321-collated-nomenclature

bmds/bmds3/constants.py

@@ -127,14 +127,14 @@ class ContinuousModelChoices(Enum):
     )
     c_exp_m3 = ContinuousModel(
         id=ContinuousModelIds.c_exp_m3.value,
-        verbose="ExponentialM3",
+        verbose="Exponential 3",
         params=("a", "b", "c", "d"),
         variance_params=("rho", "log-alpha"),
         model_form_str="P[dose] = a * exp(±1 * (b * dose) ^ d)",
     )
     c_exp_m5 = ContinuousModel(
         id=ContinuousModelIds.c_exp_m5.value,
-        verbose="ExponentialM5",
+        verbose="Exponential 5",
         params=("a", "b", "c", "d"),
         variance_params=("rho", "log-alpha"),
         model_form_str="P[dose] = a * (c - (c - 1) * exp(-(b * dose) ^ d)",

bmds/bmds3/models/base.py

@@ -198,7 +198,7 @@ def cdf_plot(self, figsize: tuple[float, float] | None = None):
         fig = plotting.create_empty_figure(figsize=figsize)
         ax = fig.gca()
         ax.set_xlabel(self.dataset.get_xlabel())
-        ax.set_ylabel("Percentile")
+        ax.set_ylabel("Cumulative Probability")
         ax.plot(
             self.results.fit.bmd_dist[0],
             self.results.fit.bmd_dist[1],

bmds/bmds3/models/continuous.py

@@ -184,7 +184,7 @@ class Polynomial(BmdModelContinuous):
     degree_required: bool = True
 
     def name(self) -> str:
-        return self.settings.name or f"Polynomial {self.settings.degree}°"
+        return self.settings.name or f"Polynomial {self.settings.degree}"
 
     def get_model_settings(
         self, dataset: ContinuousDatasets, settings: InputModelSettings

bmds/bmds3/reporting.py

@@ -254,8 +254,8 @@ def write_frequentist_table(report: Report, session: BmdsSession):
     write_cell(tbl.cell(0, 3), "BMDU", style=hdr)
     write_pvalue_header(tbl.cell(0, 4), style=hdr)
     write_cell(tbl.cell(0, 5), "AIC", style=hdr)
-    write_cell(tbl.cell(0, 6), "Scaled Residual for Dose Group near BMD", style=hdr)
-    write_cell(tbl.cell(0, 7), "Scaled Residual for Control Dose Group", style=hdr)
+    write_cell(tbl.cell(0, 6), "Scaled Residual near BMD", style=hdr)
+    write_cell(tbl.cell(0, 7), "Scaled Residual at Control", style=hdr)
     write_cell(tbl.cell(0, 8), "Recommendation and Notes", style=hdr)
 
     # write body
@@ -382,8 +382,8 @@ def write_bayesian_table(report: Report, session: BmdsSession):
     write_cell(tbl.cell(0, 4), "BMD", style=hdr)
     write_cell(tbl.cell(0, 5), "BMDU", style=hdr)
     write_cell(tbl.cell(0, 6), "Unnormalized Log Posterior Probability", style=hdr)
-    write_cell(tbl.cell(0, 7), "Scaled Residual for Dose Group near BMD", style=hdr)
-    write_cell(tbl.cell(0, 8), "Scaled Residual for Control Dose Group", style=hdr)
+    write_cell(tbl.cell(0, 7), "Scaled Residual near BMD", style=hdr)
+    write_cell(tbl.cell(0, 8), "Scaled Residual at Control", style=hdr)
 
     ma = session.model_average
     for idx, model in enumerate(session.models, start=1):
@@ -441,8 +441,8 @@ def write_model(report: Report, model: BmdModel, bmd_cdf_table: bool, header_lev
 
 
 def write_bmd_cdf_table(report: Report, model: BmdModel):
-    df = pd.DataFrame(data=model.results.fit.bmd_dist.T, columns=["BMD", "Percentile"])
-    df_to_table(report, df[["Percentile", "BMD"]])
+    df = pd.DataFrame(data=model.results.fit.bmd_dist.T, columns=["BMD", "Cumulative Probability"])
+    df_to_table(report, df[["Cumulative Probability", "BMD"]])
 
 
 def write_setting_p(report: Report, title: str, value: str):

bmds/bmds3/sessions.py

@@ -319,7 +319,7 @@ def to_docx(
                 reporting.write_models(report, self, bmd_cdf_table, header_level + 2)
 
         else:
-            report.document.add_paragraph("Frequentist Summary", h2)
+            report.document.add_paragraph("Maximum Likelihood Approach Summary", h2)
             reporting.write_base_frequentist_table(report, self)
             if all_models:
                 report.document.add_paragraph("Individual Model Results", h2)

bmds/bmds3/types/continuous.py

@@ -71,16 +71,31 @@ def confidence_level(self) -> float:
     def distribution(self) -> str:
         return f"{self.disttype.distribution_type} + {self.disttype.variance_model}"
 
+    @property
+    def is_hybrid(self) -> bool:
+        if self.bmr_type in [ContinuousRiskType.HybridExtra, ContinuousRiskType.HybridAdded]:
+            return True
+
     def tbl(self, show_degree: bool = True) -> str:
         data = [
             ["BMR", self.bmr_text],
             ["Distribution", self.distribution],
             ["Modeling Direction", self.direction],
-            ["Confidence Level", self.confidence_level],
-            ["Tail Probability", self.tail_prob],
-            ["Modeling Approach", self.priors.prior_class.name],
+            ["Confidence Level (one-sided)", self.confidence_level],
         ]
 
+        if self.priors.prior_class.name in ["frequentist_restricted", "frequentist_unrestricted"]:
+            data.append(
+                ["Modeling Approach", "MLE"],
+            )
+        else:
+            data.append(
+                ["Modeling Approach", "Bayesian"],
+            )
+
+        if self.is_hybrid:
+            data.append(["Tail Probability", self.tail_prob])
+
         if show_degree:
             data.append(["Degree", self.degree])
 
@@ -90,15 +105,17 @@ def tbl(self, show_degree: bool = True) -> str:
         return pretty_table(data, "")
 
     def docx_table_data(self) -> list:
-        return [
+        data = [
             ["Setting", "Value"],
             ["BMR", self.bmr_text],
             ["Distribution", self.distribution],
-            ["Modeling Direction", self.direction],
+            ["Adverse Direction", self.direction],
             ["Maximum Polynomial Degree", self.degree],
-            ["Confidence Level", self.confidence_level],
-            ["Tail Probability", self.tail_prob],
+            ["Confidence Level (one-sided)", self.confidence_level],
         ]
+        if self.is_hybrid:
+            data.append(["Tail Probability", self.tail_prob])
+        return data
 
     def update_record(self, d: dict) -> None:
         """Update data record for a tabular-friendly export"""
@@ -250,8 +267,6 @@ class ContinuousParameters(BaseModel):
     names: list[str]
     values: NumpyFloatArray
     se: NumpyFloatArray
-    lower_ci: NumpyFloatArray
-    upper_ci: NumpyFloatArray
     bounded: NumpyFloatArray
     cov: NumpyFloatArray
     prior_type: NumpyIntArray
@@ -295,8 +310,6 @@ def from_model(cls, model) -> Self:
             values=result.parms,
             bounded=summary.bounded,
             se=summary.stdErr[:slice],
-            lower_ci=summary.lowerConf[:slice],
-            upper_ci=summary.upperConf[:slice],
             cov=cov,
             prior_type=priors[0],
             prior_initial_value=priors[1],
@@ -306,25 +319,21 @@ def from_model(cls, model) -> Self:
         )
 
     def tbl(self) -> str:
-        headers = "Variable|Estimate|Bounded|Std Error|Lower CI|Upper CI".split("|")
+        headers = "Variable|Estimate|On Bound|Std Error".split("|")
         data = []
-        for name, value, bounded, se, lower_ci, upper_ci in zip(
+        for name, value, bounded, se in zip(
             self.names,
             self.values,
             self.bounded,
             self.se,
-            self.lower_ci,
-            self.upper_ci,
             strict=True,
         ):
             data.append(
                 (
                     name,
                     value,
                     BOOL_ICON[bounded],
-                    "NA" if bounded else f"{se:g}",
-                    "NA" if bounded else f"{lower_ci:g}",
-                    "NA" if bounded else f"{upper_ci:g}",
+                    "Not Reported" if bounded else f"{se:g}",
                 )
             )
         return pretty_table(data, headers)
@@ -344,8 +353,6 @@ def rows(self, extras: dict) -> list[dict]:
                         name=self.names[i],
                         value=self.values[i],
                         se=self.se[i],
-                        lower_ci=self.lower_ci[i],
-                        upper_ci=self.upper_ci[i],
                         bounded=bool(self.bounded[i]),
                         initial_distribution=PriorType(self.prior_type[i]).name,
                         initial_value=self.prior_initial_value[i],
@@ -397,8 +404,8 @@ def from_model(cls, model) -> Self:
         )
 
     def tbl(self, disttype: constants.DistType) -> str:
-        mean_headers = "Dose|Size|Observed Mean|Calculated Mean|Estimated Mean|Scaled Residual"
-        sd_headers = "Dose|Size|Observed SD|Calculated SD|Estimated SD"
+        mean_headers = "Dose|N|Sample Mean|Model fitted Mean|Scaled Residual"
+        sd_headers = "Dose|N|Sample SD|Model fitted SD"
         if disttype == constants.DistType.log_normal:
             mean_headers = mean_headers.replace("ted Mean", "ted Median")
             sd_headers = sd_headers.replace("ted SD", "ted GSD")
@@ -410,7 +417,6 @@ def tbl(self, disttype: constants.DistType) -> str:
                     self.dose[idx],
                     self.size[idx],
                     self.obs_mean[idx],
-                    self.calc_mean[idx],
                     self.est_mean[idx],
                     self.residual[idx],
                 ]
@@ -420,7 +426,6 @@ def tbl(self, disttype: constants.DistType) -> str:
                     self.dose[idx],
                     self.size[idx],
                     self.obs_sd[idx],
-                    self.calc_sd[idx],
                     self.est_sd[idx],
                 ]
             )
@@ -478,7 +483,7 @@ def from_model(cls, model) -> Self:
         )
 
     def tbl(self) -> str:
-        headers = "Name|Loglikelihood Ratio|Test DOF|P-Value".split("|")
+        headers = "Name|-2* Log(Likelihood Ratio)|Test d.f.|P-Value".split("|")
         data = []
         for name, ll_ratio, df, p_value in zip(
             self.names, self.ll_ratios, self.dfs, self.p_values, strict=True
@@ -529,28 +534,37 @@ def tbl(self) -> str:
             ["BMDL", self.bmdl],
             ["BMDU", self.bmdu],
             ["AIC", self.fit.aic],
-            ["Log Likelihood", self.fit.loglikelihood],
+            ["-2* Log(Likelihood Ratio)", self.fit.loglikelihood],
             ["P-Value", self.tests.p_values[3]],
-            ["Model DOF", self.tests.dfs[3]],
+            ["Model d.f.", self.tests.dfs[3]],
         ]
         return pretty_table(data, "")
 
+    def bound_footnote(self) -> str:
+        if any(self.parameters.bounded):
+            return """
+                Standard errors estimates are not generated for parameters estimated on corresponding bounds,
+                although sampling error is present for all parameters, as a rule. Standard error estimates may not be
+                reliable as a basis for confidence intervals or tests when one or more parameters are on bounds
+                """
+
     def text(self, dataset: ContinuousDatasets, settings: ContinuousModelSettings) -> str:
         return multi_lstrip(
             f"""
-        Summary:
+        Modeling Summary:
         {self.tbl()}
 
         Model Parameters:
         {self.parameters.tbl()}
+        {self.bound_footnote()}
 
         Goodness of Fit:
         {self.gof.tbl(disttype=settings.disttype)}
 
-        Likelihoods of Interest:
+        Likelihoods:
         {self.deviance.tbl()}
 
-        Tests of Interest:
+        Tests of Mean and Variance Fits:
         {self.tests.tbl()}
         """
         )

bmds/bmds3/types/dichotomous.py

@@ -345,7 +345,7 @@ def from_model(cls, model) -> Self:
         )
 
     def tbl(self) -> str:
-        headers = "Model|Log Likelihood|# Params|Deviance|Test DOF|P-Value".split("|")
+        headers = "Model|Log Likelihood|# Params|Deviance|Test d.f.|P-Value".split("|")
         data = []
         for i in range(len(self.names)):
             # manually format columns b/c tabulate won't format if first row text is str

bmds/bmds3/types/nested_dichotomous.py

@@ -328,7 +328,7 @@ def tbl(self) -> str:
             ["BMDL", self.summary.bmdl],
             ["BMDU", self.summary.bmdu],
             ["AIC", self.summary.aic],
-            ["P-value", self.combined_pvalue],
+            ["P-Value", self.combined_pvalue],
             ["D.O.F", self.dof],
             ["Chi²", self.summary.chi_squared],
             ["Log-likelihood", self.ll],

bmds/datasets/continuous.py

@@ -175,7 +175,7 @@ def plot(self, figsize: tuple[float, float] | None = None) -> Figure:
             self.doses,
             self.means,
             yerr=self.errorbars(),
-            label="Mean ± 95% CI",
+            label="Observed Mean ± 95% CI",
             **plotting.DATASET_POINT_FORMAT,
         )
         ax.legend(**plotting.LEGEND_OPTS)

bmds/stats/anova.py

@@ -96,7 +96,7 @@ def output_3tests(tests) -> str:
 
         outputs = [
             "                     Tests of Interest    ",
-            "   Test    -2*log(Likelihood Ratio)  Test df        p-value    ",
+            "   Test    -2*log(Likelihood Ratio)  Test df        P-Value    ",
         ]
         for i, test in enumerate([tests.test1, tests.test2, tests.test3]):
             outputs.append("   Test %d %20.6g %10d %16.4g" % (i + 1, test.MSE, test.CDF, test.TEST))

tests/bmds3/types/test_continuous.py

@@ -48,8 +48,6 @@ def test_exp3(self, cdataset):
         for field in [
             "values",
             "se",
-            "lower_ci",
-            "upper_ci",
             "bounded",
             "prior_type",
             "prior_initial_value",

tests/datasets/test_continuous.py

@@ -108,7 +108,7 @@ def test_correct_variance_model(self, cdataset):
     def test_anova(self, anova_dataset, bad_anova_dataset):
         # Check that anova generates expected output from original specifications.
         report = anova_dataset.get_anova_report()
-        expected = "                     Tests of Interest    \n   Test    -2*log(Likelihood Ratio)  Test df        p-value    \n   Test 1              22.2699         12           0.0346\n   Test 2               5.5741          6           0.4725\n   Test 3               5.5741          6           0.4725"
+        expected = "                     Tests of Interest    \n   Test    -2*log(Likelihood Ratio)  Test df        P-Value    \n   Test 1              22.2699         12           0.0346\n   Test 2               5.5741          6           0.4725\n   Test 3               5.5741          6           0.4725"
         assert report == expected
 
         # check bad anova dataset