Mixed-model post-fit containers.
Classes:
| Name | Description |
|---|---|
ProfileState | Profile likelihood state for variance component CIs. |
VaryingSpreadState | Variance components for mixed models. |
VaryingState | Random effects (BLUPs) for mixed models. |
Classes¶
ProfileState¶
Profile likelihood state for variance component CIs.
Created by: profile_likelihood(), ProfileState(**profile_data) Consumed by: infer_profile_variance_components(), plot_profile() Augmented by: Never
Attributes:
| Name | Type | Description |
|---|---|---|
ci_lower_sd | NDArray[floating] | |
ci_sd | dict[str, tuple[float, float]] | |
ci_theta | dict[str, tuple[float, float]] | |
ci_upper_sd | NDArray[floating] | |
conf_level | float | |
dev_opt | float | |
spline_forward | dict[str, Any] | |
spline_reverse | dict[str, Any] | |
table | ‘pl.DataFrame’ | |
threshold | float |
Attributes¶
ci_lower_sd¶
ci_lower_sd: NDArray[np.floating] = field(repr=False, validator=is_ndarray)ci_sd¶
ci_sd: dict[str, tuple[float, float]] = field(validator=(validators.instance_of(dict)))ci_theta¶
ci_theta: dict[str, tuple[float, float]] = field(validator=(validators.instance_of(dict)))ci_upper_sd¶
ci_upper_sd: NDArray[np.floating] = field(repr=False, validator=is_ndarray)conf_level¶
conf_level: float = field(converter=normalize_conf_level, validator=is_conf_level)dev_opt¶
dev_opt: float = field(validator=(validators.instance_of((int, float))))spline_forward¶
spline_forward: dict[str, Any] = field(repr=False, validator=(validators.instance_of(dict)))spline_reverse¶
spline_reverse: dict[str, Any] = field(repr=False, validator=(validators.instance_of(dict)))table¶
table: 'pl.DataFrame' = field(repr=False)threshold¶
threshold: float = field(default=4.0, validator=[validators.instance_of((int, float)), validators.gt(0)])VaryingSpreadState¶
Variance components for mixed models.
Created by: build_varying_spread_state(), build_mixed_post_fit_state() Consumed by: build_varying_spread_dataframe(), model.varying_spread, model.varying_corr Augmented by: attrs.evolve() after infer() adds confidence intervals
Attributes:
| Name | Type | Description |
|---|---|---|
ci_lower | dict[str, float] | None | |
ci_method | str | None | |
ci_upper | dict[str, float] | None | |
components | ‘pl.DataFrame’ | |
conf_level | float | None | |
has_inference | bool | Check if confidence intervals have been computed. |
icc | float | None | |
rho | dict[str, float] | |
sigma2 | float | |
tau2 | dict[str, float] |
Attributes¶
ci_lower¶
ci_lower: dict[str, float] | None = field(default=None, validator=(validators.optional(validators.instance_of(dict))))ci_method¶
ci_method: str | None = field(default=None, validator=(validators.optional(validators.in_(('profile', 'wald')))))ci_upper¶
ci_upper: dict[str, float] | None = field(default=None, validator=(validators.optional(validators.instance_of(dict))))components¶
components: 'pl.DataFrame' = field(repr=False)conf_level¶
conf_level: float | None = field(default=None, converter=normalize_optional_conf_level, validator=is_optional_conf_level)has_inference¶
has_inference: boolCheck if confidence intervals have been computed.
icc¶
icc: float | None = field(default=None, validator=(validators.optional(validators.instance_of((int, float)))))rho¶
rho: dict[str, float] = field(factory=dict, validator=(validators.instance_of(dict)))sigma2¶
sigma2: float = field(validator=(validators.instance_of((int, float))))tau2¶
tau2: dict[str, float] = field(validator=(validators.instance_of(dict)))VaryingState¶
Random effects (BLUPs) for mixed models.
Created by: build_varying_state(), build_mixed_post_fit_state() Consumed by: build_varying_offsets_dataframe(), model.varying_offsets, model.varying_params Augmented by: attrs.evolve() after infer() adds prediction intervals
Attributes:
| Name | Type | Description |
|---|---|---|
conf_level | float | None | |
effects | dict[str, ndarray] | |
grid | ‘pl.DataFrame’ | |
grouping_var | str | |
has_inference | bool | Check if prediction intervals have been computed. |
n_groups | int | |
pi_lower | dict[str, ndarray] | None | |
pi_upper | dict[str, ndarray] | None |
Attributes¶
conf_level¶
conf_level: float | None = field(default=None, converter=normalize_optional_conf_level, validator=is_optional_conf_level)effects¶
effects: dict[str, np.ndarray] = field(validator=(validators.instance_of(dict)))grid¶
grid: 'pl.DataFrame' = field(repr=False)grouping_var¶
grouping_var: str = field(validator=(validators.instance_of(str)))has_inference¶
has_inference: boolCheck if prediction intervals have been computed.
n_groups¶
n_groups: int = field(validator=is_positive_int)pi_lower¶
pi_lower: dict[str, np.ndarray] | None = field(default=None, validator=(validators.optional(validators.instance_of(dict))))pi_upper¶
pi_upper: dict[str, np.ndarray] | None = field(default=None, validator=(validators.optional(validators.instance_of(dict))))