marginaleffects 0.0.4

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marginaleffects for Python

The marginaleffects package allows Python users to compute and plot three principal quantities of interest: (1) predictions, (2) comparisons, and (3) slopes. In addition, the package includes a convenience function to compute a fourth estimand, “marginal means”, which is a special case of averaged predictions. marginaleffects can also average (or “marginalize”) unit-level (or “conditional”) estimates of all those quantities, and conduct hypothesis tests on them.

WARNING

This is an alpha version of the package, released to gather feedback, feature requests, and bug reports from potential users. This version includes known bugs. There are also known discrepancies between the numerical results produced in Python and R. Please report any issues you encounter here: https://github.com/vincentarelbundock/pymarginaleffects/issues

Supported models

There is a good chance that this package will work with (nearly) all the models supported by the statsmodels formula API, ex: ols, probit, logit, mnlogit, quantreg, poisson, negativebinomial, mixedlm, rlm, etc. However, the package has only been tested with a subset of those, and some weirdness remains. Again: this is alpha software; it should not be used in critical applications yet.

Installation

Install the latest PyPi release:

pip install marginaleffects

Estimands: Predictions, Comparisons, and Slopes

Definitions

Predictions:

The outcome predicted by a fitted model on a specified scale for a given combination of values of the predictor variables, such as their observed values, their means, or factor levels. a.k.a. Fitted values, adjusted predictions. predictions(), avg_predictions(), plot_predictions().

Comparisons:

Compare the predictions made by a model for different regressor values (e.g., college graduates vs. others): contrasts, differences, risk ratios, odds, etc. comparisons(), avg_comparisons(), plot_comparisons().

Slopes:

Partial derivative of the regression equation with respect to a regressor of interest. a.k.a. Marginal effects, trends. slopes(), avg_slopes(), plot_slopes().

Hypothesis and Equivalence Tests:

Hypothesis and equivalence tests can be conducted on linear or non-linear functions of model coefficients, or on any of the quantities computed by the marginaleffects packages (predictions, slopes, comparisons, marginal means, etc.). Uncertainy estimates can be obtained via the delta method (with or without robust standard errors), bootstrap, or simulation.

Predictions, comparisons, and slopes are fundamentally unit-level (or “conditional”) quantities. Except in the simplest linear case, estimates will typically vary based on the values of all the regressors in a model. Each of the observations in a dataset is thus associated with its own prediction, comparison, and slope estimates. Below, we will see that it can be useful to marginalize (or “average over”) unit-level estimates to report an “average prediction”, “average comparison”, or “average slope”.

We now apply marginaleffects functions to compute each of the estimands described above. First, we fit a linear regression model with multiplicative interactions:

Predictions

import numpy as np
import polars as pl
from marginaleffects import *
import statsmodels.formula.api as smf
mtcars = pl.read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/datasets/mtcars.csv")
mod = smf.ols("mpg ~ hp * wt * am", data = mtcars).fit()

print(mod.summary().as_text())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                    mpg   R-squared:                       0.896
Model:                            OLS   Adj. R-squared:                  0.866
Method:                 Least Squares   F-statistic:                     29.55
Date:                Mon, 24 Jul 2023   Prob (F-statistic):           2.60e-10
Time:                        18:11:21   Log-Likelihood:                -66.158
No. Observations:                  32   AIC:                             148.3
Df Residuals:                      24   BIC:                             160.0
Df Model:                           7                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept     40.3272     13.008      3.100      0.005      13.480      67.175
hp            -0.0888      0.065     -1.372      0.183      -0.222       0.045
wt            -4.7968      4.002     -1.199      0.242     -13.056       3.462
hp:wt          0.0145      0.019      0.755      0.458      -0.025       0.054
am            12.8371     14.222      0.903      0.376     -16.517      42.191
hp:am         -0.0326      0.089     -0.366      0.717      -0.216       0.151
wt:am         -5.3620      4.597     -1.166      0.255     -14.851       4.127
hp:wt:am       0.0178      0.026      0.680      0.503      -0.036       0.072
==============================================================================
Omnibus:                        1.875   Durbin-Watson:                   2.205
Prob(Omnibus):                  0.392   Jarque-Bera (JB):                1.588
Skew:                           0.528   Prob(JB):                        0.452
Kurtosis:                       2.721   Cond. No.                     3.32e+04
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 3.32e+04. This might indicate that there are
strong multicollinearity or other numerical problems.

Then, we call the predictions() function. As noted above, predictions are unit-level estimates, so there is one specific prediction per observation. By default, the predictions() function makes one prediction per observation in the dataset that was used to fit the original model. Since mtcars has 32 rows, the predictions() outcome also has 32 rows:

pre = predictions(mod)

pre.shape

print(pre.head())

| Estimate | Std.Error | z    | P(>|z|) | S    | [    | ]    |
|----------|-----------|------|---------|------|------|------|
| 22.5     | 0.884     | 25.4 | 0       | inf  | 20.7 | 24.3 |
| 20.8     | 1.19      | 17.4 | 4e-15   | 47.8 | 18.3 | 23.3 |
| 25.3     | 0.709     | 35.7 | 0       | inf  | 23.8 | 26.7 |
| 20.3     | 0.704     | 28.8 | 0       | inf  | 18.8 | 21.7 |
| 17       | 0.712     | 23.9 | 0       | inf  | 15.5 | 18.5 |

Columns: rowid, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, , mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb

Comparisons: Differences, Ratios, Log-Odds, Lift, etc.

Now, we use the comparisons() function to compute the difference in predicted outcome when each of the predictors is incremented by 1 unit (one predictor at a time, holding all others constant). Once again, comparisons are unit-level quantities. And since there are 3 predictors in the model and our data has 32 rows, we obtain 96 comparisons:

cmp = comparisons(mod)

cmp.shape

print(cmp.head())

| Term | Contrast | Estimate | Std.Error | … | P(>|z|) | S    | [       | ]       |
|------|----------|----------|-----------|---|---------|------|---------|---------|
| hp   | +1       | -0.0369  | 0.0185    | … | 0.0575  | 4.12 | -0.0751 | 0.00128 |
| hp   | +1       | -0.0287  | 0.0156    | … | 0.0788  | 3.67 | -0.0609 | 0.00357 |
| hp   | +1       | -0.0466  | 0.0226    | … | 0.0502  | 4.32 | -0.0932 | 4.6e-05 |
| hp   | +1       | -0.0423  | 0.0133    | … | 0.00401 | 7.96 | -0.0697 | -0.0149 |
| hp   | +1       | -0.039   | 0.0134    | … | 0.00769 | 7.02 | -0.0667 | -0.0113 |

Columns: rowid, term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, predicted, predicted_lo, predicted_hi, , mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb

The comparisons() function allows customized queries. For example, what happens to the predicted outcome when the hp variable increases from 100 to 120?

cmp = comparisons(mod, variables = {"hp": [120, 100]})
print(cmp)

| Term | Contrast  | Estimate | Std.Error | … | P(>|z|) | S     | 2.5%      | 97.5% |
|------|-----------|----------|-----------|---|---------|-------|-----------|-------|
| hp   | 100 - 120 | 0.738    | 0.37      | … | 0.0576  | 4.12  | -0.0256   | 1.5   |
| hp   | 100 - 120 | 0.574    | 0.313     | … | 0.0788  | 3.67  | -0.0713   | 1.22  |
| hp   | 100 - 120 | 0.931    | 0.452     | … | 0.0502  | 4.32  | -0.000918 | 1.86  |
| hp   | 100 - 120 | 0.845    | 0.266     | … | 0.00401 | 7.96  | 0.297     | 1.39  |
| …    | …         | …        | …         | … | …       | …     | …         | …     |
| hp   | 100 - 120 | 0.384    | 0.27      | … | 0.168   | 2.57  | -0.173    | 0.941 |
| hp   | 100 - 120 | 0.641    | 0.334     | … | 0.0671  | 3.9   | -0.0488   | 1.33  |
| hp   | 100 - 120 | 0.126    | 0.272     | … | 0.648   | 0.626 | -0.436    | 0.688 |
| hp   | 100 - 120 | 0.635    | 0.332     | … | 0.068   | 3.88  | -0.0507   | 1.32  |

Columns: rowid, term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, predicted, predicted_lo, predicted_hi, , mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb

What happens to the predicted outcome when the wt variable increases by 1 standard deviation about its mean?

cmp = comparisons(mod, variables = {"hp": "sd"})
print(cmp)

| Term | Contrast           | Estimate | Std.Error | … | P(>|z|) | S     | 2.5%  | 97.5%   |
|------|--------------------|----------|-----------|---|---------|-------|-------|---------|
| hp   | +68.56286848932059 | -2.53    | 1.27      | … | 0.0576  | 4.12  | -5.15 | 0.0878  |
| hp   | +68.56286848932059 | -1.97    | 1.07      | … | 0.0788  | 3.67  | -4.18 | 0.245   |
| hp   | +68.56286848932059 | -3.19    | 1.55      | … | 0.0502  | 4.32  | -6.39 | 0.00315 |
| hp   | +68.56286848932059 | -2.9     | 0.911     | … | 0.00401 | 7.96  | -4.78 | -1.02   |
| …    | …                  | …        | …         | … | …       | …     | …     | …       |
| hp   | +68.56286848932059 | -1.32    | 0.925     | … | 0.168   | 2.57  | -3.22 | 0.594   |
| hp   | +68.56286848932059 | -2.2     | 1.15      | … | 0.0671  | 3.9   | -4.57 | 0.167   |
| hp   | +68.56286848932059 | -0.432   | 0.933     | … | 0.648   | 0.626 | -2.36 | 1.49    |
| hp   | +68.56286848932059 | -2.18    | 1.14      | … | 0.068   | 3.88  | -4.53 | 0.174   |

Columns: rowid, term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, predicted, predicted_lo, predicted_hi, , mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb

The comparisons() function also allows users to specify arbitrary functions of predictions, with the comparison argument. For example, what is the average ratio between predicted Miles per Gallon after an increase of 50 units in Horsepower?

cmp = comparisons(
  mod,
  variables = {"hp": 50},
  comparison = "ratioavg")
print(cmp)

| Term | Contrast | Estimate | Std.Error | … | P(>|z|) | S   | 2.5% | 97.5% |
|------|----------|----------|-----------|---|---------|-----|------|-------|
| hp   | +50      | 0.91     | 0.0291    | … | 0       | inf | 0.85 | 0.97  |

Columns: term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high

Slopes: Derivatives and elasticities

Consider a logistic regression model with a single predictor:

url = "https://vincentarelbundock.github.io/Rdatasets/csv/datasets/mtcars.csv"
mtcars = pl.read_csv(url)
mod = smf.logit("am ~ mpg", data = mtcars).fit()

Optimization terminated successfully.
         Current function value: 0.463674
         Iterations 6

We can estimate the slope of the prediction function with respect to the mpg variable at any point in the data space. For example, what is the slope of the prediction function at mpg = 24?

mfx = slopes(mod, newdata = datagrid(mpg = 24, newdata = mtcars))
print(mfx)

| Term | Contrast | Estimate | Std.Error | … | P(>|z|)  | S    | 2.5%   | 97.5% |
|------|----------|----------|-----------|---|----------|------|--------|-------|
| mpg  | dY/dX    | 0.0665   | 0.0178    | … | 0.000798 | 10.3 | 0.0301 | 0.103 |

Columns: term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high

This is equivalent to the result we obtain by taking the analytical derivative using the chain rule:

from scipy.stats import logistic
beta_0 = mod.params.iloc[0]
beta_1 = mod.params.iloc[1]
print(beta_1 * logistic.pdf(beta_0 + beta_1 * 24))

0.06653436463892946

This computes a “marginal effect (or slope) at the mean” or “at the median”, that is, when all covariates are held at their mean or median values:

mfx = slopes(mod, newdata = "mean")
print(mfx)

| Term | Contrast | Estimate | Std.Error | … | P(>|z|) | S    | 2.5%   | 97.5% |
|------|----------|----------|-----------|---|---------|------|--------|-------|
| mpg  | dY/dX    | 0.0732   | 0.0283    | … | 0.0148  | 6.08 | 0.0154 | 0.131 |

Columns: term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high

mfx = slopes(mod, newdata = "median")
print(mfx)

| Term | Contrast | Estimate | Std.Error | … | P(>|z|) | S    | 2.5%   | 97.5% |
|------|----------|----------|-----------|---|---------|------|--------|-------|
| mpg  | dY/dX    | 0.0679   | 0.0253    | … | 0.0118  | 6.41 | 0.0162 | 0.12  |

Columns: term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high

We can also compute an “average slope” or “average marginaleffects”

mfx = avg_slopes(mod)
print(mfx)

| Term | Contrast    | Estimate | Std.Error | … | P(>|z|)  | S    | 2.5%   | 97.5%  |
|------|-------------|----------|-----------|---|----------|------|--------|--------|
| mpg  | mean(dY/dX) | 0.0465   | 0.00886   | … | 1.16e-05 | 16.4 | 0.0284 | 0.0646 |

Columns: term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high

Which again is equivalent to the analytical result:

np.mean(beta_1 * logistic.pdf(beta_0 + beta_1 * mtcars["mpg"]))

0.04648596405936302

Grid

Predictions, comparisons, and slopes are typically “conditional” quantities which depend on the values of all the predictors in the model. By default, marginaleffects functions estimate quantities of interest for the empirical distribution of the data (i.e., for each row of the original dataset). However, users can specify the exact values of the predictors they want to investigate by using the newdata argument.

newdata accepts data frames like this:

pre = predictions(mod, newdata = mtcars.tail(2))
print(pre)

| Estimate | Std.Error | z    | P(>|z|)  | S    | 2.5%    | 97.5% |
|----------|-----------|------|----------|------|---------|-------|
| 0.119    | 0.0778    | 1.53 | 0.136    | 2.88 | -0.0396 | 0.278 |
| 0.492    | 0.12      | 4.11 | 0.000281 | 11.8 | 0.247   | 0.736 |

Columns: rowid, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, , mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb

The datagrid function gives us a powerful way to define a grid of predictors. All the variables not mentioned explicitly in datagrid() are fixed to their mean or mode:

pre = predictions(
  mod,
  newdata = datagrid(
    newdata = mtcars,
    am = [0, 1],
    wt = [mtcars["wt"].max(), mtcars["wt"].min()]))

print(pre)

| Estimate | Std.Error | z    | P(>|z|) | S    | 2.5%  | 97.5% |
|----------|-----------|------|---------|------|-------|-------|
| 0.393    | 0.108     | 3.63 | 0.00106 | 9.89 | 0.172 | 0.614 |
| 0.393    | 0.108     | 3.63 | 0.00106 | 9.89 | 0.172 | 0.614 |
| 0.393    | 0.108     | 3.63 | 0.00106 | 9.89 | 0.172 | 0.614 |
| 0.393    | 0.108     | 3.63 | 0.00106 | 9.89 | 0.172 | 0.614 |

Columns: rowid, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, am, wt, , mpg, cyl, disp, hp, drat, qsec, vs, gear, carb

Averaging

Since predictions, comparisons, and slopes are conditional quantities, they can be a bit unwieldy. Often, it can be useful to report a one-number summary instead of one estimate per observation. Instead of presenting “conditional” estimates, some methodologists recommend reporting “marginal” estimates, that is, an average of unit-level estimates.

(This use of the word “marginal” as “averaging” should not be confused with the term “marginal effect” which, in the econometrics tradition, corresponds to a partial derivative, or the effect of a “small/marginal” change.)

To marginalize (average over) our unit-level estimates, we can use the by argument or the one of the convenience functions: avg_predictions(), avg_comparisons(), or avg_slopes(). For example, both of these commands give us the same result: the average predicted outcome in the mtcars dataset:

pre = avg_predictions(mod)
print(pre)

| Estimate | Std.Error | z    | P(>|z|)  | S    | 2.5%  | 97.5% |
|----------|-----------|------|----------|------|-------|-------|
| 0.406    | 0.0688    | 5.91 | 1.81e-06 | 19.1 | 0.266 | 0.547 |

Columns: estimate, std_error, statistic, p_value, s_value, conf_low, conf_high

This is equivalent to manual computation by:

np.mean(mod.predict())

0.40624999999999994

The main marginaleffects functions all include a by argument, which allows us to marginalize within sub-groups of the data. For example,

cmp = avg_comparisons(mod, by = "am")
print(cmp)

| am | Term | Contrast | Estimate | … | P(>|z|)  | S    | 2.5%   | 97.5%  |
|----|------|----------|----------|---|----------|------|--------|--------|
| 1  | mpg  | +1       | 0.0449   | … | 1.44e-07 | 22.7 | 0.0315 | 0.0584 |
| 0  | mpg  | +1       | 0.0475   | … | 0.000285 | 11.8 | 0.0239 | 0.0711 |

Columns: am, term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high

Marginal Means are a special case of predictions, which are marginalized (or averaged) across a balanced grid of categorical predictors. To illustrate, we estimate a new model with categorical predictors:

dat = mtcars \
  .with_columns(
    pl.col("am").cast(pl.Boolean),
    pl.col("cyl").cast(pl.Utf8)
  )
mod_cat = smf.ols("mpg ~ am + cyl + hp", data = dat).fit()

We can compute marginal means manually using the functions already described:

pre = avg_predictions(
  mod_cat,
  newdata = datagrid(
    newdata = dat,
    cyl = dat["cyl"].unique(),
    am = dat["am"].unique()),
  by = "am")

print(pre)

cmp = avg_comparisons(mod_cat)
print(cmp)

| Term | Contrast                 | Estimate | Std.Error | … | P(>|z|) | S    | 2.5%    | 97.5%   |
|------|--------------------------|----------|-----------|---|---------|------|---------|---------|
| hp   | +1                       | -0.0442  | 0.0146    | … | 0.00527 | 7.57 | -0.0742 | -0.0143 |
| cyl  | 6 - 4                    | -3.92    | 1.54      | … | 0.0167  | 5.91 | -7.08   | -0.77   |
| cyl  | 8 - 4                    | -3.53    | 2.5       | … | 0.169   | 2.56 | -8.67   | 1.6     |
| am   | mean(True) - mean(False) | 4.16     | 1.26      | … | 0.00266 | 8.55 | 1.58    | 6.74    |

Columns: term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high

Hypothesis and equivalence tests

The hypotheses() function and the hypothesis argument can be used to conduct linear and non-linear hypothesis tests on model coefficients, or on any of the quantities computed by the functions introduced above.

Consider this model:

mod = smf.ols("mpg ~ qsec * drat", data = mtcars).fit()
mod.params

Intercept    12.337199
qsec         -1.024118
drat         -3.437146
qsec:drat     0.597315
dtype: float64

Can we reject the null hypothesis that the drat coefficient is 2 times the size of the qsec coefficient?

hyp = hypotheses(mod, "b3 = 2 * b2")
print(hyp)

| term    | estimate  | std_error | statistic | p_value  | s_value  | conf_low   | conf_high |
|---------|-----------|-----------|-----------|----------|----------|------------|-----------|
| b3=2*b2 | -1.388909 | 10.77593  | -0.12889  | 0.898366 | 0.154625 | -23.462402 | 20.684583 |

The main functions in marginaleffects all have a hypothesis argument, which means that we can do complex model testing. For example, consider two slope estimates:

range = lambda x: [x.max(), x.min()]
cmp = comparisons(
  mod,
  variables = "drat",
  newdata = datagrid(newdata = mtcars, qsec = range(mtcars["qsec"])))
print(cmp)

| Term | Contrast | Estimate | Std.Error | … | P(>|z|) | S    | 2.5%   | 97.5% |
|------|----------|----------|-----------|---|---------|------|--------|-------|
| drat | +1       | 10.2     | 5.16      | … | 0.0571  | 4.13 | -0.331 | 20.8  |
| drat | +1       | 5.22     | 3.79      | … | 0.179   | 2.48 | -2.54  | 13    |

Columns: rowid, term, contrast, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high, predicted, predicted_lo, predicted_hi, qsec, , mpg, cyl, disp, hp, drat, wt, vs, am, gear, carb

Are these two contrasts significantly different from one another? To test this, we can use the hypothesis argument:

cmp = comparisons(
  mod,
  hypothesis = "b1 = b2",
  variables = "drat",
  newdata = datagrid(newdata = mtcars, qsec = range(mtcars["qsec"])))
print(cmp)

| Term  | Estimate | Std.Error | z     | P(>|z|) | S     | 2.5%  | 97.5% |
|-------|----------|-----------|-------|---------|-------|-------|-------|
| b1=b2 | 5.02     | 8.52      | 0.589 | 0.561   | 0.835 | -12.4 | 22.5  |

Columns: term, estimate, std_error, statistic, p_value, s_value, conf_low, conf_high