Thin Plate Spline for 3D surface prediction in R -


i tried answer

get surface plot in r

but hasn't helped. perform tps (using tps fields{}) on xyz dataframe xy co-orinates , z thickness. visualise plot firstly before tps , after tps..? possible. extract predicted thicknesses given set of new xy co-ordinates..?

please let me know if possible

my dataframe looks this, dataframe called lsp:

time   part   meas    partsub   xloc   yloc xxxx   1      1.956           -3465  -94350 xxxx   1      1.962           -3465  -53850 xxxx   1      1.951           50435  -40350 xxxx   1      1.958           -57365 -40350

so 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 null..?

so attempting plot gives me:

error in nrow(z) : argument "z" missing, no default

any appreciated. paul.

predict.surface obsolete / deprecated function. use predictsurface instead.

fit<- tps( bd[,1:4], bd$lnya)  # fit surface data   # evaluate fitted surface  first 2  # variables holding other 2 fixed @ median values  out.p<- predictsurface(fit) surface(out.p, type="c")

enter image description here

thanks - how second question....how can extract predicted surface thickness values given set of xy locations..?

use predict function. have read on ?predict.tps. above example, let's want predict @ first 4 locations in bd[, 1:4], 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 two-column matrix.


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