rbentfit {Rbent} | R Documentation |
This function use Wilcoxon score functions for fitting the bent line regression model.
rbentfit(y, z, x, bet.ini, tau.ini, tol = 1e-04, max.iter = 50)
y |
A vector of response |
z |
A vector of covariates |
x |
A numeric variable with change point |
bet.ini |
A initial vector of regression coefficients |
tau.ini |
A initial value of change point |
tol |
tolerance value, 1e-4 for default |
max.iter |
the maximum iteration steps |
A list with the elements
est |
The estimated regression coefficients with intercept. |
bp |
The estimated change point. |
est.se |
The estimated standard error of the regression coefficients. |
bp.est |
The estimated standard error of the change point. |
iter |
The iteration steps. |
Feipeng Zhang
n <- 150 x <- runif(n, 0, 4) z <- rnorm(n, 1, 1) y <- 1 + 0.5*z + 1.5*x - 3 *pmax(x-2, 0) + rt(n, 2) rbentfit(y, cbind(1,z), x, bet.ini = c(0, 1, 1, -2), tau.ini = 1) # for the example of MRS data data(data_mrs) x <- log(data_mrs$mass) y <- log(data_mrs$speed) z <- data_mrs$hopper tau.ini <- 3 dat.new <- data.frame(y=y, z1=z, z2 = x, z3=pmax(x-tau.ini,0)) library(Rfit) fit.ini <- rfit(y~ z1 + z2 +z3, data= dat.new) # with intercept bet.ini <- fit.ini$coef fit.rank <- rbentfit(y, cbind(1,z), x, bet.ini, tau.ini)