Internal functions not intended for users.
NLLbeta(y, x,
Spline = c("b-spline", "tp-spline", "tpi-spline"),
Knots = NULL,
Degree = 3,
Intercept = FALSE,
Boundary.knots = range(x),
Keep.duplicates = TRUE,
outer.ok = TRUE,
...)NPHalpha(x,
timevar,
Spline = c("b-spline", "tp-spline", "tpi-spline"),
Knots.t = NULL,
Degree.t = 3,
Intercept.t = TRUE,
Boundary.knots.t = c(0, max(timevar)),
Keep.duplicates.t = TRUE,
outer.ok = TRUE,
...)
NLLbeta(x, y, ...) returns y * NLL(x, ...).
NPH(x, timevar, ...) is equal to x * NPHalpha(x, timevar, ...).
the predictor variable.
the time variable.
the name of variable for which tests NLL effect.
type of spline basis. "b-spline" for B-spline basis, "tp-spline" for truncated power basis and "tpi-spline" for monotone (increasing) truncated power basis.
the internal breakpoints that define the spline used to estimate the NLL effect. By default there are none.
degree of splines which are considered.
a logical value indicating whether intercept/first basis of spline should be considered.
range of variable which is analysed.
Should duplicate interior knots be kept or removed. Defaults is FALSE, which removes
duplicate knots with a warning if duplicate interior knots are found.
the internal breakpoints that define the spline used to estimate the NPH effect. By default there are none.
degree of splines which are considered.
a logical value indicating whether intercept/first basis of spline should be considered.
range of time period which is analysed. By default it is c(0, max(timevar)).
Should duplicate interior knots be kept or removed. Defaults is FALSE, which removes duplicate knots with a warning if duplicate interior knots are found.
logical indicating how are managed timevar or x values outside the knots. If FALSE,
return NA, if TRUE, return 0 for the corresponding timevar or x values.
not used
Internal functions.
NPH,
NLL, and
NPHNLL.