Hi,
I would like to fit the following model with quantile regression:
y ~ alpha + beta
where both alpha and beta are factors. The conceptual model I have in my
head is that alpha is a constant set of values, that should be independent
of the quantile, tau and that all of the variability arises d
Hi,
I seem to be having a problem adding the following two raster objects
together - one is a rasterLayer, the other is a rasterBrick. The extent,
resolution, and origin are the same, so according to my understand it
should work. The objects look like so:
> obs.clim
class : RasterLayer
dime
Dear R-help,
I have a problem where I am using the mgcv package to in a situation where
I am fitting a gam model with a 1-D spline smoother model over a domain
[a,b] but then need to make predictions and extrapolate beyond b. Is there
anyway where I force the first derivative of the spline to be z
fit,lty=2)
>
> lines(pd$x,ff$fit-2*ff$se.fit,lty=2)
>
>
> On 08/11/2018 15:26, Mark R Payne wrote:
> > Dear R-help,
> >
> > I have a problem where I am using the mgcv package to in a situation
> where
> > I am fitting a gam model with a 1-D spline s
Hi,
I am trying to use stackApply() to perform averages over subsets of a
brick. However, I am struggling with the indices argument, and how it
should be interpreted. Here is a simple working example illustrating my
problem:
r <- raster()
r[] <- 1
inp <- brick(r,r,r,r,r,r)*(1:6)
res <- stackAppl
Thanks for the reply Don - that's the problem in a nutshell - I'll
repost on r-sig-geo
Mark
On 10/12/15 20:46, MacQueen, Don wrote:
Appears to me that results for the third set of indices you supplied (1,1)
ended up in the third layer of the result. Similarly for the other sets of
indices.
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