Fit a Thin-plate splines model to a set of observations

Fit a Thin-plate splines model to a set of observations

fit_surface(x)

Arguments

x	A sr_obs object defined with sr_obs.

Value

An object of class Krig and Tps which includes the fitted values and the model residuals. See Tps. It also allows interpolation using interpolate.

Examples

d <- data.frame(lon = runif(30), lat = runif(30))
d <- transform(d, date = round(10 * sqrt(lon**2 + lat**2)))
sro <- sr_obs(d, "date")
fm <- fit_surface(sro)
summary(fm)
#> CALL:
#> Tps(x = st_coordinates(x), Y = x[[attr(x, "timevar")]], give.warning = TRUE)
#>                                                 
#>  Number of Observations:                30      
#>  Number of unique points:               30      
#>  Number of parameters in the null space 3       
#>  Parameters for fixed spatial drift     3       
#>  Effective degrees of freedom:          10.4    
#>  Residual degrees of freedom:           19.6    
#>  MLE sigma                              0.3211  
#>  GCV sigma                              0.2881  
#>  MLE rho                                16.53   
#>  Scale passed for covariance (rho)      <NA>    
#>  Scale passed for nugget (sigma^2)      <NA>    
#>  Smoothing parameter lambda             0.006237
#> 
#> Residual Summary:
#>      min    1st Q   median    3rd Q      max 
#> -0.46170 -0.15790  0.04453  0.14390  0.41520 
#> 
#> Covariance Model: Rad.cov
#>   Names of non-default covariance arguments: 
#>        p
#> 
#> DETAILS ON SMOOTHING PARAMETER:
#>  Method used:   GCV    Cost:  1
#>    lambda       trA       GCV   GCV.one GCV.model      shat 
#>  0.006237 10.352214  0.126753  0.126753        NA  0.288122 
#> 
#>  Summary of all estimates found for lambda
#>              lambda   trA    GCV   shat -lnLike Prof converge
#> GCV        0.006237 10.35 0.1268 0.2881        15.01        1
#> GCV.model        NA    NA     NA     NA           NA       NA
#> GCV.one    0.006237 10.35 0.1268 0.2881           NA        1
#> RMSE             NA    NA     NA     NA           NA       NA
#> pure error       NA    NA     NA     NA           NA       NA
#> REML       0.002306 14.09 0.1394 0.2719        13.67        3
plot(fm)    # Some model diagnostics plots
fields::surface(fm)  # Quick image/contour plot of predicted surface.