predict.se.tps {funfits} | R Documentation |
The predictions are represented as linear combinations of the dependent variables using the function make.Amatrix. Given the estimate for sigma2 and the assumption of uncorrelated errors, the prediction variance associated with the estimate at a particular point is sigma2 multiplied by the sum of the squared linear weights. Note that no adjustment is made for the bias in the spline. This can be substantial and so these standard errors only represent part of the uncertainty in the estimate. For more details refer tot he FUNFITS manual.
predict.se.tps(out, x)
out |
A fitted tps object. |
x |
Matrix of x values on which to calculate the standard errors of predictions of the thin plate spline regression. If omitted, the out$x will be used. |
A vector of standard errors for the predicted values of the thin plate spline regression.
tps, predict.tps
tps(ozone$x,ozone$y) -> fit # tps fit predict.se.tps(fit) # std errors of predictions cbind(seq(87,89,,10),seq(40,42,,10)) -> x # new x matrix predict.se.tps(fit,x) -> out # std errors of predictions tps(BD[,1:4],BD$lnya,scale.type="range") -> fit # fitting a DNA strand # displacement surface to various buffer compositions predict.se.tps(fit) -> out # std erros of predictions