Probability density, distribution, quantile, random generation, hazard,
cumulative hazard, mean and restricted mean functions for the Royston/Parmar
spline model, with one argument per parameter. For the equivalent functions with all parameters collected together in a single argument, see Survspline.
mean_survspline0(
gamma0,
gamma1,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp"
)mean_survspline1(
gamma0,
gamma1,
gamma2,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp"
)
mean_survspline2(
gamma0,
gamma1,
gamma2,
gamma3,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp"
)
mean_survspline3(
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp"
)
mean_survspline4(
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp"
)
mean_survspline5(
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp"
)
mean_survspline6(
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp"
)
mean_survspline7(
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
gamma8,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp"
)
rmst_survspline0(
t,
gamma0,
gamma1,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp",
start = 0
)
rmst_survspline1(
t,
gamma0,
gamma1,
gamma2,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp",
start = 0
)
rmst_survspline2(
t,
gamma0,
gamma1,
gamma2,
gamma3,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp",
start = 0
)
rmst_survspline3(
t,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp",
start = 0
)
rmst_survspline4(
t,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp",
start = 0
)
rmst_survspline5(
t,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp",
start = 0
)
rmst_survspline6(
t,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp",
start = 0
)
rmst_survspline7(
t,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
gamma8,
knots = c(-10, 10),
scale = "hazard",
timescale = "log",
spline = "rp",
start = 0
)
dsurvspline0(
x,
gamma0,
gamma1,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
log = FALSE
)
dsurvspline1(
x,
gamma0,
gamma1,
gamma2,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
log = FALSE
)
dsurvspline2(
x,
gamma0,
gamma1,
gamma2,
gamma3,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
log = FALSE
)
dsurvspline3(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
log = FALSE
)
dsurvspline4(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
log = FALSE
)
dsurvspline5(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
log = FALSE
)
dsurvspline6(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
log = FALSE
)
dsurvspline7(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
gamma8,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
log = FALSE
)
psurvspline0(
q,
gamma0,
gamma1,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
psurvspline1(
q,
gamma0,
gamma1,
gamma2,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
psurvspline2(
q,
gamma0,
gamma1,
gamma2,
gamma3,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
psurvspline3(
q,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
psurvspline4(
q,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
psurvspline5(
q,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
psurvspline6(
q,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
psurvspline7(
q,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
gamma8,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
qsurvspline0(
p,
gamma0,
gamma1,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
qsurvspline1(
p,
gamma0,
gamma1,
gamma2,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
qsurvspline2(
p,
gamma0,
gamma1,
gamma2,
gamma3,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
qsurvspline3(
p,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
qsurvspline4(
p,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
qsurvspline5(
p,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
qsurvspline6(
p,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
qsurvspline7(
p,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
gamma8,
knots,
scale = "hazard",
timescale = "log",
spline = "rp",
lower.tail = TRUE,
log.p = FALSE
)
rsurvspline0(
n,
gamma0,
gamma1,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
rsurvspline1(
n,
gamma0,
gamma1,
gamma2,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
rsurvspline2(
n,
gamma0,
gamma1,
gamma2,
gamma3,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
rsurvspline3(
n,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
rsurvspline4(
n,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
rsurvspline5(
n,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
rsurvspline6(
n,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
rsurvspline7(
n,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
gamma8,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
hsurvspline0(
x,
gamma0,
gamma1,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
hsurvspline1(
x,
gamma0,
gamma1,
gamma2,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
hsurvspline2(
x,
gamma0,
gamma1,
gamma2,
gamma3,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
hsurvspline3(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
hsurvspline4(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
hsurvspline5(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
hsurvspline6(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
hsurvspline7(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
gamma8,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
Hsurvspline0(
x,
gamma0,
gamma1,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
Hsurvspline1(
x,
gamma0,
gamma1,
gamma2,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
Hsurvspline2(
x,
gamma0,
gamma1,
gamma2,
gamma3,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
Hsurvspline3(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
Hsurvspline4(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
Hsurvspline5(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
Hsurvspline6(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
Hsurvspline7(
x,
gamma0,
gamma1,
gamma2,
gamma3,
gamma4,
gamma5,
gamma6,
gamma7,
gamma8,
knots,
scale = "hazard",
timescale = "log",
spline = "rp"
)
Parameters describing the baseline spline function, as
described in flexsurvspline.
Locations of knots on the axis of log time, supplied in
increasing order. Unlike in flexsurvspline, these include
the two boundary knots. If there are no additional knots, the boundary
locations are not used. If there are one or more additional knots, the
boundary knots should be at or beyond the minimum and maximum values of the
log times. In flexsurvspline these are exactly at the
minimum and maximum values.
This may in principle be supplied as a matrix, in the same way as for
gamma, but in most applications the knots will be fixed.
"hazard", "odds", or "normal", as
described in flexsurvspline. With the default of no knots in
addition to the boundaries, this model reduces to the Weibull, log-logistic
and log-normal respectively. The scale must be common to all times.
"log" or "identity" as described in
flexsurvspline.
"rp" to use the natural cubic spline basis
described in Royston and Parmar. "splines2ns" to use the
alternative natural cubic spline basis from the splines2
package (Wang and Yan 2021), which may be better behaved due to
the basis being orthogonal.
Optional left-truncation time or times. The returned restricted mean survival will be conditioned on survival up to this time.
Vector of times.
Return log density or probability.
logical; if TRUE (default), probabilities are \(P(X \le x)\), otherwise, \(P(X > x)\).
Vector of probabilities.
Number of random numbers to simulate.
Christopher Jackson <chris.jackson@mrc-bsu.cam.ac.uk>
These functions go up to 7 spline knots, or 9 parameters.