NLLbeta | R Documentation |
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,
...)
x |
the predictor variable. |
timevar |
the time variable. |
y |
the name of variable for which tests NLL effect. |
Spline |
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. |
Knots |
the internal breakpoints that define the spline used to estimate the NLL effect. By default there are none. |
Degree |
degree of splines which are considered. |
Intercept |
a logical value indicating whether intercept/first basis of spline should be considered. |
Boundary.knots |
range of variable which is analysed. |
Keep.duplicates |
Should duplicate interior knots be kept or removed. Defaults is |
Knots.t |
the internal breakpoints that define the spline used to estimate the NPH effect. By default there are none. |
Degree.t |
degree of splines which are considered. |
Intercept.t |
a logical value indicating whether intercept/first basis of spline should be considered. |
Boundary.knots.t |
range of time period which is analysed. By default it is |
Keep.duplicates.t |
Should duplicate interior knots be kept or removed. Defaults is |
outer.ok |
logical indicating how are managed |
... |
not used |
Internal functions.
NLLbeta(x, y, ...)
returns y * NLL(x, ...)
.
NPH(x, timevar, ...)
is equal to x * NPHalpha(x, timevar, ...)
.
NPH
,
NLL
, and
NPHNLL
.
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