Make natural splines constant outside boundary

Hi,

I'm using natural cubic splines from splines::ns() in survival 
regression (regressing inter-arrival times of patients to a queue on 
queue size). The queue size fluctuates between 3600 and 3900.

I would like to be able to run predict.survreg() for sizes <3600 and 

 >3900 by assuming that the rate for <3600 is the same as for 3600 and 

that for >4000 it's the same as for 4000 (i.e., keep the splines cubic 
within the boundaries but make them constant outside the boundaries).

Any suggestions?

range(ovarian$age)

trim <- function(x) pmin(74.5041 ,pmax(38.8932 , x))
s <- survreg(Surv(futime, fustat) ~ ns(age, knots=c(50,

s2 <- survreg(Surv(futime, fustat) ~ ns(trim(age), knots=c(50,

matplot(newage, cbind(predict(s,newdata=newage),predict(s2,newdata=newage)))

Thanks,
Gad

Gad Abraham <g.abraham <at> ms.unimelb.edu.au> writes:

Hi,

I'm using natural cubic splines from splines::ns() in survival
regression (regressing inter-arrival times of patients to a queue on
queue size). The queue size fluctuates between 3600 and 3900.

I would like to be able to run predict.survreg() for sizes <3600 and

3900 by assuming that the rate for <3600 is the same as for 3600 and

that for >4000 it's the same as for 4000 (i.e., keep the splines cubic
within the boundaries but make them constant outside the boundaries).

[snip]

Any suggestions?

Here is one.

range(ovarian$age)

[1] 38.8932 74.5041

trim <- function(x) pmin(74.5041 ,pmax(38.8932 , x))
s <- survreg(Surv(futime, fustat) ~ ns(age, knots=c(50,

60),Boundary.knots=c(38.8932, 74.5041)),data=ovarian)

s2 <- survreg(Surv(futime, fustat) ~ ns(trim(age), knots=c(50,

60),Boundary.knots=c(38.8932, 74.5041)),data=ovarian)

matplot(newage, cbind(predict(s,newdata=newage),predict(s2,newdata=newage)))

On Sat, 8 Dec 2007, Charles C. Berry wrote:

Gad Abraham <g.abraham <at> ms.unimelb.edu.au> writes:

Hi,

I'm using natural cubic splines from splines::ns() in survival
regression (regressing inter-arrival times of patients to a queue on
queue size). The queue size fluctuates between 3600 and 3900.

I would like to be able to run predict.survreg() for sizes <3600 and

3900 by assuming that the rate for <3600 is the same as for 3600 and

that for >4000 it's the same as for 4000 (i.e., keep the splines cubic
within the boundaries but make them constant outside the boundaries).

[snip]

Any suggestions?

Here is one.

range(ovarian$age)

[1] 38.8932 74.5041

trim <- function(x) pmin(74.5041 ,pmax(38.8932 , x))
s <- survreg(Surv(futime, fustat) ~ ns(age, knots=c(50,

60),Boundary.knots=c(38.8932, 74.5041)),data=ovarian)

s2 <- survreg(Surv(futime, fustat) ~ ns(trim(age), knots=c(50,

60),Boundary.knots=c(38.8932, 74.5041)),data=ovarian)

Should have copy-and-pasted this here:

newage <- data.frame( age=seq(10,200,10 ) )

matplot(newage, cbind(predict(s,newdata=newage),predict(s2,newdata=newage)))