
out_str = cont_bayes_two_group_linear_cr_str
if cr == "none":
out_str = out_str.replace("beta0 ~ dnorm(0.0,precnorm)", "")
if num_covs:
out_str = out_str.replace("mu[i] <- delta + beta0 *(theta.c[i] - muc)", "mu[i] <- delta" + cov_str)
out_str += build_assignments_for_covs(num_covs)
else:
# no control rate, no covariates.
out_str = out_str.replace("delta.star[i] ~ dnorm(mu[i], inv.tau2 )", "delta.star[i] ~ dnorm(delta, inv.tau2 )")
out_str = out_str.replace("mu[i] <- delta + beta0 *(theta.c[i] - muc)", "mu[i] <- delta")
else:
if num_covs:
out_str = out_str.replace("mu[i] <- delta + beta0 *(theta.c[i] - muc)", "mu[i] <- delta + beta0 *(theta.c[i] - muc)" + cov_str)
out_str += build_assignments_for_covs(num_covs)
else:
