X is a 5000 x 5 matrix (numerical variables)
k = 6
My desktop machine is a Xeon-processor Dell Precision T3500.
[Ubuntu 12.04.4 LTS (GNU/Linux 3.2.0-58-generic x86_64) with 24 GB RAM]
[R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computin
On 27/09/13 00:33, Duncan Murdoch wrote:
On 25/09/2013 11:38 PM, Ben Harrison wrote:
Hello,
It's been the way it is for about 14 years, and I don't recall anyone
else complaining, so I'd conclude it must have been set that way with
you in mind.
Ah-hah, I knew it! I *am* s
On 27/09/13 00:03, Ista Zahn wrote:
Hi Ben,
On Wed, Sep 25, 2013 at 11:38 PM, Ben Harrison
wrote:
Hello,
I agree that it would be nicer to have the whole TOC entry
hyperlinked. But out of curiosity, why are you using the pdf
documentation? I find the html much nicer.
Best,
Ista
It&#
Hello,
I am mildly annoyed each time I use a PDF doc of an R package that the
table of contents hyperlinks are *only* on the page numbers. To activate
a hyperlink, one must carefully scan sideways from the text item wanted
to the far right of the page and click on a tiny box. Multiply that mild
On 10/09/13 06:45, sewal...@umn.edu wrote:
Please advise
Can't help you in Minneapolis, though surely your university has a
statistics department?
To learn R there are many online tutorials and video guides. The latest
is a large collection from the Google developers:
http://www.youtube.c
If I were Michael (OP) right now, I think my head would be spinning.
As a newbie myself, I know how hard it is to read R code for the first
time, so could it also be part of the newsgroup etiquette to at least
partially explain provided code to newbies?
I agree that the interactive help '?' i
n respect of Duncan and Peter's solutions, I have found that R can be a
troublesome pet at times, and this seems like one of them.
Thanks for the help and warnings!
Ben.
On 9 September 2013 00:39, peter dalgaard wrote:
>
> On Sep 8, 2013, at 16:09 , Duncan Murdoch wrote:
>
> &
Hello,
I wish to create a copy of a data frame, but with missing values replaced
with NAs.
I thought I should be able to do it in one step using parentheses to group
the statements and force those inside the parens to execute first:
df <- (BWS6[BWS6 < -998] <- NA)
But all this does is assign NA
Hello, I am quite a novice when it comes to predictive modelling, so
would like to see where my particular problem might lie in the spectrum
of problems that you collectively have seen in your experiences.
Background: I have been handed a piece of software that uses a kohonen
SOM network to an
On 22/08/13 21:57, Michael Weylandt wrote:
On Aug 22, 2013, at 7:39, Ben Harrison wrote:
No idea about the problem specifics but what are your OS and version of R? You
might be limited there.
I have 64-bit Ubuntu 12.04, R version 3.0.1.
More likely, however, is that your problem is
I have a 70363 x 5 double matrix that I am playing with.
> head(df)
GR SP SN LN NEUT
1 1.458543 1.419946 -0.2928088 -0.2615358 -0.5565227
2 1.432041 1.418573 -0.2942713 -0.2634204 -0.5927334
3 1.406642 1.418226 -0.2958296 -0.2652920 -0.6267121
4 1.382284 1.4188
If I am to do a lot of pre-processing operations on some data,
comprising many steps such as:
-- despike a set of signals
-- smooth a set of signals
-- filter
-- subset
-- impute missing values
etc... Should I be assigning the result of each operation to a new
object, and then destroying the old
On 30 July 2013 21:35, Rui Barradas wrote:
> Hello,
>
> Maybe the following does it.
>
> op <- par(mfrow=c(2, 3))
>
> for(i in 1:6){
> plot(somdata.xyf,
> type="property",
> property=somdata.xyf$codes$X[, i],
> main=colnames(somdata.xyf$codes$X)[i])
>
I have an xyf object from the kohonen package, and wish to plot a
lattice or grid or multiplot of a set of attributes of this object.
I've included the structure of the object below for reference, and
here is the set of plots I wish to produce, given in long-hand. I
don't know enough R to generali
For supervised version of the kohonen SOM (xyf), I wish to train a
map, and then predict a property from the trained map. For the
function xyf, whose basic call is:
xyf(data, Y, grid)
should the data argument contain the Y property? Or does it need to be excluded?
e.g.:
> head(somdata)
MEAS_T
On 24 July 2013 21:32, Ben Harrison wrote:
> On 24 July 2013 19:25, ONKELINX, Thierry wrote:
>> Try rescaling your data prior to splitting it up into a training and test
>> set. Otherwise you end up with two different ways of scaling.
> I still cannot understand how I can s
On 24 July 2013 19:25, ONKELINX, Thierry wrote:
> Try rescaling your data prior to splitting it up into a training and test
> set. Otherwise you end up with two different ways of scaling.
>
I was mistaken, there is a visible improvement!
I still cannot understand how I can sensibly revert the s
On 24 July 2013 19:25, ONKELINX, Thierry wrote:
> Try rescaling your data prior to splitting it up into a training and test
> set. Otherwise you end up with two different ways of scaling.
Thanks, good point.
I have adjusted the code, however with no visible improvement.
Also, I want to be able
I would really like some or any advice on how I can improve (or fix??)
the following analysis. I hope I have provided a completely runnable
code - it doesn't produce any errors for me.
The resulting plot at the end shows a pretty poor correlation (just
speaking visually here) to the test set. How
Trying re-send as plain text.
I have a data set with 10 variables, and about 8000 instances (or
objects/rows/samples). In addition I have one more ('class') variable
that I have about 10 instances for, but for which I wish to impute
values for.
I am a little confused how to go about doing this, m
I have a data set with 10 variables, and about 8000 instances (or
objects/rows/samples). In addition I have one more ('class') variable that
I have about 10 instances for, but for which I wish to impute values for.
I am a little confused how to go about doing this, mostly as I'm not
well-versed in
On 28 September 2012 16:38, David Winsemius wrote:
>
> ?text # should be fairly clear.
Thank you. I was stupid to ask such a trivial question along with a
not-so-trivial one. The second part of the question was probably more
important: is there a way to obtain the location of segments produced
b
Hello,
I have produced some segmented regressions with the segmented package by
Viggo Mutteo. I have some example data and code below. I want to annotate
the individual segments with the slope parameter (actually it would be
nicer to annotate with 1000*slope and add some small amount of text as
we
Thank you Santosh.
I am so new to R that I don't even have droplevels:
1> tcc2 = droplevels(tcc)
Error: could not find function "droplevels"
I looked up ?droplevels:
1> ?droplevels
No documentation for 'droplevels' in specified packages and libraries:
you could try '??droplevels'
1> ??droplevels
Thank you Santosh.
I am so new to R that I don't even have droplevels:
1> tcc2 = droplevels(tcc)
Error: could not find function "droplevels"
I looked up ?droplevels:
1> ?droplevels
No documentation for 'droplevels' in specified packages and libraries:
you could try '??droplevels'
1> ??droplevels
I am having difficulty understanding how I would constrain a data set by
filtering out 'records' based on certain criteria.
Using SQL I could query using 'select * from my.data where LithClass in
('sand', 'clay')' or some such.
Using subset, there seem to be ghosts left behind (that is, all of the
On 18 January 2011 22:52, Peter Ehlers wrote:
> Since you don't provide data, let's borrow from the
> help(droplevels) page:
>
As an aside, is it normal practice then to attach data files to questions on
this mailing list? I might do that in future if it's possible and
acceptable.
Ben.
Dennis, thank you for the response!
Sorry for lack of clarity, I'll explain a little more below...
> plot(Depth[LithClass=='sand'], Conductivity[LithClass=='sand'])
>> (ad nauseum... how can I loop through them all?)
>>
>
I have several lithology classes - sand, clay, limestone, etc... I wish to
p
Thanks for the reply Peter.
On 18 January 2011 22:52, Peter Ehlers wrote:
> Since you don't provide data, let's borrow from the
> help(droplevels) page:
>
I had no joy with my R install finding droplevels exactly, but found this
instead:
> ??droplevels
gdata::drop.levels Drop unused factor
hello, I am very new to R.
My current data set is a mix of values and categories. It is a geoscience
data set, with values per rock sample. Case in point, each sample belongs to
a lithology class, and each sample has several physical property
measurements (density, porosity...).
I want to be able
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