Description Usage Arguments Value Author(s) Examples
The grid search algorithm for the continuous threshold expectile regression
1 | cterFit(y, x, z, tau = 0.5, max.iter = 100, tol = 1e-04)
|
y |
A vector of response |
x |
A scalar covariate with threshold |
z |
A vector of covariates |
tau |
the expectile level, 0.5 for default |
max.iter |
the maximum iteration steps, 100 for default |
tol |
tolerance value, 1e-4 for default |
A list with the elements
coef.est |
The estimated regression coefficients with intercept. |
threshold.est |
The estimated threshold. |
coef.se |
The estimated standard error of the regression coefficients. |
threshold.se |
The estimated standard error of the threshold. |
iter |
The iteration steps. |
Feipeng Zhang and Qunhua Li
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ## simulated data
ptm <- proc.time()
n <- 200
t0 <- 1.5
bet0 <- c(1, 3, -2, 1)
tau <- 0.3
modtype <- 1
errtype <- 1
dat <- cterSimData(n, bet0, t0, tau, modtype, errtype)
y <- dat[, 1]
x <- dat[, 2]
z <- dat[, 3]
fit <- cterFit(y, x, z, tau)
## The example of Baseball pitcher salary
data(data_bbsalaries)
y <- data_bbsalaries$y
x <- data_bbsalaries$x
z <- NULL
tau <- 0.5
fit <- cterFit(y, x, z, tau)
proc.time() - ptm
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