Struct linregress::RegressionModel
source · [−]pub struct RegressionModel { /* private fields */ }
Expand description
A fitted regression model.
Is the result of FormulaRegressionBuilder.fit()
.
Implementations
sourceimpl RegressionModel
impl RegressionModel
sourcepub fn regressor_names(&self) -> &[String]
pub fn regressor_names(&self) -> &[String]
The names of the regressor columns
sourcepub fn p_values(&self) -> &[f64]
pub fn p_values(&self) -> &[f64]
The two-tailed p-values for the t-statistics of the parameters
sourcepub fn iter_p_value_pairs(&self) -> impl Iterator<Item = (&str, f64)> + '_
pub fn iter_p_value_pairs(&self) -> impl Iterator<Item = (&str, f64)> + '_
Iterates over pairs of regressor columns and their associated p-values
Note
This does not include the value for the intercept.
Usage
use linregress::{FormulaRegressionBuilder, RegressionDataBuilder};
let y = vec![1.,2. ,3. , 4.];
let x1 = vec![4., 3., 2., 1.];
let x2 = vec![1., 2., 3., 4.];
let data = vec![("Y", y), ("X1", x1), ("X2", x2)];
let data = RegressionDataBuilder::new().build_from(data)?;
let model = FormulaRegressionBuilder::new().data(&data).formula("Y ~ X1 + X2").fit()?;
let pairs: Vec<(&str, f64)> = model.iter_p_value_pairs().collect();
assert_eq!(pairs[0], ("X1", 1.7052707580549508e-28));
assert_eq!(pairs[1], ("X2", 2.522589878779506e-31));
sourcepub fn parameters(&self) -> &[f64]
pub fn parameters(&self) -> &[f64]
The model’s intercept and slopes (also known as betas)
sourcepub fn iter_parameter_pairs(&self) -> impl Iterator<Item = (&str, f64)> + '_
pub fn iter_parameter_pairs(&self) -> impl Iterator<Item = (&str, f64)> + '_
Iterates over pairs of regressor columns and their associated slope values
Note
This does not include the value for the intercept.
Usage
use linregress::{FormulaRegressionBuilder, RegressionDataBuilder};
let y = vec![1.,2. ,3. , 4.];
let x1 = vec![4., 3., 2., 1.];
let x2 = vec![1., 2., 3., 4.];
let data = vec![("Y", y), ("X1", x1), ("X2", x2)];
let data = RegressionDataBuilder::new().build_from(data)?;
let model = FormulaRegressionBuilder::new().data(&data).formula("Y ~ X1 + X2").fit()?;
let pairs: Vec<(&str, f64)> = model.iter_parameter_pairs().collect();
assert_eq!(pairs[0], ("X1", -0.03703703703703709));
assert_eq!(pairs[1], ("X2", 0.9629629629629626));
sourcepub fn iter_se_pairs(&self) -> impl Iterator<Item = (&str, f64)> + '_
pub fn iter_se_pairs(&self) -> impl Iterator<Item = (&str, f64)> + '_
Iterates over pairs of regressor columns and their associated standard errors
Note
This does not include the value for the intercept.
Usage
use linregress::{FormulaRegressionBuilder, RegressionDataBuilder};
let y = vec![1.,2. ,3. , 4.];
let x1 = vec![4., 3., 2., 1.];
let x2 = vec![1., 2., 3., 4.];
let data = vec![("Y", y), ("X1", x1), ("X2", x2)];
let data = RegressionDataBuilder::new().build_from(data)?;
let model = FormulaRegressionBuilder::new().data(&data).formula("Y ~ X1 + X2").fit()?;
let pairs: Vec<(&str, f64)> = model.iter_parameter_pairs().collect();
assert_eq!(pairs[0], ("X1", -0.03703703703703709));
assert_eq!(pairs[1], ("X2", 0.9629629629629626));
sourcepub fn rsquared_adj(&self) -> f64
pub fn rsquared_adj(&self) -> f64
Adjusted R-squared of the model
sourcepub fn scale(&self) -> f64
pub fn scale(&self) -> f64
A scale factor for the covariance matrix
Note that the square root of scale
is often
called the standard error of the regression.
sourcepub fn predict<'a, I, S>(&self, new_data: I) -> Result<Vec<f64>, Error> where
I: IntoIterator<Item = (S, Vec<f64>)>,
S: Into<Cow<'a, str>>,
pub fn predict<'a, I, S>(&self, new_data: I) -> Result<Vec<f64>, Error> where
I: IntoIterator<Item = (S, Vec<f64>)>,
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_slices_almost_eq!(&prediction, &[3.500000000000028, 2.5000000000000644]);
Trait Implementations
sourceimpl Clone for RegressionModel
impl Clone for RegressionModel
sourcefn clone(&self) -> RegressionModel
fn clone(&self) -> RegressionModel
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
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
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
The inverse inclusion map: attempts to construct self
from the equivalent element of its
superset. Read more
fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
Checks if self
is actually part of its subset T
(and can be converted to it).
fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
Use with care! Same as self.to_subset
but without any property checks. Always succeeds.
fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts self
to the equivalent element of its superset.