Hi, what about this:
C <- 0:499
for (i in C)
{
cat (i," ")
}
Best regards,
Andris Jankevics
AIO
Groningen Bioinformatics Centre
Groningen Biomolecular Sciences and Biotechnology Institute
University of Groningen
Kerklaan 30, Haren, 9751 NN, The Netherlands
On Mon, Dec 12,
Hi, something like this perhaps,
if("myfolder"%in%dir()==FALSE) dir.create("myfolder")
Command dir.create() is not overwriting an existing folder.
Best regards,
Andris
On Fri, Oct 29, 2010 at 12:08 PM, Ron Michael wrote:
> Hi all, I am wondering is there any way to check whether some Director
Hi,
?loadings
print (pca_result$loadings,cutoff=0)
unclass(pca_result$loadings)
--
Andris Jankevics
AIO
Groningen Bioinformatics Centre
Groningen Biomolecular Sciences and Biotechnology Institute
University of Groningen
Kerklaan 30, Haren, 9751 NN, The Netherlands
On Mon, Oct 19, 2009 at 4:34
Actually that is stated in paper abstract:
http://www.krebsregister.saarland.de/improve/periodR_en.html
Andris
2009/10/1 "Jens Oehlschlägel" :
> Hi,
> Does anyone know where the following package is available:
>
> Holleczek B, Gondos A, Brenner H.
> PeriodR - an R package to calculate long term
Hi, You can make a biplot on Your own, it is not so hard. And in this
case You can change parameters for every low level function as You
wish.
PC <- prcomp (iris[,1:4])
lambda <- PC$sdev * sqrt(nrow(PC$x))
plot (t(t(PC$x)/lambda),pch=16,col=as.numeric(iris[,5]))
par (new=T)
Rot <- t(t(PC$rotation)
Hi,
You want something like this?
library (pls)
data(yarn)
library (scatterplot3d)
yarn.pls <- plsr(density ~ NIR, 6, data = yarn, validation = "CV")
palette (rainbow(length(yarn$density)))
scatterplot3d
(yarn.pls$scores[,1:3],pch=16,color=1:length(yarn$density),cex.symbols=2)
?scatterplot3d
Hi,
plsr funtion in pls package are intented for calculation of regression
models. You must define your Y matrix as bianary identification matrix
or You can just use plsda function from R's caret package.
http://caret.r-forge.r-project.org/Classification_and_Regression_Training.html
Best regards,
Hi, try this
A <- c(1:1000)
A <- paste ("000",A,sep="")
substr (A,nchar(A)-3,nchar(A))
Best regards,
Andris Jankevics
On Mon, Apr 27, 2009 at 12:35 PM, Mario dos Reis wrote:
> I've been trough the R documentation for about half an hour and it's not
>
Hi,
just quick "googling" gave me the hint that it can be ralated to SE
Linux restrictions of Fedora linux. Just try to disable SE Linux
feature.
http://www.appistry.com/community/forums/content/cannot-restore-segment-prot-after-reloc-permission-denied
Best regards,
--
Andris
subramanian wrot
Hi, take a look on pls package and it's documentation, there are
examples also for NIR data.
http://mevik.net/work/software/pls.html
Article form "Journal of Statistical Software"
http://www.jstatsoft.org/v18/i02
Also "Caret" package can be used to evaluate pls and other regreesion models:
htt
only 15% of the variability.
> So, my questions are 1) is clara() a proper way to analyze such a large
> data set? and 2) Is there an appropiate method for graphic plotting of
> my data, that takes into account the whole variability if my data, not
> just two principal components?
>
.0 ...
> $ train : logi TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
> TRUE ...
>
> I think the important structure for my application is in the NIR line.
> Now that I "know" what the structure is, what does it mean, and how do I
> get my data into the same
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