# Built-in datasets Whittaker ships with 10 synthetic datasets covering a range of response families and modeling scenarios. They are generated with fixed random seeds, so they are fully reproducible without internet access or optional dependencies. Use them for quick demonstrations, testing, and learning. # Listing available datasets [list_datasets()](../reference/list_datasets.md#whittaker.list_datasets) returns metadata for every built-in dataset: ``` python import whittaker as wk for ds in wk.list_datasets(): print(f"{ds['name']:>14} {ds['family']:<12} {ds['description']}") ``` mcycle Gaussian Simulated motorcycle-crash accelerometer data (n=133). co2 Gaussian Synthetic monthly atmospheric CO2 concentrations (n=504, 1958-1999). fish Poisson Synthetic fish-count survey data (n=300). credit Binomial Synthetic credit-default dataset (n=1000). wages Gamma Synthetic worker-earnings dataset (n=800). proportions Beta Synthetic seed-germination dataset (n=400). meuse Gaussian Synthetic river-bank heavy-metals dataset (n=155). survival CoxPH Synthetic clinical-trial survival dataset (n=250). abalone Gaussian Synthetic abalone morphology dataset (n=500). climate GaussianLS Synthetic climate station dataset (n=600). Each entry includes the dataset `name`, a short `description`, the intended response `family`, the `variables` it contains, and a `note` about what modeling scenario it illustrates. # Loading a dataset [load_dataset()](../reference/load_dataset.md#whittaker.load_dataset) returns a column-oriented dictionary that can be passed directly to [GAM.fit()](../reference/GAM.md#whittaker.GAM.fit): ``` python data = wk.load_dataset("mcycle") print(f"Type: {type(data)}") print(f"Keys: {list(data.keys())}") print(f"Observations: {len(data['times'])}") ``` Type: Keys: ['times', 'accel'] Observations: 133 ``` python model = wk.GAM("accel ~ s(times)").fit(data) model.summary() ``` GAM fit summary ============================================================ Formula: accel ~ s(times) Family: Gaussian(link='identity') Inference: GCV Observations: 133 Coefficients: 10 Parametric coefficients: Term Estimate Std.Err t value p-value ------------------------ ---------- ---------- ---------- ---------- (Intercept) -45.6924 1.8364 -24.882 < 1e-16 Approximate significance of smooth terms: Term EDF Ref.df Chi.sq p-value ------------------------ ------ ------ ---------- ---------- s(times) 7.92 8 663.396 < 1e-16 Total EDF: 8.92 Scale est: 448.517253 Deviance: 55653.7152 Null dev: 357878.4929 Dev. expl: 84.4% GCV score: 480.746119 AIC: 1198.44 BIC: 1224.22 ## Loading as a DataFrame Pass `as_frame=True` to get a pandas DataFrame instead (requires pandas): ``` python df = wk.load_dataset("wages", as_frame=True) df.head() ```

PandasRows5Columns3
	age f64	experience f64	wage f64
0	62.3236369619	9.21681747511	80.2312228265
1	42.0323949823	2.48784091836	40.0241886358
2	63.8834541683	7.2519118615	60.2928515793
3	21.7992931231	3.79929312309	20.2329400441
4	46.5457241038	1.73811889414	62.3832136627

# Dataset catalog ## mcycle -- Gaussian, heteroscedastic Simulated motorcycle-crash accelerometer data (n=133). Head acceleration measured at various times after impact. Strongly non-linear and heteroscedastic -- a standard stress test for smoothing. ``` python data = wk.load_dataset("mcycle") model = wk.GAM("accel ~ s(times)").fit(data) gof = model.goodness_of_fit() print(f"EDF: {gof.edf_total:.1f}, Dev. explained: {gof.deviance_explained:.1%}") ``` EDF: 8.9, Dev. explained: 84.4% ## co2 -- Gaussian, trend + seasonal Synthetic monthly CO2 concentrations (n=504, 1958-1999) with a rising trend and annual cycle. Good for cyclic smooths and additive decomposition. ``` python data = wk.load_dataset("co2") model = wk.GAM("co2 ~ s(t) + s(month, bs='cc', k=12)").fit(data, method="REML") gof = model.goodness_of_fit() print(f"EDF: {gof.edf_total:.1f}, Dev. explained: {gof.deviance_explained:.1%}") ``` EDF: 16.5, Dev. explained: 100.0% ## fish -- Poisson counts Fish-abundance survey (n=300) with a hump-shaped temperature effect and a linear depth effect. ``` python from whittaker.families.poisson import Poisson data = wk.load_dataset("fish") model = wk.GAM("count ~ s(temperature) + s(depth)", family=Poisson()).fit(data) gof = model.goodness_of_fit() print(f"EDF: {gof.edf_total:.1f}, Dev. explained: {gof.deviance_explained:.1%}") ``` EDF: 7.0, Dev. explained: 62.0% ## credit -- Binomial (binary) Credit-default dataset (n=1000) with smooth effects of income and debt ratio on default probability. ``` python from whittaker.families.binomial import Binomial data = wk.load_dataset("credit") model = wk.GAM("default ~ s(income) + s(debt_ratio) + s(age)", family=Binomial()).fit(data) gof = model.goodness_of_fit() print(f"EDF: {gof.edf_total:.1f}, Dev. explained: {gof.deviance_explained:.1%}") ``` EDF: 8.1, Dev. explained: 16.7% ## wages -- Gamma Worker-earnings dataset (n=800) with log-wages shaped by smooth age and experience effects. ``` python from whittaker.families.gamma import Gamma data = wk.load_dataset("wages") model = wk.GAM("wage ~ s(age) + s(experience)", family=Gamma()).fit(data) gof = model.goodness_of_fit() print(f"EDF: {gof.edf_total:.1f}, Dev. explained: {gof.deviance_explained:.1%}") ``` EDF: 7.1, Dev. explained: 78.2% ## proportions -- Beta Seed-germination dataset (n=400) with a bounded `[0, 1]` response and a non-linear temperature optimum. ``` python from whittaker.families.beta import Beta data = wk.load_dataset("proportions") model = wk.GAM("germination_rate ~ s(temperature) + s(water)", family=Beta()).fit(data) gof = model.goodness_of_fit() print(f"EDF: {gof.edf_total:.1f}, Dev. explained: {gof.deviance_explained:.1%}") ``` EDF: 7.8, Dev. explained: 65.5% ## meuse -- Gaussian, spatial River-bank heavy-metals dataset (n=155) with map coordinates. Log(zinc) decreases with distance from the river. Good for 2D spatial smooths. ``` python import numpy as np data = wk.load_dataset("meuse") data["log_zinc"] = np.log(data["zinc"]) model = wk.GAM("log_zinc ~ s(x, y) + s(dist)").fit(data, method="REML") gof = model.goodness_of_fit() print(f"EDF: {gof.edf_total:.1f}, Dev. explained: {gof.deviance_explained:.1%}") ``` EDF: 3.0, Dev. explained: 61.8% ## survival -- Cox PH Clinical-trial survival dataset (n=250) with a smooth age effect and a binary treatment arm. Approximately 30% censored. ``` python from whittaker.families.cox_ph import CoxPH data = wk.load_dataset("survival") model = wk.GAM("time ~ s(age) + treatment", family=CoxPH(status="event")).fit(data) print(f"EDF: {model.goodness_of_fit().edf_total:.1f}") ``` EDF: 3.1 ## abalone -- Gaussian, multi-predictor Abalone morphology dataset (n=500) with four predictors and a ring count response. Good for tensor products and multi-term additive models. ``` python data = wk.load_dataset("abalone") model = wk.GAM("rings ~ s(length) + s(shucked_weight)").fit(data) gof = model.goodness_of_fit() print(f"EDF: {gof.edf_total:.1f}, Dev. explained: {gof.deviance_explained:.1%}") ``` EDF: 11.4, Dev. explained: 82.7% ## climate -- GaussianLS (location-scale) Climate station dataset (n=600) where both the mean and variance of temperature depend on altitude and latitude. Designed for GAMLSS location-scale modeling. ``` python data = wk.load_dataset("climate") model = wk.GAM("temperature ~ s(altitude) + s(latitude) + s(month, bs='cc', k=12)").fit( data, method="REML" ) gof = model.goodness_of_fit() print(f"EDF: {gof.edf_total:.1f}, Dev. explained: {gof.deviance_explained:.1%}") ``` EDF: 9.5, Dev. explained: -19.9% > **Tip: GAMLSS with the climate dataset** > > For the full location-scale analysis, see [Distributional regression (GAMLSS)](gamlss.md), which models both the mean and variance as smooth functions of the predictors. # Where to go next - **[Quick start](quick-start.md)**: fitting your first model. - **[Response families](families.md)**: choosing the right family for your data. - **[Data input](data-input.md)**: using your own data with pandas, polars, or pyarrow.