registr (version 1.0.0)

loss_h: Loss function for registration step optimization

Description

Loss function for registration step optimization

Usage

loss_h(
  Y,
  Theta_h,
  mean_coefs,
  knots,
  beta.inner,
  family,
  t_min,
  t_max,
  parametric_warps = FALSE
)

Value

The scalar value taken by the loss function.

Arguments

Y

vector of observed points.

Theta_h

B-spline basis for inverse warping functions.

mean_coefs

spline coefficient vector for mean curve.

knots

knot locations for B-spline basis used to estimate mean and FPC basis function.

beta.inner

spline coefficient vector to be estimated for warping function h.

family

gaussian or binomial.

t_min

minimum value to be evaluated on the time domain.

t_max

maximum value to be evaluated on the time domain.

parametric_warps

If FALSE (default), inverse warping functions are estimated nonparametrically. If 'beta_cdf', they are assumed to have the form of a Beta(a,b) CDF. If 'piecewise' they follow a piecewise parameterized function.