t; (pls values)
Again, that depends on what you want with your model.
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f course, but
that you would have to implement yourself. :)
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l of them in the formula (y ~ x1 + x2 + x3 + ...), or you can use the
following shortcut to regress y on all the remaining coloumns:
plsr(y ~ ., ..., data = mydata)
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any components (i.e., one starts to model
"noise"), the coefficients of the last components get higher and
higher.)
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ssible (or at least very difficult) for sensitive information to leak
out of the network.
I would say that your best bet is to expect all analysis software to
have security holes or be compromised, and design your setup/network
around that assumption.
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sig
Well, this is not an elegant (or robust) solution, but this would work
for the example you give, at least:
starttime <- as.POSIXct("2018-11-20 23:01:18") # Just pick a random date
format(starttime + c(0:4), format = "%T")
There are probably better ways. :)
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new argument `nrep'. See ?cvsegments
for details.
- It now has a vignette.
- It now has a NEWS file that can be accessed by news().
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or
message is still displayed, which can be confusing. Is there a way
around this problem?
Also, perhaps the useHTTPS option should default to FALSE if the libcurl
capability is FALSE?
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u get this error if the n96 object was a
data.frame instead of a matrix. Can you check with, e.g.,
> class(n96)
If it says "data.frame", try using I(as.matrix(n96)).
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variables you want in the
model.
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
an
n96.1 n96.2
1 1 1 6
2 2 2 7
3 3 3 8
4 4 4 9
5 5 510
> dim(frame1)
[1] 5 2
> names(frame1)
[1] "gushVM" "n96"
> rm(n96)
> pls1 <- plsr(gushVM ~ n96, data = frame1)
> pls1
Partial least squares
ALSE)
})
in the Rprofile.site file, or your ~/.Rprofile. You still get a
warning, but you do get the list of http repositories.
Come to think about it: would it be an idea if R defaulted to useHTTPS =
FALSE if capabilites("libcur") is FALSE?
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() for automatically suggesting the
optimal number of components for the model. The function implements
two different algorithms, and will optionally plot the RMSEP values
and number of components.
- A description of selectNcomp() has been added to the vignette.
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onse and the prediction
variables. (Or as they tend to say in chemometrics: You don't have a
model.)
> As I said, I think it better to follow up or complain about me on
> stackexchange rather than here.
Sorry, I read this too late. :)
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Des
Why not simply
a <- c(a, 5)
or
a <- c(a, b)
if b is another vector.
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"[Ricardo Rodriguez] Your XEN ICT Team" writes:
> John Kane wrote:
>> No but have you had a look at Tinn-R http://www.sciviews.org/Tinn-R/.
>
> Any similar option for Mac OS X?
I guess you can use Emacs on Mac OS X.
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They should be
matrices instead of data frames, for instance by converting them like
this:
inputmat <- data.frame(respmat = I(as.matrix(foo)), predmat =
I(as.matrix(bar)))
As for missing values: the default behaviour of plsr is to omit cases
with missing values. Th
dation in the pls package does not propose a number of
factors as optimum, you have to select this yourself. (The reason for
this is that there is AFAIK no theoretically founded and widely accepted
way of doing this automatically. I'd be happy to learn otherwise.)
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Bjø
Barry Rowlingson writes:
> http://www.maths.lancs.ac.uk/~rowlings/R/Cranography/
Absolutely beautiful!
> Note this is just for fun. No warranties. Maybe I should use a little
> 'R' as a marker.
That would be cool.
> Maybe I should get a life.
:-)
r-visible changes:
- In order to comply with current CRAN submission policies,
pls.options() no longer stores the modified option list in the global
environment. This has the effect that the options will have to be set
every time R is started, even if the work space was saved an
els in the plot.
If you wish to have them in the same panel, you will have to add the
points yourself. This should work:
plot(gas1, ncomp=2, asp = 1, line = TRUE)
points(predict(gas1, ncomp = 2) ~ gasoline$octane, col = "red")
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_
ata it is indeed
PCC^2, but _not_ for cross-validation or test data.
IMHO, R^2 only has a meaningful interpretation for training data. For
test data or cross-validation, I prefer MSEP or RMSEP.
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t; /lib64/libgcc_s.so.1 (0x7ff526cb9000)
libintlc.so.5 =>
/cluster/software/VERSIONS/intel-2011.10/composer_xe_2011_sp1/lib/intel64/libintlc.so.5
(0x7ff526b6a000)
libc.so.6 => /lib64/libc.so.6 (0x7ff5267d7000)
/lib64/ld-linux-x86-64.so.2 (0x00344520)
I(spec))
Then you can analyse like this:
plsr(resp ~ spec, data = mydata, )
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projection[,1:2]
will reproduce the values from predict().
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and provide commented, minimal, self-contained, reproducible code.
yve <- 100 * drop(R2(object, estimate = "train",
intercept = FALSE)$val)
(For cross-validated or test set validated models, it uses RMSEP.)
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"SC10 Disruptive Technology Preview: The First Cloud Portal to “R” and Beyond"
http://www.hpcinthecloud.com/features/SC10-Disruptive-Technology-Preview--The-First-Cloud-Portal-to-R-and-Beyond-105776458.html?viewAll=y
(My apologies if ths has been posted already.)
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this is equivalent to cor(y, yhat)^2, but not for test data or
cross-validation.
>From your second email, I would guess that MOE uses cor(y, yhat)^2 instead
of 1 - SSE/SST.
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./configure --prefix="/site/VERSIONS/R-$version" \
--with-blas="-L/site/intel/cmkl/8.1/lib/em64t -lmkl -lvml -lguide -lpthread" \
--with-lapack="-L/site/intel/cmkl/8.1/lib/em64t -lmkl_lapack64 -lmkl" \
--enable-R-shlib
but we wanted to switch to g
g the coloumns in FullDataListTrans
separate variables in the data frame.)
BHPLS1 <- plsr(GroupingList ~ PCIList, ncomp = FeaturePresenceExpected[1], data
= FullDataListTrans, validation = "LOO")
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R-h
sure of how to utilise these to identify the significant
> variables.
You can use the jackknife built into plsr to get an indication about
significant variables, by adding the argument "jackknife = TRUE" to the
plsr call. Use jack.test(BHPLS1) to do the test.
But _PLEASE_ do read
umbers. That is not surprising, since the variable names are PCIList1,
PCIList2, etc., and the documentation for loadinplot says:
with '"numbers"', the variable names are converted to numbers, if
possible. Variable names of the forms '"number&qu
ee the problem??
By using `labels = "numbers"', you are asking the plot function to
convert the names "PCIList1" "PCIList2" "PCIList3" "PCIList4" ... to
numbers. It doesn't know how to do that. (See ?loadingplot for the
details.)
Your o
and is a standard way of representing Near Infrared Reflectance
measurements.
[1] http://cran.r-project.org/doc/Rnews/Rnews_2006-3.pdf
[2] J. H. Kalivas. Two data sets of near infrared spectra. Chemometrics and
Intelligent Laboratory Systems, 37: 255–259, 1997.
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Bjørn-Helge Mevik
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You give us far too little information about what you do, what you
want and what happens.
Given that, the only help one can give is: Read the documentation. :)
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Research Computing Services, University of Oslo
Try to read the pls package article available here:
http://www.jstatsoft.org/v18/i02/
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PLEASE do read the posting guide http://www.R
on to linear
models in R, and you will come a long way.
There is also a paper in JSS about the pls package:
http://www.jstatsoft.org/v18/i02/
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+/-1 SE (since 2.2.0). See ?coefplot
- The package now has a name space (since 2.2.0).
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res (OLS)
regression (the lm() function in R). There you get p-values automatically.
Furthermore, a PLS regression with the same number of components as
predictor variables is equivalent to OLS, so there seems no reason to
use PLS at all in your case.
-
message says - matches multiple arguments, in this case "segment.type".
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
othesis tests and performance statistics. This is not how PLSR is
usually applied, and there are few such tools. The traditional/typical
focus amongst PLSR practicioners is much more on prediction performance
(RMSEP) and interpretation by plotting scores and loadings.
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3
[2,]24
> ## Without I():
> d2 <- data.frame(A = A, B = B)
> d2
A.1 A.2 B.1 B.2
1 1 3 2 4
2 2 4 3 5
> names(d2)
[1] "A.1" "A.2" "B.1" "B.2"
> d2$A
NULL
> d2$A.1
[1] 1 2
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_
lsr(depy ~ indx, data=eqn)
>
> and this gives me [7] ERROR: object 'depy' not found
because you are missing the I(as.matrix()).
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if
you have many, it will take a lot of typing, and make the
formula handling part of plsr() take _ages_. Then using matrices is
easier and faster.
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newdata)
and that yourdata$subX contains 501 coloumns, but yournewdata$subX only
contains 73 coloumns. You must supply a newdata with the same number of
coloumns as in the modelling data.
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Thomas Möckel writes:
> I have a question about understanding PLS. If I use the predict function of R
> than it seems to me the function only uses the last latent variable to model
> new Y values. But should the function not use all latent variables to model
> new Y´s?
It should, and it definite
stead of c(), or simply say
A <- pls[,1:4]
B <- pls[,5:8]
The the rest should work.
Btw. it is probably a good idea to avoid single-character names for
variables. Especially c and C, because they are names of functions in R.
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_
27;t say anything about which R
package you use for PLSR (and since I don't have access to SAS), I can
only guess. :)
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PLEASE do read th
lement of
'scale'. If 'scale' is 'TRUE', X is scaled by dividing each
variable by its sample standard deviation. If
cross-validation is selected, scaling by the standard
deviation is done for every segment.
When in doubt, read the documentat
not
too large. With many variables (>> 1000), R will spend a very long time
dealing with the formula.
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a.frame(resp = cancerv1[, 408],
VARS = I(as.matrix(cancerv1[, 2:407])))
otherwise data.frame() will split the matrix into single coloumn variables.
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es, I haven't found any difference between having
the matrices with class "AsIs" and "matrix".
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knowledge) not known. Any significance deduced
from them should therefore be regarede as merely indicators.
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strange.
Actually, as of version 2.0-0, mvr() etal should cope with factors
without problems. They will be coded just as in lm().
Another thing to try is to say traceback() just after receiving the
error message. That might tell you more about _where_ the error
occurred.
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The `Writing R Extensions', chapter 5 `System and foreign language
interfaces' tells you how to compile and run Fortran code from R.
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PLEA
# why it is different from 0.15?
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and provide commented, minimal, self-contained, reproducible code.
, fit.pls$Xmeans, "+")
would give you what you want.
Note, however, that this will calculate the _fitted_ SPE, not the
cross-validated SPE. The crossvalidation implemented in the pls package
does not save the cross-validated scores/loadings -- that would consume
too much memory. (C
"Raphael Saldanha" <[EMAIL PROTECTED]> writes:
> Is there a Gui for R with improvements in the command line? I'm not looking
> for buttons, menus and etc, but (more) colored syntax, auto-complete
> commands and etc?
ESS in Emacs, p
"glenn" writes:
> Is there a function (before I try and write it !) that allows the input of a
> covariance or correlation matrix to calculate PCA, rather than the actual
> data as in princomp()
Yes, there is: princomp(). :-)
--
k to suggest me?
There is the package 'pls', with Principal Component Regression (PCR) and
Partial Least Squares Regression (PLSR). It also contains a couple of
plots that are useful for princomp() or prcomp() analyses (PCA).
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ations and performance changes). I don't recall if the
criterion was cov or cov^2, but I believe they should be identical (up
to sign).
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PLEAS
the file. The best idea is
probably to ask the one(s) who created the file.
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A good answer is found in the FAQ for R, in
FAQ 7.31 Why doesn't R think these numbers are equal?
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ting in 1964: Cambridge autocode,
>algol, phoenix, machine-code, Fortran, BBC-Basic,
>GLIM, GENSTAT, Linux, S-Plus and finally (probably
>the best so far!) R."
Well, calling Linux a computer language will probably not add too much
credibility to the
e following to get R^2 and cross-validated R^2
(A.K.A. Q^2):
mypls <- plsr(Ytrain ~ Xtrain, ncomp = 1, validation="LOO")
## R^2:
R2(mypls, estimate = "train")
## cross-validated R^2:
R2(mypls)
## Both:
R2(mypls, estimate = "all")
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Bjørn-Helge Mevik
See the file CHANGES in the sources for all changes.
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