Plot a M-spline function, showing how it is built up from its basis
Source:R/mspline_plots.R
plot_mspline.Rd
See mspline_plotdata
for the data behind the plot
Usage
plot_mspline(
knots = NULL,
bknot = 10,
df = 10,
degree = 3,
bsmooth = TRUE,
coefs = NULL,
scale = 1,
tmin = 0,
tmax = 10,
show_knots = FALSE,
show_means = FALSE
)
Arguments
- knots
Vector of knot locations. If not supplied,
df
has to be specified. One of two rules is then used to choose the knot locations. Ifbknot
is specified, a set of equally spaced knots between zero andbknot
is used. Otherwise ifobstimes
is supplied, the knots are chosen as equally spaced quantiles ofobstimes
.The number of knots (excluding zero) is
df - degree + 1
ifbsmooth
isTRUE
, ordf - degree - 1
otherwise.- bknot
Location of the final spline knot.
- df
Desired number of basis terms, or "degrees of freedom" in the spline. If
knots
is not supplied, the number of knots is then chosen to satisfy this.- degree
Spline polynomial degree. Can only be changed from the default of 3 if
bsmooth
isFALSE
.- bsmooth
If
TRUE
then the function is constrained to also have zero derivative and second derivative at the boundary.- coefs
Coefficients of the spline basis terms. These are normalised internally to sum to 1, if they do not already sum to 1.
- scale
Scale parameter. After computing the standard M-spline function as a weighted sum of the basis terms, the function is multiplied by
scale
. The log of the scale is the parameter calledalpha
in the results of asurvextrap
model, the intercept of the linear model on the log hazard.- tmin
Minimum plotting time. Defaults to zero.
- tmax
Maximum plotting time. Defaults to the highest knot.
- show_knots
Show the positions of the knots, including the upper boundary
- show_means
Show the "mean" around which each basis term is centred (defined as the mean of a random variable whose PDF is defined by the basis term).