Struct linregress::FormulaRegressionBuilder
source · [−]pub struct FormulaRegressionBuilder<'a> { /* private fields */ }
Expand description
A builder to create and fit a linear regression model.
Given a dataset and a set of columns to use this builder will produce an ordinary least squared linear regression model.
See formula
and data
for details on how to configure this builder.
The pseudo inverse method is used to fit the model.
Usage
use linregress::{FormulaRegressionBuilder, RegressionDataBuilder};
let y = vec![1., 2. ,3., 4.];
let x = vec![4., 3., 2., 1.];
let data = vec![("Y", y), ("X", x)];
let data = RegressionDataBuilder::new().build_from(data)?;
let model = FormulaRegressionBuilder::new().data(&data).formula("Y ~ X").fit()?;
// Alternatively
let model = FormulaRegressionBuilder::new().data(&data).data_columns("Y", ["X"]).fit()?;
assert_eq!(model.parameters.intercept_value, 4.999999999999998);
assert_eq!(model.parameters.regressor_values[0], -0.9999999999999989);
assert_eq!(model.parameters.regressor_names[0], "X");
Implementations
Set the data to be used for the regression.
The data has to be given as a reference to a RegressionData
struct.
See RegressionDataBuilder
for details.
Set the formula to use for the regression.
The expected format is <regressand> ~ <regressor 1> + <regressor 2>
.
E.g. for a regressand named Y and three regressors named A, B and C
the correct format would be Y ~ A + B + C
.
Note that there is currently no special support for categorical variables.
So if you have a categorical variable with more than two distinct values
or values that are not 0
and 1
you will need to perform “dummy coding” yourself.
Alternatively you can use data_columns
.
pub fn data_columns<I, S1, S2>(self, regressand: S1, regressors: I) -> Self where
I: IntoIterator<Item = S2>,
S1: Into<Cow<'a, str>>,
S2: Into<Cow<'a, str>>,
pub fn data_columns<I, S1, S2>(self, regressand: S1, regressors: I) -> Self where
I: IntoIterator<Item = S2>,
S1: Into<Cow<'a, str>>,
S2: Into<Cow<'a, str>>,
Set the columns to be used as regressand and regressors for the regression.
Note that there is currently no special support for categorical variables.
So if you have a categorical variable with more than two distinct values
or values that are not 0
and 1
you will need to perform “dummy coding” yourself.
Alternatively you can use formula
.
Fits the model and returns a RegressionModel
if successful.
You need to set the data with data
and a formula with formula
before you can use it.
Like fit
but does not perfom any statistics on the resulting model.
Returns a RegressionParameters
struct containing the model parameters
if successfull.
This is usefull if you do not care about the statistics or the model and data you want to fit result in too few residual degrees of freedom to perform statistics.
Trait Implementations
Auto Trait Implementations
impl<'a> RefUnwindSafe for FormulaRegressionBuilder<'a>
impl<'a> Send for FormulaRegressionBuilder<'a>
impl<'a> Sync for FormulaRegressionBuilder<'a>
impl<'a> Unpin for FormulaRegressionBuilder<'a>
impl<'a> UnwindSafe for FormulaRegressionBuilder<'a>
Blanket Implementations
Mutably borrows from an owned value. Read more
The inverse inclusion map: attempts to construct self
from the equivalent element of its
superset. Read more
Checks if self
is actually part of its subset T
(and can be converted to it).
Use with care! Same as self.to_subset
but without any property checks. Always succeeds.
The inclusion map: converts self
to the equivalent element of its superset.