Non Log-Linear effect and non proportional effect

Description

Internal functions not inteded for users.

Usage

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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 = range(timevar), 
    Keep.duplicates.t = TRUE, 
    outer.ok = TRUE, 
    ...)

Arguments

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 FALSE, which removes duplicate knots with a warning if duplicate interior knots are found.

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 the range of time variable.

Keep.duplicates.t

Should duplicate interior knots be kept or removed. Defaults is FALSE, which removes duplicate knots with a warning if duplicate interior knots are found.

outer.ok

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

Details

Internal functions.

Value

NLLbeta(x, y, ...) returns y * NLL(x, ...).

NPH(x, timevar, ...) is equal to x * NPHalpha(x, timevar, ...).

See Also

NPH, NLL, and NPHNLL.