def test_plot_km():
if pdf_output:
from matplotlib.backends.backend_pdf import PdfPages
pdf = PdfPages("test_survfunc.pdf")
else:
pdf = None
sr1 = SurvfuncRight(ti1, st1)
sr2 = SurvfuncRight(ti2, st2)
fig = plot_survfunc(sr1)
close_or_save(pdf, fig)
fig = plot_survfunc(sr2)
close_or_save(pdf, fig)
fig = plot_survfunc([sr1, sr2])
close_or_save(pdf, fig)
# Plot the SAS BMT data
gb = bmt.groupby("Group")
sv = []
for g in gb:
s0 = SurvfuncRight(g[1]["T"], g[1]["Status"], title=g[0])
sv.append(s0)
fig = plot_survfunc(sv)
ax = fig.get_axes()[0]
ax.set_position([0.1, 0.1, 0.64, 0.8])
ha, lb = ax.get_legend_handles_labels()
fig.legend([ha[k] for k in (0, 2, 4)],
[lb[k] for k in (0, 2, 4)],
'center right')
close_or_save(pdf, fig)
# Simultaneous CB for BMT data
ii = bmt.Group == "ALL"
sf = SurvfuncRight(bmt.loc[ii, "T"], bmt.loc[ii, "Status"])
fig = sf.plot()
ax = fig.get_axes()[0]
ax.set_position([0.1, 0.1, 0.64, 0.8])
ha, lb = ax.get_legend_handles_labels()
lcb, ucb = sf.simultaneous_cb(transform="log")
plt.fill_between(sf.surv_times, lcb, ucb, color="lightgrey")
lcb, ucb = sf.simultaneous_cb(transform="arcsin")
plt.plot(sf.surv_times, lcb, color="darkgrey")
plt.plot(sf.surv_times, ucb, color="darkgrey")
plt.plot(sf.surv_times, sf.surv_prob - 2*sf.surv_prob_se, color="red")
plt.plot(sf.surv_times, sf.surv_prob + 2*sf.surv_prob_se, color="red")
plt.xlim(100, 600)
close_or_save(pdf, fig)
if pdf_output:
pdf.close()
evt = -mn*np.log(np.random.uniform(size=4*n)) # Event times
fut = -mf*np.log(np.random.uniform(size=4*n)) # Follow up times
y = np.where(evt < fut, evt, fut) # The time that is observed
c = (y == evt).astype(np.int) # Censoring indicator (1 if censored)
g = np.kron(np.arange(4), np.ones(n)) # Group labels
df = pd.DataFrame({"y": y, "c": c, "g": g})
# Estimate the survival functions
sf = []
for k, dx in df.groupby("g"):
s = sm.SurvfuncRight(dx.y, dx.c)
sf.append(s)
# Plot the survival function estimates.
plt.clf()
plot_survfunc(sf)
ha, lb = plt.gca().get_legend_handles_labels()
leg = plt.figlegend(ha, lb, "center right")
leg.draw_frame(False)
pdf.savefig()
# Simulate data for proportional hazards regression analysis
n = 400
xmat = np.random.normal(size=(n, 4))
lp = np.dot(xmat, np.r_[1, -1, 0, 0])
evt = - np.exp(-lp) * np.log(np.random.uniform(size=n)) # Event times
fut = -5 * np.log(np.random.uniform(size=n)) # Follow up times
y = np.where(evt < fut, evt, fut) # The time that is observed
c = (y == evt).astype(np.int) # Censoring indicator (1 if censored)
df = pd.DataFrame({"y": y, "c": c, "x0": xmat[:, 0], "x1": xmat[:, 1], "x2": xmat[:, 2], "x3": xmat[:, 3]})
