spline

I tried the following: spline= interpolate.InterpolatedUnivariateSpline(X, Y, k=3) coefs= spline.get_coeffs() With five values in each of X and Y , I ended up with coefs also having five values. Given that five data points implies four spline sections, and that a cubic polynomial has four coefficients, I would have expected to get four times four= 16 coefficients. Does anyone know how to interpret the values that are returned by the get_coeffs method? Is there any place where this is documented? These are not the coefficients of x, x**2, and so forth: such monomials are ill-suited to

I've been able to apply a smooth animation to my sprite and control it using the accelerometer. My sprite is fixed to move left and right along the x-aixs. From here, I need to figure out how to create a vertical infinite wavy line for the sprite to attempt to trace. the aim of my game is for the user to control the sprite's left/right movement with the accelerometer in an attempt to trace the never ending wavy line as best they can, whilst the sprite and camera both move in a vertical direction to simulate "moving along the line." It would be ideal if the line was randomly generated. I've

I want to plot the estimated hazard ratio as a function of time in the case of a coxph model with a time-dependent coefficient that is based on a spline term. I created the time-dependent coefficient using function tt , analogous to this example that comes straight from ?coxph : # Fit a time transform model using current age cox = coxph(Surv(time, status) ~ ph.ecog + tt(age), data=lung, tt=function(x,t,...) pspline(x + t/365.25)) Calling survfit(cox) results in an error that survfit does not understand models with a tt term ( as described in 2011 by Terry Therneau ). You can extract the linear

I feel like this is an easy question... How do you identify coordinates in a figure? I plotted some data, used unireg (the uniReg package) to make a spline curve, and want to pull out the data from a point. library(uniReg) P0mM <- read.table(text=" Time FeuM 0.04 138.8181818 7 1258.636364 14 1320.545455 21 2110.37037 28 13730.37037 35 1550.909091",header=TRUE) z=seq(min(P0mM$Time),max(P0mM$Time),length=201) uf=with(P0mM,unireg(Time,FeuM,g=5,sigma=1)) plot(FeuM~Time,P0mM,ylim=c(0,16000),ylab="Fe2+ uM", xlab="Time", main="0mM P") lines(z,uf$unimod.func(z)) I was able to find the max y value of

I have a list of 2d points which are the control vertices (Dx) for a closed uniform cubic B-spline. I am assuming a simple curve (non-self-intersecting, all control points are distinct). I am trying to find the area enclosed by the curve: If I calculate the knot points (Px), I can treat the curve as a polygon; then I "just" need to find the remaining delta areas, for each segment, between the actual curve and the straight line joining the knot points. I understand that the shape (and therefore area) of a Bspline is invariant under rotation and translation - so for each segment, I can find a

I realise that there are posts on the topic of B-Splines on this board but those have actually made me more confused so I thought someone might be able to help me. I have simulated data for x-values ranging from 0 to 1. I'd like to fit to my data a cubic spline ( degree = 3 ) with knots at 0, 0.1, 0.2, ... , 0.9, 1. I'd also like to use the B-Spline basis and OLS for parameter estimation (I'm not looking for penalised splines). I think I need the bs function from the spline package but I'm not quite sure and I also don't know what exactly to feed it. I'd also like to plot the resulting