Dear Roger,
thanks for pointing out
> This will only generate compliant behaviour when rgdal is loaded,
> converting
> +init=epsg:4326 to +init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs
> +ellps=WGS84 +towgs84=0,0,0, so that is.projected(spdf) is FALSE
> which was the intention - either
I noticed that dnearneigh::spdep shows diverging behaviour with matrix and
SpatialPoints objects respectively:
reproducible example:
library(spdep)
set.seed(5)
spdf<-SpatialPointsDataFrame(cbind(lon=runif(1000,2,8),lat=runif(1000,53,56)),
data=data.frame(par=runif(1000,0,1)),coords.nrs =
Juliet,
for you the diagnostic plots:
just to recall:
the first model was this:
fit<-gam(target
~s(mgs)+s(gsd)+s(mud)+s(ssCmax),family=quasi(link=log),data=wspe1,method="REML",select=F)
> summary(fit)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
Simon,
thanks for the reply, I guess I'm pretty much up to date using
mgcv 1.7-22.
Upgrading to R 3.0.0 also didn't do any change.
Unfortunately using method="REML" does not make any difference:
### first with "select=FALSE"
> fit<-gam(target
> ~s(mgs)+s(gsd)+s(mud)+s(ssCmax),family=quasi(
I have 11 possible predictor variables and use them to model quite a few
target variables.
In search for a consistent manner and possibly non-manual manner to identify
the significant predictor vars out of the eleven I thought the option
"select=T" might do.
Example: (here only 4 pedictors)
firs
5 matches
Mail list logo
