you could try this approach (use calc whenever you can):
(supposing your bricks have 12 layers)
br3 <- stack(brick1, brick2)
lmS <- function(x) lm(x[1:12] ~ x[13:24)$coefficients[2]
r <- calc(br3, lmS)
Jacob.
--- On Fri, 26/11/10, steven mosher <mosherste...@gmail.com> wrote:
From: steven mosher <mosherste...@gmail.com>
Subject: Re: [R-sig-Geo] gridded time series analysis
To: "Martin" <martin_bra...@gmx.net>
Cc: r-sig-geo@stat.math.ethz.ch
Date: Friday, 26 November, 2010, 23:33
that's cool, I'm also interested in a similar problem but just with one
brick ending up with a slope raster as the output. It may be possible with
stackApply(). have a look. or maybe robert will chime in
On Fri, Nov 26, 2010 at 1:35 PM, Martin <martin_bra...@gmx.net> wrote:
>
> this is what I did to perform a regression between two bricks (each brick
> represent a time series):
>
> r <- raster(brick1)
> for (i in 1:ncell(r)) {
> r[i] = lm(as.ts(cellValues(brick1, i)) ~ as.ts(cellValues(brick2,
> i)))$coefficients[2]
> }
>
> The result will be a slope raster, but it really takes a lot of time, so
> maybe there is a better solution..
>
>
> --
> View this message in context:
> http://r-sig-geo.2731867.n2.nabble.com/gridded-time-series-analysis-tp5775651p5778472.html
> Sent from the R-sig-geo mailing list archive at Nabble.com.
>
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