Hi Jeff,
thanks, the raster package disaggregate will do the trick as well.
library(raster)
rmm <- raster(ncols=5, nrows=3)
rmm[] <- matrix(1:15,nrow=3,byrow = T)
xrmm <- disaggregate(rmm, fact=c(3, 3))
> > as.matrix(rmm)
> [,1] [,2] [,3] [,4] [,5]
> [1,]12345
> [2,]6
You probably ought to be using the raster package. See the CRAN Spatial Task
View.
--
Sent from my phone. Please excuse my brevity.
On July 5, 2017 12:20:28 AM PDT, "Anthoni, Peter (IMK)"
wrote:
>Hi all,
>(if me email goes out as html, than my email client don't do as told,
>and I apologies al
Hi Jim,
thanks that works like a charm.
cheers
Peter
> On 5. Jul 2017, at 12:01, Jim Lemon wrote:
>
> Hi Peter,
>
> apply(t(apply(mm,1,rep,each=3)),2,rep,each=3)
>
> Jim
>
> On Wed, Jul 5, 2017 at 5:20 PM, Anthoni, Peter (IMK)
> wrote:
>> Hi all,
>> (if me email goes out as html, than
Hi Peter,
apply(t(apply(mm,1,rep,each=3)),2,rep,each=3)
Jim
On Wed, Jul 5, 2017 at 5:20 PM, Anthoni, Peter (IMK)
wrote:
> Hi all,
> (if me email goes out as html, than my email client don't do as told, and I
> apologies already.)
>
> We need to downscale climate data and therefore first need t
Hi all,
(if me email goes out as html, than my email client don't do as told, and I
apologies already.)
We need to downscale climate data and therefore first need to expand the
climate from 0.5deg to the higher resolution 10min, before we can add high
resolution deviations. We basically need to
5 matches
Mail list logo