{selectedFrequentist ? (
`|Residual for Dose Group Near BMD| > ${val}`,
+ name: "|Residual near BMD| too large",
+ notes: val => `|Residual Near BMD| > ${val}`,
hasThreshold: true,
enabledContinuous: true,
enabledDichotomous: true,
enabledNested: true,
},
[RULES.WARNINGS]: {
- name: "BMDS model Warning",
+ name: "BMDS model warning",
notes: val => "BMD output file included warning",
hasThreshold: false,
enabledContinuous: false,
@@ -200,7 +200,7 @@ export const RULES = Object.freeze({
enabledNested: true,
},
[RULES.CONTROL_RESIDUAL_HIGH]: {
- name: "Abs(Residual at control) too large",
+ name: "|Residual at control| too large",
notes: val => `|Residual at control| > ${val}`,
hasThreshold: true,
enabledContinuous: true,
diff --git a/frontend/src/constants/modelConstants.js b/frontend/src/constants/modelConstants.js
index 35dfcc18..4fda10c1 100644
--- a/frontend/src/constants/modelConstants.js
+++ b/frontend/src/constants/modelConstants.js
@@ -78,7 +78,7 @@ const modelsList = {
hasDegrees = new Set(["Multistage", "Polynomial"]),
getNameFromDegrees = function(model) {
const degree = model.parameters.names.length - 1;
- return `Multistage ${degree}°`;
+ return `Multistage ${degree}`;
};
export {allModelOptions, getNameFromDegrees, hasDegrees, isLognormal, models, modelsList};
diff --git a/frontend/src/constants/optionsConstants.js b/frontend/src/constants/optionsConstants.js
index e79cf094..c471f673 100644
--- a/frontend/src/constants/optionsConstants.js
+++ b/frontend/src/constants/optionsConstants.js
@@ -44,6 +44,9 @@ export const options = {
{value: 6, label: "Hybrid-Extra Risk"},
{value: 7, label: "Hybrid-Added Risk"},
],
+ isHybridBmr = function(val) {
+ return val === 6 || val === 7;
+ },
distTypeOptions = [
{value: 1, label: "Normal + Constant"},
{value: 2, label: "Normal + Non-constant"},
diff --git a/frontend/src/constants/plotting.js b/frontend/src/constants/plotting.js
index 2135a8ff..014695e0 100644
--- a/frontend/src/constants/plotting.js
+++ b/frontend/src/constants/plotting.js
@@ -112,9 +112,9 @@ export const getResponse = dataset => {
},
getCdfLayout = function(dataset) {
let layout = _.cloneDeep(doseResponseLayout);
- layout.title.text = "BMD Cumulative distribution function";
+ layout.title.text = "BMD Cumulative Distribution Function";
layout.xaxis.title.text = getDoseLabel(dataset);
- layout.yaxis.title.text = "Percentile";
+ layout.yaxis.title.text = "Cumulative Probability";
layout.yaxis.range = [0, 1];
return layout;
},
diff --git a/tests/analysis/test_executor.py b/tests/analysis/test_executor.py
index fec5669b..8fbf25cf 100644
--- a/tests/analysis/test_executor.py
+++ b/tests/analysis/test_executor.py
@@ -169,17 +169,17 @@ def test_disttype(self, bmds3_complete_continuous):
session = AnalysisSession.create(data, 0, 0)
assert len(session.frequentist.models) == 5
names = [model.name() for model in session.frequentist.models]
- assert names == ["ExponentialM3", "ExponentialM5", "Hill", "Linear", "Power"]
+ assert names == ["Exponential 3", "Exponential 5", "Hill", "Linear", "Power"]
data["options"][0]["dist_type"] = 2
session = AnalysisSession.create(data, 0, 0)
assert len(session.frequentist.models) == 5
names = [model.name() for model in session.frequentist.models]
- assert names == ["ExponentialM3", "ExponentialM5", "Hill", "Linear", "Power"]
+ assert names == ["Exponential 3", "Exponential 5", "Hill", "Linear", "Power"]
# lognormal
data["options"][0]["dist_type"] = 3
session = AnalysisSession.create(data, 0, 0)
assert len(session.frequentist.models) == 2
names = [model.name() for model in session.frequentist.models]
- assert names == ["ExponentialM3", "ExponentialM5"]
+ assert names == ["Exponential 3", "Exponential 5"]
diff --git a/tests/analysis/test_validators.py b/tests/analysis/test_validators.py
index 425e9aca..879b7573 100644
--- a/tests/analysis/test_validators.py
+++ b/tests/analysis/test_validators.py
@@ -350,7 +350,7 @@ def test_dichotomous(self, bmds3_complete_dichotomous):
# check incidence > n
check = deepcopy(dataset)
check["incidences"][0] = check["ns"][0] + 1
- with pytest.raises(PydanticValidationError, match="Incidence cannot be greater than N"):
+ with pytest.raises(PydanticValidationError, match="Incidence > N"):
datasets.MaxDichotomousDatasetSchema(**check)
# check minimums
diff --git a/tests/data/db.yaml b/tests/data/db.yaml
index e5f6cc52..4d7e79b3 100644
--- a/tests/data/db.yaml
+++ b/tests/data/db.yaml
@@ -1612,10 +1612,10 @@
- 1
model_notes:
- '0':
- - Control stdev. fit greater than threshold (2.312 > 1.5)
+ - Control stdev. fit > threshold (2.312 > 1.5)
'1':
- - Goodness of fit p-value less than threshold (0 < 0.1)
- - Abs(Residual of interest) greater than threshold (2.246 > 2.0)
+ - Goodness of fit p-value < threshold (0 < 0.1)
+ - '|Residual near BMD| > threshold (2.246 > 2.0)'
- Variance test failed (Test 2 p-value 2 < 0.05)
- Incorrect variance model (p-value 2 = 0), constant variance selected
'2': []
@@ -7067,8 +7067,8 @@
2.0, "enabled_dichotomous": true, "enabled_continuous": false, "enabled_nested":
true}]}, "results": {"recommended_model_index": null, "recommended_model_variable":
null, "model_bin": [1], "model_notes": [{"0": ["Control stdev. fit greater than
- threshold (2.312 > 1.5)"], "1": ["Goodness of fit p-value less than threshold
- (0 < 0.1)", "Abs(Residual of interest) greater than threshold (2.246 > 2.0)",
+ threshold (2.312 > 1.5)"], "1": ["Goodness of fit p-value < threshold
+ (0 < 0.1)", "|Residual near BMD| > threshold (2.246 > 2.0)",
"Constant variance test failed (p-value 2 < 0.05)", "Incorrect variance model
(p-value 2 = 0), constant variance selected"], "2": []}]}}, "selected": {"model_index":
null, "notes": ""}}, "bayesian": null}]}, "errors": [], "created": "2021-11-15T18:42:28.857Z",
@@ -7342,8 +7342,8 @@
2.0, "enabled_dichotomous": true, "enabled_continuous": false, "enabled_nested":
true}]}, "results": {"recommended_model_index": null, "recommended_model_variable":
null, "model_bin": [1], "model_notes": [{"0": ["Control stdev. fit greater than
- threshold (2.312 > 1.5)"], "1": ["Goodness of fit p-value less than threshold
- (0 < 0.1)", "Abs(Residual of interest) greater than threshold (2.246 > 2.0)",
+ threshold (2.312 > 1.5)"], "1": ["Goodness of fit p-value < threshold
+ (0 < 0.1)", "|Residual near BMD| > threshold (2.246 > 2.0)",
"Constant variance test failed (p-value 2 < 0.05)", "Incorrect variance model
(p-value 2 = 0), constant variance selected"], "2": []}]}}, "selected": {"model_index":
null, "notes": "No best fitting model selected"}}, "bayesian": null}], "analysis_id":
diff --git a/tests/integration/test_integration.py b/tests/integration/test_integration.py
index e435ce92..a05acc87 100644
--- a/tests/integration/test_integration.py
+++ b/tests/integration/test_integration.py
@@ -250,8 +250,8 @@ def test_multi_tumor(self):
page.locator("#close-modal").click()
# check one result (individual)
- page.get_by_role("link", name="Multistage 1°*").click()
- expect(page.get_by_role("dialog")).to_contain_text("Multistage 1°")
+ page.get_by_role("link", name="Multistage 1*").click()
+ expect(page.get_by_role("dialog")).to_contain_text("Multistage 1")
page.locator("#close-modal").click()
# Read-only