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####

Standard errors in gam(), spm(), geoR in R

- The standard errors produced as the
`se.fit` output component
from `predict.gam()` are standard errors for the function estimate,
not prediction standard errors. I.e., the noise uncertainty (variability
in observations about the function) is not part of the standard errors.

- Also note that the output of
`predict.gam()` is an array. You
can freeze R if you try to use this output as an input to `lm()`.
Instead use the numeric version of the output, e.g., `c(predict.gam(mod))`.

- The standard errors produced by the
`predict.spm()` function
in SemiPar are also just standard errors for the function estimate
and not prediction standard errors.
- The standard errors produced by kriging in the geoR package and output
as the
`$krige.var` list item (i.e. as variances, the square
of the standard error) are prediction standard errors, but function
standard errors can be obtained using `output=output.control(signal=TRUE))`
as an arigument to `krige.conv()`.
- The standard errors produced by kriging in the geoR package and output
as the list item (i.e. as variances, the square of the standard error)
also appear to be just standard errors for the function uncertainty.

Keywords: gam, mgcv, geoR, R, standard errors, predict.gam, prediction,
predict.spm, krige.var, kriging
Last modified 12/22/06.

** Next:** Using gamm()
** Up:** Spatial and spatio-temporal data
** Previous:** Universal kriging and generalized
Chris Paciorek
2012-01-21