Partial residual plots with Pearson correlation coefficient and P-value

Hello Matplotlib/Python users

I’m attempting to plot the residual regression plots with Pearson correlation coefficient and P-value in Python.

I’ve read an article regarding the simple correlation and linear regression:

What would be the best way to do it in Python?

Y=Continuous var
X=Continuous var
Nuisance covariate=Z (Continuous var)

Thanks for considering my request.

Matplotlib version

Operating system: Ubutnu 16.0.4
Matplotlib version: 3.1.1
Python version: 3.7/Anaconda 2019.10 for Linux Installer

Best regard
Larry Lai

What have you tried and what isn’t working? the blog post you linked recommends this:

fig = plt.figure(figsize=(10,7))
sns.residplot(df_females.Height, df_females.Weight, color='magenta')
# Title and labels.
plt.title('Residual plot 500 females', size=24)
plt.xlabel('Height (inches)', size=18)
plt.ylabel('Weight (pounds)', size=18)

Another option might be YellowBricks Residual Plots, but more information on what you’re trying to do would be helpful.

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Thanks for pointing that out.

I would like to insert Pearson correlation coefficient and P-value to the residual regression plot.

  1. Running partial correlation
  2. Running partial regression and insert the results from step 1.

I’m sorry, but I’m still a bit unclear on what you’re asking for. Do you have some code you can post?

If you’re trying to plot a line through the residuals, then seaborn’s residplot takes a lowess keyword argument that might do the trick:

lowess: boolean, optional
    Fit a lowess smoother to the residual scatterplot.

Thanks so much. This is exactly what I’m looking for.

{x, y}_partial : matrix or string(s) , optional

Matrix with same first dimension as x, or column name(s) in data. These variables are treated as confounding and are removed from the x or y variables before plotting.

In addition, adding r and P-value to the plot.

Cdoe Examples (adapted from:

Partial correlation with one covariate

import pingouin as pg
df = pg.read_dataset(‘partial_corr’)
pg.partial_corr(data=df, x=‘x’, y=‘y’, covar=‘cv1’)
n r CI95% r2 adj_r2 p-val BF10 power
pearson 30 0.568 [0.26, 0.77] 0.323 0.273 0.001055 37.773 0.925

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