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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(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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u could have three sets in a 3d plot, of course, but
that you would have to implement yourself. :)
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hat I get in the Environment
> (pls values)
Again, that depends on what you want with your model.
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orrelation between the response 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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s
() 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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t
local({
options(useHTTPS = FALSE)
})
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?
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 regression , fitted with the kernel
select/deselect which groups of variables you want in the
model.
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far as I can remember, you 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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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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of a 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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= mydata)
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fits too many components (i.e., one starts to model
noise), the coefficients of the last components get higher and
higher.)
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().
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there, and that for _training_ data 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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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$octane, col = red)
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with current CRAN submission policies,
pls.options() no longer stores the modified option list in the global
environment. This has the effect that the options will have to be set
every time R is started, even if the work space was saved an loaded.
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this:
plsr(resp ~ spec, data = mydata, )
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= /lib64/libc.so.6 (0x7ff5267d7000)
/lib64/ld-linux-x86-64.so.2 (0x00344520)
libtinfo.so.5 = /lib64/libtinfo.so.5 (0x7ff5265b6000)
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You give us far too little information about what you do, what you
want and what happens.
Given that, the only help one can give is: Read the documentation. :)
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,
Bjørn-Helge Mevik
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anything about which R
package you use for PLSR (and since I don't have access to SAS), I can
only guess. :)
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coloumns, but yournewdata$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 thomas.moc...@nateko.lu.se 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
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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the I(as.matrix()).
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() take _ages_. Then using matrices is
easier and faster.
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.
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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at +/-1 SE (since 2.2.0). See ?coefplot
- The package now has a name space (since 2.2.0).
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introduction 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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Try to read the pls package article available here:
http://www.jstatsoft.org/v18/i02/
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hypothesis 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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)
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.
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, not
using the `labels' argument, or converting the names manually.
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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 the Warning section inf ?jack.test!
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is optional), are handled.
So don't ask the plot function to use numbers as labels. Use e.g. names
instead: labels = names.
Tip: It is always a good idea to read the output and error messages very
carefully.
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)
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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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\
--with-lapack=-L/site/intel/cmkl/8.1/lib/em64t -lmkl_lapack64 -lmkl \
--enable-R-shlib
but we wanted to switch to gcc because not all R packages compile with icc.)
Does anyone have any idea about what could be wrong?
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)^2 instead
of 1 - SSE/SST.
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= train,
intercept = FALSE)$val)
(For cross-validated or test set validated models, it uses RMSEP.)
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[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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- 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. This is controlled by the 'na.action' argument.
See ?na.action for details.
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[Ricardo Rodriguez] Your XEN ICT Team webmas...@xen.net 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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Barry Rowlingson b.rowling...@lancaster.ac.uk 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.
:-)
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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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glenn g1enn.robe...@btinternet.com 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(). :-)
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or cov^2, but I believe they should be identical (up
to sign).
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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, perhaps?
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. The crossvalidation implemented in the pls package
does not save the cross-validated scores/loadings -- that would consume
too much memory. (Calculation of SPE withing the cross-validation
routines could have been implemented, but was not.)
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Why not simply
a - c(a, 5)
or
a - c(a, b)
if b is another vector.
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],
VARS = I(as.matrix(cancerv1[, 2:407])))
otherwise data.frame() will split the matrix into single coloumn variables.
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achieve this?
I don't remember exactly what I did, but this will work, at least:
yarn - data.frame(density = ..., train = ...)
yarn$NIR - as.matrix(...)
For practical purposes, I haven't found any difference between having
the matrices with class AsIs and matrix.
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long time
dealing with the formula.
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knowledge) not known. Any significance deduced
from them should therefore be regarede as merely indicators.
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?
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traceback() just after receiving the
error message. That might tell you more about _where_ the error
occurred.
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in the sources for all changes.
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-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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,
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 quote(r). :-)
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to ask the one(s) who created the file.
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