On Thu, 4 Aug 2011, Rebecca Bennett wrote:
Great, this worked, thank you for your quick reply Roger. The missing values
are intended - I didn't realise that I had to specify such to R.
This probably going to show my vast ignorance of R in comparison to SPSS, but
is there a way to tell / set the significance level for the analysis?
?cor.test
but beware that your observations are not independent, and will have inflated
significance levels, so the test output really should not be used unless you
can first show that there is no spatial autocorrelation in either variable. You
could look at ?Geary in the raster package as a way of checking
autocorrelation. If you are an ecologist, you could look at Fortin & Dale for
background, and maybe the Numerical ecology with R useR! book from Springer.
The Moran() and Geary() functions in raster don't give you a significance test
for autocorrelation, though, you may need other approaches in spdep for that.
Hope this helps,
Roger
cheers,
Rebecca
________________________________
From: Roger Bivand <[email protected]>
To: Rebecca Bennett <[email protected]>
Cc: "[email protected]" <[email protected]>
Sent: Thursday, 4 August 2011, 10:41
Subject: Re: [GRASS-stats] Correlation calculation
On Thu, 4 Aug 2011, Rebecca Bennett wrote:
Hello all,
I am *very* new to R and am experimenting with the GRASS / R interface to
look at correlation of elevation and spectral values.
Reading through the literature and mailing list, I have imported my GRASS
rasters to R as a dataset looked at the summary statistics and then
attempted to run a correlation analysis using cor, along the lines of the
example here:
map <- readRAST6(c("lageado_10k_fisher_K_15x15",
"lageado_10k_fisher_K_25x25")) summary(map)
I think that here you will see that for your current GRASS region, there are
missing values (NAs) in the first attribute.
cor(as(map, "data.frame"))
and get the output:
coombe_rev_LRM intensity_coombe x y
coombe_rev_LRM 1 NA NA NA
intensity_coombe NA 1.00000000 -0.1525509 0.09057913
x NA -0.15255095 1.0000000 0.00000000
y NA 0.09057913 0.0000000 1.00000000
so results for intensity_coombe are given, but not for coombe_rev_LRM. The x
and y are the coordinates of the raster cells are uninteresting, and are
added when you use as(..., "data.frame"). Try:
cor(as(map, "data.frame")[,1:2], use="complete.obs")
to use only the two real attributes, and to drop observations where there
are missing values. Do check that the missing values are intended, as they
may be caused by your region setting in GRASS.
Hope this helps,
Roger
so the correlation is running on the x and y values of the rasters not the
z?
How do I begin to assess correlation in the z value of two rasters?
Many thanks for reading, (and sorry if my question seems stupid or I have
missed something obvious)
Rebecca
-- Roger Bivand
Department of Economics, NHH Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: [email protected]
--
Roger Bivand
Department of Economics, NHH Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: [email protected]
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