update dataset

Author: CamDavidsonPilon

CHANGELOG.md

@@ -5,6 +5,7 @@
 ##### Bug fixes
  - Fixed bug when no covariates were passed into `CoxPHFitter`. See #975
  - Fixed error in `StatisticalResult` where the test name was not displayed correctly.
+ - Fixed a keyword bug in `plot_covariate_groups` for parametric models.
 
 
 #### 0.24.1 - 2020-03-05
@@ -18,15 +19,15 @@
 
 #### 0.24.0 - 2020-02-20
 
-This version and future versions of lifelines no longer support py35. Pandas 1.0 is fully supported, along with previous version. Minimum Scipy has been bumped to 1.2.0
+This version and future versions of lifelines no longer support py35. Pandas 1.0 is fully supported, along with previous versions. Minimum Scipy has been bumped to 1.2.0.
 
 ##### New features
  - `CoxPHFitter` and `CoxTimeVaryingFitter` has support for an elastic net penalty, which includes L1 and L2 regression.
  - `CoxPHFitter` has new baseline survival estimation methods. Specifically, `spline` now estimates the coefficients and baseline survival using splines. The traditional method, `breslow`, is still the default however.
  - Regression models have a new `score` method that will score your model against a dataset (ex: a testing or validation dataset). The default is to evaluate the log-likelihood, but also the concordance index can be chose.
  - New `MixtureCureFitter` for quickly creating univariate mixture models.
  - Univariate parametric models have a `plot_density`, `density_at_times`, and property `density_` that computes the probability density function estimates.
- - new dataset for interval regression involving C. Botulinum.
+ - new dataset for interval regression involving *C. Botulinum*.
  - new `lifelines.fitters.mixins.ProportionalHazardMixin` that implements proportional hazard checks.
 
 ##### API Changes

lifelines/datasets/c_botulinum_lag_phase.csv

@@ -1,4 +1,4 @@
-NaCL %,pH,lower_bound_days,upper_bound_days
+NaCl %,pH,lower_bound_days,upper_bound_days
 0,7.0,0,1.0
 0,6.5,0,1.0
 0,6.0,0,1.0

lifelines/fitters/__init__.py

@@ -2407,7 +2407,7 @@ def plot(self, columns=None, parameter=None, ax=None, **errorbar_kwargs):
 
         return ax
 
-    def plot_covariate_groups(self, covariates, values, plot_baseline=True, ax=None, timeline=None, **kwargs):
+    def plot_covariate_groups(self, covariates, values, plot_baseline=True, ax=None, times=None, **kwargs):
         """
         Produces a plot comparing the baseline survival curve of the model versus
         what happens when a covariate(s) is varied over values in a group. This is useful to compare
@@ -2422,8 +2422,8 @@ def plot_covariate_groups(self, covariates, values, plot_baseline=True, ax=None,
             an iterable of the values we wish the covariate to take on.
         plot_baseline: bool
             also display the baseline survival, defined as the survival at the mean of the original dataset.
-        timeline:
-            pass in a timeline to plot
+        times:
+            pass in a times to plot
         kwargs:
             pass in additional plotting commands
 
@@ -2481,9 +2481,9 @@ def plot_covariate_groups(self, covariates, values, plot_baseline=True, ax=None,
         for covariate, value in zip(covariates, values.T):
             X[covariate] = value
 
-        self.predict_survival_function(X, timeline=timeline).plot(ax=ax, **kwargs)
+        self.predict_survival_function(X, times=times).plot(ax=ax, **kwargs)
         if plot_baseline:
-            self.predict_survival_function(x_bar, timeline=timeline).rename(columns={0: "baseline survival"}).plot(
+            self.predict_survival_function(x_bar, times=times).rename(columns={0: "baseline survival"}).plot(
                 ax=ax, ls=":", color="k"
             )
         return ax
@@ -3113,7 +3113,7 @@ def plot(self, columns=None, parameter=None, ax=None, **errorbar_kwargs):
 
         return ax
 
-    def plot_covariate_groups(self, covariates, values, plot_baseline=True, ax=None, timeline=None, **kwargs):
+    def plot_covariate_groups(self, covariates, values, plot_baseline=True, ax=None, times=None, **kwargs):
         """
         Produces a visual representation comparing the baseline survival curve of the model versus
         what happens when a covariate(s) is varied over values in a group. This is useful to compare
@@ -3128,8 +3128,8 @@ def plot_covariate_groups(self, covariates, values, plot_baseline=True, ax=None,
             an iterable of the values we wish the covariate to take on.
         plot_baseline: bool
             also display the baseline survival, defined as the survival at the mean of the original dataset.
-        timeline: iterable
-            pass in a timeline to plot
+        times: iterable
+            pass in a times to plot
         kwargs:
             pass in additional plotting commands
 
@@ -3194,9 +3194,9 @@ def plot_covariate_groups(self, covariates, values, plot_baseline=True, ax=None,
             X["_intercept"] = 1.0
             ancillary_X["_intercept"] = 1.0
 
-        self.predict_survival_function(X, ancillary_df=ancillary_X, timeline=timeline).plot(ax=ax, **kwargs)
+        self.predict_survival_function(X, ancillary_df=ancillary_X, times=times).plot(ax=ax, **kwargs)
         if plot_baseline:
-            self.predict_survival_function(x_bar, ancillary_df=x_bar_anc, timeline=timeline).rename(
+            self.predict_survival_function(x_bar, ancillary_df=x_bar_anc, times=times).rename(
                 columns={0: "baseline survival"}
             ).plot(ax=ax, ls=":", color="k")
         return ax