Thin Plate Spline for 3D surface prediction in R

Question

I tried this answer

get a surface plot in R

but it hasn't really helped. I would like to perform a TPS (using Tps from Fields{}) on an XYZ dataframe where xy are co-orinates and z is a thickness. Then I would like to visualise the plot firstly before TPS and then after TPS..? Is this possible. Then I would like to extract predicted thicknesses for a given set of new xy co-ordinates..?

Please let me know if this is possible

My Dataframe looks like this, dataframe is called LSP:

time   PART   MEAS    PARTSUB   XLOC   YLOC
xxxx   1      1.956   a         -3465  -94350
xxxx   1      1.962   a         -3465  -53850
xxxx   1      1.951   a         50435  -40350
xxxx   1      1.958   a         -57365 -40350

So I tried this:

LSP.spline <- Tps(LSP[,5:6], LSP$MEAS)
out.p <- predict.surface(LSP.spline, xy = c(1,2))
plot.surface(out.p, type="p")

But out.p is just NULL..?

so attempting the plot gives me:

Error in nrow(z) : argument "z" is missing, with no default

Any help is appreciated. Paul.

Answer 1

predict.surface is now an obsolete / deprecated function. Use predictSurface instead.

fit<- Tps( BD[,1:4], BD$lnya)  # fit surface to data 

# evaluate fitted surface for  first two 
# variables holding other two fixed at median values

out.p<- predictSurface(fit)
surface(out.p, type="C") 

Thanks for that - how about my second question....how can I extract predicted surface thickness values for a given set of XY locations..?

Use predict function. Have a read on ?predict.Tps. For the above example, let's say we want to predict at the first 4 locations in BD[, 1:4], we can do

predict(fit, x = BD[1:4, 1:4])

#          [,1]
#[1,] 11.804124
#[2,] 11.804124
#[3,]  8.069056
#[4,]  9.501551

In general, pass x a two-column matrix.