Prediction¶

Generate predictions from a fitted CrossedLMEResult. The recommended interface is result.predict(newdata=...) — this page documents the underlying function and its parameters. Works with both CrossedLMEResult and statsmodels.MixedLMResults.

predict(result, newdata=None, include_re=True)[source]¶

Return predictions from a fitted CrossedLMEResult.

Parameters:

result – A fitted CrossedLMEResult.
newdata – DataFrame to predict on. If None, returns result.fittedvalues (in-sample conditional predictions). Any narwhals-compatible frame (pandas, polars, …) is accepted.
include_re – If True (default), add BLUP contributions for known group levels. If False, return fixed-effects-only predictions (population mean).

Return type:

ndarray

Returns:

np.ndarray of shape (n_obs,)

Parameters:

result (CrossedLMEResult)
newdata (Any | None)
include_re (bool)

Conditional vs marginal predictions¶

Type	Includes BLUPs?	Use when
Conditional	Yes	Predicting for known groups already in the training data
Marginal	No (fixed effects only)	Predicting for new, unseen groups

Unseen group levels automatically receive a BLUP of zero (pure population mean) — no special handling required.

Examples¶

Predicting for known groups¶

import pandas as pd

df_new = pd.DataFrame({
    "hours_studied": [5.0, 8.0],
    "prior_gpa":     [3.2, 3.8],
    "student_id":    ["s1", "s2"],   # known groups — BLUPs applied
    "school_id":     ["sch1", "sch1"],
})

preds = result.predict(newdata=df_new)
print(preds)  # conditional predictions including group offsets

Predicting for new (unseen) groups¶

df_new = pd.DataFrame({
    "hours_studied": [6.0],
    "prior_gpa":     [3.5],
    "student_id":    ["s_new"],  # unseen → BLUP shrinks to 0
    "school_id":     ["sch_new"],
})

preds = result.predict(newdata=df_new)
# Returns marginal prediction (population average for these covariate values)

Marginal predictions (fixed effects only)¶

# Set all group columns to a sentinel unseen value, or omit them if supported
preds_marginal = result.predict(newdata=df_new, include_re=False)