Struct frame_benchmarking::RegressionModel [−][src]
A fitted regression model.
Is the result of FormulaRegressionBuilder.fit()
.
If a field has only one value for the model it is given as f64
.
Otherwise it is given as a RegressionParameters
struct.
Fields
parameters: RegressionParameters
The model’s intercept and slopes (also known as betas).
se: RegressionParameters
The standard errors of the parameter estimates.
ssr: f64
Sum of squared residuals.
rsquared: f64
R-squared of the model.
rsquared_adj: f64
Adjusted R-squared of the model.
pvalues: RegressionParameters
The two-tailed p-values for the t-statistics of the params.
residuals: RegressionParameters
The residuals of the model.
scale: f64
A scale factor for the covariance matrix.
Note that the square root of scale
is often
called the standard error of the regression.
Implementations
impl RegressionModel
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pub fn predict<'a, I, S>(&self, new_data: I) -> Result<Vec<f64, Global>, Error> where
I: IntoIterator<Item = (S, Vec<f64, Global>)>,
S: Into<Cow<'a, str>>,
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I: IntoIterator<Item = (S, Vec<f64, Global>)>,
S: Into<Cow<'a, str>>,
Evaluates the model on given new input data and returns the predicted values.
The new data is expected to have the same columns as the original data.
See RegressionDataBuilder.build
for details on the type of the new_data
parameter.
Note
This function does no special handling of non real values (NaN or infinity or negative infinity).
Such a value in new_data
will result in a corresponding meaningless prediction.
Example
let y = vec![1., 2., 3., 4., 5.]; let x1 = vec![5., 4., 3., 2., 1.]; let x2 = vec![729.53, 439.0367, 42.054, 1., 0.]; let x3 = vec![258.589, 616.297, 215.061, 498.361, 0.]; let data = vec![("Y", y), ("X1", x1), ("X2", x2), ("X3", x3)]; let data = RegressionDataBuilder::new().build_from(data).unwrap(); let formula = "Y ~ X1 + X2 + X3"; let model = FormulaRegressionBuilder::new() .data(&data) .formula(formula) .fit()?; let new_data = vec![ ("X1", vec![2.5, 3.5]), ("X2", vec![2.0, 8.0]), ("X3", vec![2.0, 1.0]), ]; let prediction: Vec<f64> = model.predict(new_data)?; assert_eq!(prediction, vec![3.5000000000000275, 2.5000000000000533]);
Trait Implementations
impl Clone for RegressionModel
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pub fn clone(&self) -> RegressionModel
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pub fn clone_from(&mut self, source: &Self)
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impl Debug for RegressionModel
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Auto Trait Implementations
impl RefUnwindSafe for RegressionModel
impl Send for RegressionModel
impl Sync for RegressionModel
impl Unpin for RegressionModel
impl UnwindSafe for RegressionModel
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
pub fn borrow_mut(&mut self) -> &mut T
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impl<T> CheckedConversion for T
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pub fn checked_from<T>(t: T) -> Option<Self> where
Self: TryFrom<T>,
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Self: TryFrom<T>,
pub fn checked_into<T>(self) -> Option<T> where
Self: TryInto<T>,
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Self: TryInto<T>,
impl<T> DynClone for T where
T: Clone,
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T: Clone,
pub fn __clone_box(&self, Private) -> *mut ()
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impl<T> From<T> for T
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impl<T> Instrument for T
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pub fn instrument(self, span: Span) -> Instrumented<Self>
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pub fn in_current_span(self) -> Instrumented<Self>
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T> IsType<T> for T
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pub fn from_ref(t: &T) -> &T
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pub fn into_ref(&self) -> &T
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pub fn from_mut(t: &mut T) -> &mut T
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pub fn into_mut(&mut self) -> &mut T
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impl<T, Outer> IsWrappedBy<Outer> for T where
T: From<Outer>,
Outer: AsRef<T> + AsMut<T> + From<T>,
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T: From<Outer>,
Outer: AsRef<T> + AsMut<T> + From<T>,
pub fn from_ref(outer: &Outer) -> &T
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Get a reference to the inner from the outer.
pub fn from_mut(outer: &mut Outer) -> &mut T
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Get a mutable reference to the inner from the outer.
impl<T> MaybeDebug for T where
T: Debug,
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T: Debug,
impl<T> MaybeDebug for T where
T: Debug,
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T: Debug,
impl<T> MaybeRefUnwindSafe for T where
T: RefUnwindSafe,
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T: RefUnwindSafe,
impl<T> Same<T> for T
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type Output = T
Should always be Self
impl<T> SaturatedConversion for T
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pub fn saturated_from<T>(t: T) -> Self where
Self: UniqueSaturatedFrom<T>,
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Self: UniqueSaturatedFrom<T>,
pub fn saturated_into<T>(self) -> T where
Self: UniqueSaturatedInto<T>,
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Self: UniqueSaturatedInto<T>,
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
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SS: SubsetOf<SP>,
pub fn to_subset(&self) -> Option<SS>
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pub fn is_in_subset(&self) -> bool
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pub fn to_subset_unchecked(&self) -> SS
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pub fn from_subset(element: &SS) -> SP
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impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
pub fn to_owned(&self) -> T
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pub fn clone_into(&self, target: &mut T)
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
pub fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
pub fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<S, T> UncheckedInto<T> for S where
T: UncheckedFrom<S>,
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T: UncheckedFrom<S>,
pub fn unchecked_into(self) -> T
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impl<T, S> UniqueSaturatedInto<T> for S where
T: Bounded,
S: TryInto<T>,
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T: Bounded,
S: TryInto<T>,
pub fn unique_saturated_into(self) -> T
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impl<V, T> VZip<V> for T where
V: MultiLane<T>,
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V: MultiLane<T>,