def graphics_check():
# L'Abbe Plots
labbe_plot()
plt.show()
labbe_plot(r1=[0.25, 0.5], r0=[0.1, 0.2], color='r')
plt.show()
labbe_plot(r1=[0.3, 0.5], r0=[0.2, 0.7], scale='additive', marker='+', linestyle='')
plt.show()
labbe_plot(r1=[0.3, 0.5], r0=[0.2, 0.7], scale='multiplicative', markersize=10)
plt.show()
# 1) Check EffectMeasurePlot
labs = ['Overall', 'Adjusted', '', '2012-2013', 'Adjusted', '', '2013-2014', 'Adjusted', '', '2014-2015',
'Adjusted']
measure = [np.nan, 0.94, np.nan, np.nan, 1.22, np.nan, np.nan, 0.59, np.nan, np.nan, 1.09]
lower = [np.nan, 0.77, np.nan, np.nan, '0.80', np.nan, np.nan, '0.40', np.nan, np.nan, 0.83]
upper = [np.nan, 1.15, np.nan, np.nan, 1.84, np.nan, np.nan, 0.85, np.nan, np.nan, 1.44]
p = EffectMeasurePlot(label=labs, effect_measure=measure, lcl=lower, ucl=upper)
p.plot(figsize=[7, 4])
plt.show()
# 2) Check Functional form plots
data = load_sample_data(False)
data['age_sq'] = data['age0']**2
functional_form_plot(data, 'dead', var='age0', loess=False)
plt.show()
functional_form_plot(data, 'dead', var='age0', discrete=True, loess=False)
plt.show()
functional_form_plot(data, 'cd40', var='age0', outcome_type='continuous', loess=False)
plt.show()
functional_form_plot(data, 'dead', var='age0', points=True, loess=False)
plt.show()
functional_form_plot(data, 'dead', var='age0', loess=True, loess_value=0.25, discrete=True)
plt.show()
functional_form_plot(data, 'dead', var='age0', f_form='age0 + age_sq', loess=False)
plt.show()
# 3) Check P-value plots
p = pvalue_plot(point=0.23, sd=0.1)
plt.show()
pvalue_plot(point=0.23, sd=0.1, fill=False)
plt.show()
pvalue_plot(point=0.23, sd=0.1, color='r')
plt.show()
pvalue_plot(point=0.23, sd=0.1, null=0.05)
plt.show()
pvalue_plot(point=0.23, sd=0.1, alpha=0.05)
plt.show()
# 4) Check Spaghetti plot
df = load_sample_data(timevary=True)
spaghetti_plot(df, idvar='id', variable='cd4', time='enter')
plt.show()
# 5) Check ROC
df = pd.DataFrame()
df['d'] = [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
df['p'] = [0.1, 0.15, 0.1, 0.7, 0.5, 0.9, 0.95, 0.5, 0.4, 0.8, 0.99, 0.99, 0.89, 0.95]
roc(df, true='d', threshold='p', youden_index=False)
plt.show()
roc(df, true='d', threshold='p', youden_index=True)
plt.show()
# 6) Check Dynamic Risk Plots
a = pd.DataFrame([[0, 0], [1, 0.15], [2, 0.25], [4, 0.345]], columns=['timeline', 'riske']).set_index(
'timeline')
b = pd.DataFrame([[0, 0], [1, 0.2], [1.5, 0.31], [3, 0.345]], columns=['timeline', 'riske']).set_index(
'timeline')
dynamic_risk_plot(a, b, loess=False)
plt.show()
dynamic_risk_plot(a, b, measure='RR', loess=False)
plt.show()
dynamic_risk_plot(a, b, measure='RR', scale='log-transform', loess=False)
plt.show()
dynamic_risk_plot(a, b, measure='RR', scale='log', loess=False)
plt.show()
dynamic_risk_plot(a, b, loess=True, loess_value=0.4)
plt.show()
dynamic_risk_plot(a, b, loess=False, point_color='green', line_color='green')
plt.show()
pvalue_plot(point=-0.049, sd=0.042, color='b', fill=False)
pvalue_plot(point=-0.062, sd=0.0231, color='r', fill=False)
plt.legend(
[Line2D([0], [0], color='b', lw=2),
Line2D([0], [0], color='r', lw=2)], ['Our Study', 'Review'])
plt.tight_layout()
plt.savefig("../images/zepid_pvalue3.png", format='png', dpi=300)
plt.close()
######################################
# Spaghetti Plot
from zepid.graphics import spaghetti_plot
df = ze.load_sample_data(timevary=True)
spaghetti_plot(df, idvar='id', variable='cd4', time='enter')
plt.tight_layout()
plt.savefig("../images/zepid_spaghetti.png", format='png', dpi=300)
plt.close()
######################################
# Effect Measure plot
import numpy as np
from zepid.graphics import EffectMeasurePlot
labs = [
'Overall', 'Adjusted', '', '2012-2013', 'Adjusted', '', '2013-2014',
'Adjusted', '', '2014-2015', 'Adjusted'
]
measure = [
np.nan, 0.94, np.nan, np.nan, 1.22, np.nan, np.nan, 0.59, np.nan, np.nan,