pub struct Cloglog {}
Expand description
The complementary log-log link g(p) = log(-log(1-p)) is appropriate when modeling the probability of non-zero counts when the counts are Poisson-distributed with mean lambda = exp(lin_pred).
Trait Implementations§
Source§impl Link<Logistic<Cloglog>> for Cloglog
impl Link<Logistic<Cloglog>> for Cloglog
Source§impl Transform for Cloglog
impl Transform for Cloglog
Source§fn nat_param<F: Float>(lin_pred: Array1<F>) -> Array1<F>
fn nat_param<F: Float>(lin_pred: Array1<F>) -> Array1<F>
The natural parameter(s) of the response distribution as a function
of the linear predictor. For canonical link functions this is the
identity. It must be monotonic, invertible, and twice-differentiable.
For link function g and canonical link function g_0 it is equal to
g_0 ( g^{-1}(lin_pred) ) .
Source§fn d_nat_param<F: Float>(lin_pred: &Array1<F>) -> Array1<F>
fn d_nat_param<F: Float>(lin_pred: &Array1<F>) -> Array1<F>
The derivative of the transformation to the natural parameter. If it is
zero in a region that the IRLS is in the algorithm may have difficulty
converging.
Source§fn adjust_errors_variance<F: Float>(
errors: Array1<F>,
variance: Array1<F>,
lin_pred: &Array1<F>,
) -> (Array1<F>, Array1<F>)
fn adjust_errors_variance<F: Float>( errors: Array1<F>, variance: Array1<F>, lin_pred: &Array1<F>, ) -> (Array1<F>, Array1<F>)
Adjust the error and variance terms of the likelihood function based on
the first and second derivatives of the transformation. The adjustment
is performed simultaneously. The linear predictor must be
un-transformed, i.e. it must be X*beta without the transformation
applied.
Auto Trait Implementations§
impl Freeze for Cloglog
impl RefUnwindSafe for Cloglog
impl Send for Cloglog
impl Sync for Cloglog
impl Unpin for Cloglog
impl UnwindSafe for Cloglog
Blanket Implementations§
Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
Source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more