Nikos:
I finally ran mrpp tests. I think all is fine but one very important
issue: I
have no idea how to export/save an mrpp object. Tried anything I
know and
searched the archives but found nothing.
David W:
And what happened when you tried what seems like the obvious:
Gavin:
[...]
I think you should read ?load to rethink what these functions do and how
they do it.
[...]
Absolutely correct. I will. Thank you for your time Gavin, Nikos
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Greets (again) :-)
I finally ran mrpp tests. I think all is fine but one very important issue: I
have no idea how to export/save an mrpp object. Tried anything I know and
searched the archives but found nothing.
Any ideas? Is really copy-pasting the mrpp results the only way?
Thank you for
On Wednesday 22 of December 2010 05:57:17 Nikos Alexandris wrote:
[...]
Apologies for repeating the same question (trying to understand the problem
myself).
I started to get a grip on this. But anyway, my questions are actually not
directly about R questions - sorry for the traffic
This time with a more-R oriented question:
Is the mrpp {vegan} package [1] useful in trying to check, or get a clue about
the differences between- and within-axes (or variables or dimensions or
columns) of a multivariate matrix?
The description explains:
...(MRPP) provides a test of whether
Hi!
My question(s) in the end might be silly but I am no expert on this, so here
it goes:
Noy-Meir (1973), Pielou (1984) and a few others have pointed to non-centered
PCA being in some cases useful. They clearly explain that it is the case
when multi-dimensional data display distinct clusters
Hi!
My question(s) in the end might be silly but I am no expert on this, so here
it goes:
Noy-Meir (1973), Pielou (1984) and a few others have pointed to non-centered
PCA being in some cases useful. They clearly explain that it is the case
when multi-dimensional data display distinct clusters
On Wednesday 30 of June 2010 23:02:09 afso...@unisinos.br wrote:
Hi all,
I am using the vegan package to run a prcincipal components analysis
on forest structural variables (tree density, basal area, average
height, regeneration density) in R.
However, I could not find out how to
Christofer Bogaso wrote:
Dear all, I am looking for some interactive study materials on Principal
component analysis. Basically I would like to know what we are actually
doing with PCA?
Having in mind the eigenvalue decomposition and a bivariate data set, the sum-
it-all in a few sentences I
Nikos Alexandris:
Among the (R-)tools, I've seen on the net, for (bivariate) Principal
Component scatter plots (+histograms), plotpc [1] is the one I like
most.
[...]
I started the modification by attempting first to get a prcomp version of
plotpc() (named it plotpc.svd()) by altering
Peter Ehlers wrote:
Nikos,
I think you can just replace the line
pc - princomp(x[,1:2], scores=TRUE, na.action=na.fail)
with
pc - prcomp(x[,1:2], retx=TRUE, center=pc.center,
scale.=pc.scale, na.action=na.fail)
and rename the components of pc
Peter Ehlers wrote:
and then use the rest of the plotpc() code as is (except for
maybe having to use flip1=TRUE, etc).
Nikos:
Hmm... I am _now_ working on it to understand how I could make this
automatic!.
If I give flip1, flip2 (=TRUE) the histograms are located where they should
Dear R-list,
Among the (R-)tools, I've seen on the net, for (bivariate) Principal Component
scatter plots (+histograms), plotpc [1] is the one I like most.
By default it performs PCA on a bivariate dataset based on R's princomp()
(which is the eigenvector-based algebraic solution to PCA). I
On Saturday 15 of May 2010 19:09:30 Nikos Alexandris wrote:
Among the (R-)tools, I've seen on the net, for (bivariate) Principal
Component scatter plots (+histograms), plotpc [1] is the one I like
most.
[...]
[1] http://cran.r-project.org/web/packages/plotpc/index.html
data.frames with identical structure
# by Nikos Alexandris, Freiburg, April 2010
# Code --
separability.measures.class - function (
Data.Frame.1
# --- Code ---
# Custom function for various Separability Measures
# by Nikos Alexandris, Freiburg, 8.04.2010
# ( based on Divergence and Jeffries-Matusita, requires input variables
as.matrices )
separability.measures - function
On Thu, 2010-03-25 at 11:54 -0800, Jeff Brown wrote:
Wow, you guys are awesome. Thanks!
Thanks for the cat() question Jeff and to all guRus out there for the
replies. This is something I was looking for the last hour.
Nikos
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Jeff Brown wrote:
Wow, you guys are awesome. Thanks!
Nikos Alexandris wrote:
Thanks for the cat() question Jeff and to all guRus out there for the
replies. This is something I was looking for the last hour.
I can't seem to make this run:
I have a function ( Column.of.Matrix.1
On Tue, 2010-04-06 at 19:29 +0200, Nikos Alexandris wrote:
Jeff Brown wrote:
Wow, you guys are awesome. Thanks!
Nikos Alexandris wrote:
Thanks for the cat() question Jeff and to all guRus out there for the
replies. This is something I was looking for the last hour.
I can't seem
Nikos Alexandris wrote:
...
I have 6 data frames consisting of 6 rows x 7 columns put together from
other data.frames.
...
I want to give the following column names to each data.frame: (SDev,
PC1, PC2, PC3, PC4, PC5, PC6)
...
How is it to be done at once for all data.frames
Thanks to all for the replies. I'll post back here when I get things
working (...in a couple of days).
Nikos
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PLEASE do read the posting guide
Greets to the list!
I am aware that this topic has been discussed several times. And I've
read quite some related posts [1]. Yet, can't seem to give a solution to
my problem.
I have 6 data frames consisting of 6 rows x 7 columns put together from
other data.frames.
Something like:
a b c d e
[Answering to the threads question]
For those who use Gnome's gedit, there is now RGedit:
http://sourceforge.net/projects/rgedit
Apologies if this was already posted,
Nikos
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On Tue, 2009-08-04 at 20:16 -0700, Mohan S wrote:
HI
am plotting different density plots in one graph each with a different
color.
And i want to add labels to plot mentioning which color belongs to which
data series.
p2 - qplot(corArms, data = data1, geom = density, adjust=0.4,
On Wed, 2009-08-05 at 13:16 -0700, Steve Jaffe wrote:
Why when I assign 0 to an element of an integer vector does the type change
to numeric?
Here is a particularly perplexing example:
v - 0:10
v
[1] 0 1 2 3 4 5 6 7 8 9 10
class(v)
[1] integer
v[1] - 0
try this:
v -
On Thu, 2009-07-02 at 00:01 -0500, Gene Leynes wrote:
playwith(xyplot(z3), time.mode = TRUE)
WoW! Looks (and is) GrEaT!
Nikos
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PLEASE do read the posting guide
megh:
I want to create a number of vectors like :
vec1 - rnorm(1)
vec2 - rnorm(2)
vec3 - rnorm(3)
and so on...
Maybe try the assign() function. Something like:
for (i in 1:10) assign ( paste ( vec , i , sep = ) , rnorm(i) )
Kind regards, Nikos
Hi R-specialists!
I would like to draw some hyperbolic (iso-)lines of a cost function in a
bi-dimensional space ( =shapes of a cost function ), based on (the
general form):
C(x) = c1*Ce + c2*Oe + c3*{ 1 - [ (1 - Ce)^a * (1 - Oe)^b ] }
where:
- Oe/Ce are Omission/Commission (ranging between 0
Maayt:
I just imported two raster maps into R using the SPGRASS6 package, one
containing elevation data and the other containing an erosion index:
Kar_inc -readRAST6(Incis_Kar, plugin=FALSE)
Kar_dem - readRAST6(DEM_Kar, plugin=FALSE)
I just wanted to make a xy plot of erosion parameter vs
Hi R-list.
This is my first post. I'll try to be as precise as possible with the
difficulty I have to get things done.
I have a hard time trying to construct a double for loop and create
within the inner loop new objects (in this case vectors).
I posted this question in a non-directly related
jim holtman:
You might want to look at how to use 'lapply' to create lists. Here
is one way of doing it:
# create test data
a_threshold - b_threshold - as.data.frame(matrix(sample(c(1:5,
NA), 100, TRUE), 10))
classification - c('a', 'b')
result - lapply(classification,
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