Displaying 20 results from an estimated 6417 matches similar to: "Gaussian Process Classification R packages"

2017 Dec 11

1

Gaussian Process Classification R packages

Google it!
"R Gaussian process model binary classification."
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Mon, Dec 11, 2017 at 4:53 AM, Damjan Krstajic <dkrstajic at hotmail.com>
wrote:
> Dear All,
>

2017 Dec 11

1

Gaussian Process Classification R packages

Thank you Charles Berry for your kind reply. I don't see anything wrong with the word "struggling". I have spent several hours trying various R packages like kernlab and GPfit to use GP to create a binary classification model which produces a prediction interval for each sample. I have been struggling because with all of them you may create a GP classification model but it only

2017 Dec 12

1

Gaussian Process Classification R packages

For the record:
I **was** trying to be helpful. I simply didn't know whether "I struggled"
meant that the OP had done a web search; as Chuck mentioned, when I did
one, I found what looked like possibly helpful hits. The OP's hostile
response frankly surprised me, but I see no reason to respond in kind.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is

2017 Dec 11

1

Gaussian Process Classification R packages

I have kindly asked for help and I am sad to receive such a reply from some on the r-help list.
I did google it prior to sending my request, and I could not find any R package which provides GP classification model which produces prediction intervals for each sample. I would be grateful if anybody could inform me about it. Thank you.
________________________________
From: Bert Gunter

2017 Dec 12

1

Gaussian Process Classification R packages

For the record please re-read my original message. It is clear, concise, polite and thankful for future help. I received a reply "Google it!". Thank you!
Thank you Jeff for your links. I am aware of them. However, they do not point to an R package for GP for binary classification which produces prediction intervals.
It seems that r-help is not as it was before. Wish you all the

2017 Dec 11

1

Gaussian Process Classification R packages

While a plea about struggling may seem appropriate to you, it is just as content-free as a reply telling you to use Google... and like it or not, that tit-for-tat arises due to frustration with lack of specificity as detailed by Charles. That is, if you are constructive about documenting your issue with a reproducible example and mentioning what you have tried and how it failed, you won't

2017 Dec 11

1

Gaussian Process Classification R packages

> On Dec 11, 2017, at 8:06 AM, Damjan Krstajic <dkrstajic at hotmail.com> wrote:
>
> I have kindly asked for help and I am sad to receive such a reply from some on the r-help list.
>
>
Well, you only said you were `struggling' to find a package.
Bert may well have done the Google search himself and found numerous resources on such models including links to R (as I

2011 Dec 27

2

differences between 1.7 and 1.7.1 glmnet versions

Dear All,
?
I have found differences between glmnet versions 1.7 and 1.7.1 which, in
my opinion, are not cosmetic and do not appear in the ChangeLog. If I am
not mistaken, glmnet appears to return different number of selected
input variables, i.e. nonzeroCoef(fit$beta[[1]]) differes between
versions. The code below is the same for 1.7.1 and 1.7, but you can see
that outputs differ. I would

2010 Mar 06

4

scientific (statistical) foundation for Y-RANDOMIZATION in regression analysis

Dear all,
I am a statistician doing research in QSAR, building regression models where the dependent variable is a numerical expression of some chemical activity and input variables are chemical descriptors, e.g. molecular weight, number of carbon atoms, etc.
I am building regression models and I am confronted with a widely a technique called Y-RANDOMIZATION for which I have difficulties in

2011 Dec 07

1

How to fit the log Gaussian Cox process model

Hi,
As far as I know, there exist some programs via the function INLA,
but I''m so curious if there is a specific function directly used to fit the
log Gaussian Cox process model and
predict the latent Gaussian field. That is, if I have a data points, then I
input it in the function and
don''t need to revise the program. Thanks for your help.
Joseph
--
View this message in

2008 Jun 12

0

using MCLUST package to estimate a poisson-gaussian process

Hi All,
I am using em() function to estimate a poisson-gaussian process from a
univariate one dimension time series, but not sure how to do. In the help
manual, it specify that in "pro" of the argument "parameter", if the model
includes a Poisson term for noise, there should be one more mixing
proportion than the number of Gaussian components. But in the example, the
parameter

2006 May 11

1

Simulating scalar-valued stationary Gaussian processes

Hi,
I have a sample of size 100 from a function in interval [0,1] which can be
assumed to come from a scalar-valued stationary Gaussian process. There are
about 500 observation points in the interval. I need an effective and fast
way to simulate from the Gaussian process conditioned on the available data.
I can of course estimate the mean and 500x500 covariance matrix from data.
I have searched

2010 Jun 01

1

BreastCancer Dataset for Classification in kknn

Dear All,
I''m getting a error while trying to apply the BreastCancer dataset
(package=mlbench) to kknn (package=kknn) that I don''t understand as I''m new
to R.
The codes are as follow:
rm = (list = ls())
library(mlbench)
data(BreastCancer)
library(kknn)
BCancer = na.omit(BreastCancer)
d = dim(BCancer)[1]
i1 = seq(1, d, 2)
i2 = seq(2, d, 2)
t1 = BCancer[i1, ]

2010 Dec 09

3

survival: ridge log-likelihood workaround

Dear all,
I need to calculate likelihood ratio test for ridge regression. In February I have reported a bug where coxph returns unpenalized log-likelihood for final beta estimates for ridge coxph regression. In high-dimensional settings ridge regression models usually fail for lower values of lambda. As the result of it, in such settings the ridge regressions have higher values of lambda (e.g.

1999 Jun 08

1

inverse.gaussian, nbinom

Two questions:
1. inverse.gaussian is up there as one of the glm families, but do
people ever use it? There is no inverse.gaussian in the R
distribution family, and when I checked McCullagh & Nelder, it only
appeared twice in the book (according to subject index), once in the
table on p. 30 and once on p. 38 in a passing sentence. Is there a
good reference on this distribution?
2. When I

2005 May 03

2

comparing lm(), survreg( ... , dist="gaussian") and survreg( ... , dist="lognormal")

Dear R-Helpers:
I have tried everything I can think of and hope not to appear too foolish
when my error is pointed out to me.
I have some real data (18 points) that look linear on a log-log plot so I
used them for a comparison of lm() and survreg. There are no suspensions.
survreg.df <- data.frame(Cycles=c(2009000, 577000, 145000, 376000, 37000,
979000, 17420000, 71065000, 46397000,

2003 Feb 12

1

rpart v. lda classification.

I''ve been groping my way through a classification/discrimination
problem, from a consulting client. There are 26 observations, with 4
possible categories and 24 (!!!) potential predictor variables.
I tried using lda() on the first 7 predictor variables and got 24 of
the 26 observations correctly classified. (Training and testing both
on the complete data set --- just to get

2011 Feb 08

2

Simulation of Multivariate Fractional Gaussian Noise and Fractional Brownian Motion

Dear R Helpers,
I have searched for any R package or code for simulating multivariate
fractional Brownian motion (mFBM) or multivariate fractional Gaussian noise
(mFGN) when a covariance matrix are given. Unfortunately, I could not find
such a package or code.
Can you suggest any solution for multivariate FBM and FGN simulation? Thank
you for your help.
Best Regards,
Ryan
-----
Wonsang You

2005 Aug 26

2

Fitting data to gaussian distributions

Hi!
I need to fit a data that shows up as two gaussians partially
superimposed to the corresponding gaussian distributions, i.e.
data=c(rnorm(100,5,2),rnorm(100,-6,1))
I figured it out how to do it with mle or fitdistr when only one
gaussian is necessary, but not with two or more. Is there a function in
R to do this?
Thank you very much in advance,
Luis

2005 Oct 21

1

finite mixture model (2-component gaussian): plotting component gaussian components?

Dear Knowledgeable R Community Members,
Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance.
I have a finite mixture modeling problem -- for example, a 2-component gaussian mixture -- where the components have a large overlap, and
I am trying to use the "mclust" package to solve this problem.
I need