View source: R/fit_elastic_regression.R
fit_elastic_regression | R Documentation |
Computes a Fréchet mean for the curves stored in data_curves
with respect
to the elastic distance. Constructor function for class elastic_reg_model
.
fit_elastic_regression( formula, data_curves, x_data, knots = seq(0, 1, 0.2), type = "smooth", closed = FALSE, max_iter = 10, eps = 0.001 )
formula |
an object of class "formula" of the form data_curves ~ ...". |
data_curves |
list of |
x_data |
a |
knots |
set of knots for the parameter curves of the regression model |
type |
if "smooth" linear srv-splines are used which results in a differentiable mean curve if "polygon" the mean will be piecewise linear. |
closed |
|
max_iter |
maximal number of iterations |
eps |
the algorithm stops if L2 norm of coefficients changes less |
an object of class elastic_reg_model
, which is a list
with entries
type |
"smooth" if linear srv-splines or "polygon" if constant srv-splines were used |
coefs |
spline coeffiecients |
knots |
spline knots |
data_curves |
list of |
closed |
|
curve <- function(x_1, x_2, t){ rbind(2*t*cos(6*t) - x_1*t , x_2*t*sin(6*t)) } set.seed(18) x_data <- data.frame(x_1 = runif(10,-1,1), x_2 = runif(10,-1,1)) data_curves <- apply(x_data, 1, function(x){ m <- sample(10:15, 1) delta <- abs(rnorm(m, mean = 1, sd = 0.05)) t <- cumsum(delta)/sum(delta) data.frame(t(curve((x[1] + 1), (x[2] + 2), t)) + 0.07*t*matrix(cumsum(rnorm(2*length(delta))), ncol = 2)) }) reg_model <- fit_elastic_regression(data_curves ~ x_1 + x_2, data_curves = data_curves, x_data = x_data) plot(reg_model)
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