321 improve nomenclature for statistical outputs (#2)

* 8

* 25a, 26, 30 comment

* 27, 29, 33, 34, 35

* 2, 6

* 31

* testfix

* rm comment, 31

* 3 note, 2, 5a, 12, 24, rm comment

* js eol lf

* revert MT

* rename percentile to cummulative probability

* resize tables as needed

* remove change

* fix test w/ degree symbol

* rename log likelihood globally

* fix bug in layout

* rename scaled residual

* rename DOF to d.f.

* change Summary to Modeling Summary

* rename "Abs(Residual of interest)" -> "|Residual near BMD|"

* rename "Frequentist Model Results" to "Maximum Likelihood Approach Model Results"

* title case

* add vertical spacing

* Change "Confidence Level" to "Confidence Level (one-sided)" globally

* add on bound footnote to UI

* handle goodness of fit table

* use < and > instead of less than and greater than

* only show tail probability if hybrid continuous bmr

* make format

---------

Co-authored-by: Andy Shapiro <[email protected]>

frontend/src/components/IndividualModel/CDFTable.js

@@ -11,12 +11,16 @@ class CDFTable extends Component {
         const {bmd_dist} = this.props;
         return (
             <table className="table table-sm table-bordered text-right">
+                <colgroup>
+                    <col width="50%" />
+                    <col width="50%" />
+                </colgroup>
                 <thead>
                     <tr className="bg-custom text-left">
                         <th colSpan="2">CDF</th>
                     </tr>
                     <tr>
-                        <th>Percentile</th>
+                        <th>Cumulative Probability</th>
                         <th>BMD</th>
                     </tr>
                 </thead>

frontend/src/components/IndividualModel/ContinuousDeviance.js

@@ -20,11 +20,11 @@ class ContinuousDeviance extends Component {
                 </colgroup>
                 <thead>
                     <tr className="bg-custom">
-                        <th colSpan="9">Likelihoods of Interest</th>
+                        <th colSpan="9">Likelihoods</th>
                     </tr>
                     <tr>
                         <th>Model</th>
-                        <th>Log Likelihood</th>
+                        <th>-2* Log(Likelihood Ratio)</th>
                         <th># of Parameters</th>
                         <th>AIC</th>
                     </tr>

frontend/src/components/IndividualModel/ContinuousSummary.js

@@ -19,7 +19,7 @@ class ContinuousSummary extends Component {
                 </colgroup>
                 <thead>
                     <tr className="bg-custom">
-                        <th colSpan="2">Summary</th>
+                        <th colSpan="2">Modeling Summary</th>
                     </tr>
                 </thead>
                 <tbody>
@@ -40,7 +40,7 @@ class ContinuousSummary extends Component {
                         <td>{ff(results.fit.aic)}</td>
                     </tr>
                     <tr>
-                        <td>Log Likelihood</td>
+                        <td>-2* Log(Likelihood Ratio)</td>
                         <td>{ff(results.fit.loglikelihood)}</td>
                     </tr>
                     <tr>
@@ -50,7 +50,7 @@ class ContinuousSummary extends Component {
                         <td>{fractionalFormatter(p_value)}</td>
                     </tr>
                     <tr>
-                        <td>Model DOF</td>
+                        <td>Model d.f.</td>
                         <td>{ff(results.tests.dfs[3])}</td>
                     </tr>
                 </tbody>

frontend/src/components/IndividualModel/ContinuousTestOfInterest.js

@@ -22,15 +22,15 @@ class ContinuousTestOfInterest extends Component {
                 </colgroup>
                 <thead>
                     <tr className="bg-custom text-left">
-                        <th colSpan="4">Test of Interest</th>
+                        <th colSpan="4">Tests of Mean and Variance Fits</th>
                     </tr>
                     <tr>
                         <th>Test</th>
                         <th>
                             LLR
                             <HelpTextPopover title="LLR" content="2 * Log(Likelihood Ratio)" />
                         </th>
-                        <th>Test DOF</th>
+                        <th>Test d.f.</th>
                         <th>
                             <i>P</i>-Value
                         </th>

frontend/src/components/IndividualModel/DichotomousDeviance.js

@@ -26,10 +26,10 @@ class DichotomousDeviance extends Component {
                     </tr>
                     <tr>
                         <th>Model</th>
-                        <th>Log Likelihood</th>
+                        <th>-2* Log(Likelihood Ratio)</th>
                         <th># Parameters</th>
                         <th>Deviance</th>
-                        <th>Test DOF</th>
+                        <th>Test d.f.</th>
                         <th>
                             <i>P</i>-Value
                         </th>

frontend/src/components/IndividualModel/DichotomousSummary.js

@@ -18,7 +18,7 @@ class DichotomousSummary extends Component {
                 </colgroup>
                 <thead>
                     <tr className="bg-custom">
-                        <th colSpan="2">Summary</th>
+                        <th colSpan="2">Modeling Summary</th>
                     </tr>
                 </thead>
                 <tbody>
@@ -39,7 +39,7 @@ class DichotomousSummary extends Component {
                         <td>{ff(results.fit.aic)}</td>
                     </tr>
                     <tr>
-                        <td>Log Likelihood</td>
+                        <td>-2* Log(Likelihood Ratio)</td>
                         <td>{ff(results.fit.loglikelihood)}</td>
                     </tr>
                     <tr>
@@ -49,7 +49,7 @@ class DichotomousSummary extends Component {
                         <td>{fourDecimalFormatter(results.gof.p_value)}</td>
                     </tr>
                     <tr>
-                        <td>Overall DOF</td>
+                        <td>Overall d.f.</td>
                         <td>{ff(results.gof.df)}</td>
                     </tr>
                     <tr>

frontend/src/components/IndividualModel/GoodnessFit.js

@@ -6,87 +6,141 @@ import {Dtype} from "@/constants/dataConstants";
 import {isLognormal} from "@/constants/modelConstants";
 import {ff} from "@/utils/formatters";
 
-/* eslint-disable */
-const hdr_c_normal = [
-        "Dose", "Size", "Observed Mean", "Calculated Mean", "Estimated Mean",
-        "Observed SD", "Calculated SD", "Estimated SD", "Scaled Residual",
-    ],
-    hdr_c_lognormal = [
-        "Dose", "Size", "Observed Mean", "Calculated Median", "Estimated Median",
-        "Observed SD", "Calculated GSD", "Estimated GSD", "Scaled Residual",
-    ],
-    hdr_d = [ "Dose", "Size", "Observed", "Expected", "Estimated Probability", "Scaled Residual"];
-/* eslint-enable */
-
 @observer
 class GoodnessFit extends Component {
-    getHeaders(dtype, settings) {
-        if (dtype == Dtype.CONTINUOUS || dtype == Dtype.CONTINUOUS_INDIVIDUAL) {
-            const headers = isLognormal(settings.disttype) ? hdr_c_lognormal : hdr_c_normal;
-            return [headers, [10, 10, 10, 12, 12, 12, 10, 12, 12]];
-        }
-        if (dtype == Dtype.DICHOTOMOUS) {
-            return [hdr_d, [17, 16, 16, 17, 17, 17]];
-        }
-        throw Error("Unknown dtype");
+    getDichotomousData() {
+        const {store} = this.props,
+            gof = store.modalModel.results.gof,
+            dataset = store.selectedDataset;
+        return {
+            headers: [
+                "Dose",
+                "N",
+                "Observed",
+                "Expected",
+                "Estimated Probability",
+                "Scaled Residual",
+            ],
+            colwidths: [17, 16, 16, 17, 17, 17],
+            data: dataset.doses.map((dose, i) => {
+                return [
+                    dose,
+                    dataset.ns[i],
+                    dataset.incidences[i],
+                    ff(gof.expected[i]),
+                    ff(gof.expected[i] / dataset.ns[i]),
+                    ff(gof.residual[i]),
+                ];
+            }),
+        };
+    }
+
+    getContinuousNormalData(dtype) {
+        const {store} = this.props,
+            gof = store.modalModel.results.gof,
+            dataset = store.selectedDataset,
+            useFF = dtype === Dtype.CONTINUOUS_INDIVIDUAL;
+        return {
+            headers: [
+                "Dose",
+                "N",
+                "Sample Mean",
+                "Model Fitted Mean",
+                "Sample SD",
+                "Model Fitted SD",
+                "Scaled Residual",
+            ],
+            colwidths: [1, 1, 1, 1, 1, 1, 1],
+            data: dataset.doses.map((dose, i) => {
+                return [
+                    dose,
+                    dataset.ns[i],
+                    useFF ? ff(gof.obs_mean[i]) : gof.obs_mean[i],
+                    ff(gof.est_mean[i]),
+                    useFF ? ff(gof.obs_sd[i]) : gof.obs_sd[i],
+                    ff(gof.est_sd[i]),
+                    ff(gof.residual[i]),
+                ];
+            }),
+        };
+    }
+
+    getContinuousLognormalData(dtype) {
+        const {store} = this.props,
+            gof = store.modalModel.results.gof,
+            dataset = store.selectedDataset,
+            useFF = dtype === Dtype.CONTINUOUS_INDIVIDUAL;
+        return {
+            headers: [
+                "Dose",
+                "N",
+                "Sample Mean",
+                "Approximate Sample Median",
+                "Model Fitted Median",
+                "Sample SD",
+                "Approximate Sample GSD",
+                "Model Fitted GSD",
+                "Scaled Residual",
+            ],
+            colwidths: [10, 10, 10, 12, 12, 12, 10, 12, 12],
+            data: dataset.doses.map((dose, i) => {
+                return [
+                    dose,
+                    dataset.ns[i],
+                    useFF ? ff(gof.obs_mean[i]) : gof.obs_mean[i],
+                    ff(gof.calc_mean[i]),
+                    ff(gof.est_mean[i]),
+                    useFF ? ff(gof.obs_sd[i]) : gof.obs_sd[i],
+                    ff(gof.calc_mean[i]),
+                    ff(gof.est_sd[i]),
+                    ff(gof.residual[i]),
+                ];
+            }),
+        };
     }
     render() {
         const {store} = this.props,
             settings = store.modalModel.settings,
-            gof = store.modalModel.results.gof,
             dataset = store.selectedDataset,
-            {dtype} = dataset,
-            headers = this.getHeaders(dtype, settings);
+            {dtype} = dataset;
+
+        let data;
+        if (dtype == Dtype.DICHOTOMOUS) {
+            data = this.getDichotomousData();
+        } else {
+            if (isLognormal(settings.disttype)) {
+                data = this.getContinuousLognormalData(dtype);
+            } else {
+                data = this.getContinuousNormalData(dtype);
+            }
+        }
         return (
             <table className="table table-sm table-bordered text-right">
                 <colgroup>
-                    {headers[1].map((d, i) => (
+                    {data.colwidths.map((d, i) => (
                         <col key={i} width={`${d}%`} />
                     ))}
                 </colgroup>
                 <thead>
                     <tr className="bg-custom text-left">
-                        <th colSpan={headers[0].length}>Goodness of Fit</th>
+                        <th colSpan={data.headers.length}>Goodness of Fit</th>
                     </tr>
                     <tr>
-                        {headers[0].map((d, i) => (
+                        {data.headers.map((d, i) => (
                             <th key={i}>{d}</th>
                         ))}
                     </tr>
                 </thead>
                 <tbody>
-                    {dtype == Dtype.CONTINUOUS || dtype == Dtype.CONTINUOUS_INDIVIDUAL
-                        ? gof.dose.map((item, i) => {
-                              const useFF = dtype === Dtype.CONTINUOUS_INDIVIDUAL;
-                              return (
-                                  <tr key={i}>
-                                      <td>{item}</td>
-                                      <td>{gof.size[i]}</td>
-                                      <td>{useFF ? ff(gof.obs_mean[i]) : gof.obs_mean[i]}</td>
-                                      <td>{ff(gof.calc_mean[i])}</td>
-                                      <td>{ff(gof.est_mean[i])}</td>
-                                      <td>{useFF ? ff(gof.obs_sd[i]) : gof.obs_sd[i]}</td>
-                                      <td>{ff(gof.calc_sd[i])}</td>
-                                      <td>{ff(gof.est_sd[i])}</td>
-                                      <td>{ff(gof.residual[i])}</td>
-                                  </tr>
-                              );
-                          })
-                        : null}
-                    {dtype == Dtype.DICHOTOMOUS
-                        ? dataset.doses.map((dose, i) => {
-                              return (
-                                  <tr key={i}>
-                                      <td>{dose}</td>
-                                      <td>{dataset.ns[i]}</td>
-                                      <td>{dataset.incidences[i]}</td>
-                                      <td>{ff(gof.expected[i])}</td>
-                                      <td>{ff(gof.expected[i] / dataset.ns[i])}</td>
-                                      <td>{ff(gof.residual[i])}</td>
-                                  </tr>
-                              );
-                          })
-                        : null}
+                    {data.data.map((row, i) => {
+                        return (
+                            <tr key={i}>
+                                {row.map((cell, j) => (
+                                    <td key={j}>{cell}</td>
+                                ))}
+                            </tr>
+                        );
+                    })}
                 </tbody>
             </table>
         );

frontend/src/components/IndividualModel/ModelDetailModal.js

@@ -69,7 +69,7 @@ class ModelBody extends Component {
                     </Col>
                 </Row>
                 <Row>
-                    <Col xl={isDichotomous ? 8 : 10}>
+                    <Col xl={isDichotomous ? 8 : 12}>
                         <GoodnessFit store={outputStore} />
                     </Col>
                 </Row>
@@ -82,7 +82,7 @@ class ModelBody extends Component {
                 ) : null}
                 {isContinuous ? (
                     <Row>
-                        <Col xl={6}>
+                        <Col xl={8}>
                             <ContinuousDeviance store={outputStore} />
                             <ContinuousTestOfInterest store={outputStore} />
                         </Col>

frontend/src/components/IndividualModel/ModelOptionsTable.js

@@ -6,6 +6,7 @@ import {getLabel} from "@/common";
 import TwoColumnTable from "@/components/common/TwoColumnTable";
 import {Dtype} from "@/constants/dataConstants";
 import {hasDegrees} from "@/constants/modelConstants";
+import {isHybridBmr} from "@/constants/optionsConstants";
 import {
     continuousBmrOptions,
     dichotomousBmrOptions,
@@ -39,7 +40,7 @@ class ModelOptionsTable extends Component {
             data = [
                 ["BMR Type", getLabel(model.settings.bmr_type, dichotomousBmrOptions)],
                 ["BMR", ff(model.settings.bmr)],
-                ["Confidence Level", ff(1 - model.settings.alpha)],
+                ["Confidence Level (one sided)", ff(1 - model.settings.alpha)],
                 hasDegrees.has(model.model_class.verbose)
                     ? ["Degree", ff(model.settings.degree)]
                     : null,
@@ -53,8 +54,10 @@ class ModelOptionsTable extends Component {
                 ["BMRF", ff(model.settings.bmr)],
                 ["Distribution Type", getLabel(model.settings.disttype, distTypeOptions)],
                 ["Direction", model.settings.is_increasing ? "Up" : "Down"],
-                ["Confidence Level", 1 - ff(model.settings.alpha)],
-                ["Tail Probability", ff(model.settings.tail_prob)],
+                ["Confidence Level (one sided)", 1 - ff(model.settings.alpha)],
+                isHybridBmr(model.settings.bmr_type)
+                    ? ["Tail Probability", ff(model.settings.tail_prob)]
+                    : null,
                 hasDegrees.has(model.model_class.verbose)
                     ? ["Degree", ff(model.settings.degree)]
                     : null,
@@ -66,7 +69,7 @@ class ModelOptionsTable extends Component {
             data = [
                 ["BMR Type", getLabel(model.settings.bmr_type, dichotomousBmrOptions)],
                 ["BMR", ff(model.settings.bmr)],
-                ["Confidence Level", ff(1 - model.settings.alpha)],
+                ["Confidence Level (one sided)", ff(1 - model.settings.alpha)],
                 ["Bootstrap Seed", model.settings.bootstrap_seed],
                 ["Bootstrap Iterations", model.settings.bootstrap_iterations],
                 [