search for: parabol

Dear all, I need your advice since I am looking for an implementation of the parabolic cylinder function in R. I found implemantations of the hypergemetric functions (the Whittaker and the confluent hypogeometric functions) in the package fAsianOptions but the parabolic cylinder function was unfortunately not there. Do you know of such implementation? Thank you very much for your...

Dear R users and experts, I want to fit a shifted parabolic function with the following functional form to my data: f(x)=a0*(x+a1)^2+a2 (a0, a1 and a2 are scaling factors.) What is standard approach to do this in R? I tried the "lm" function in R but I got problems getting the above functional form. Any help is welcome :) . Greetings, Henri

Hi all, i''m new at LARTC, and after reading the docs I found no solution to my problem ... On one side I have eth0 conected to the LAN, on the other side I have eth1 conected to a switch and to 3 DSL routers with 3 diferent providers, and also eth2 conected to a cisco 2600 conected to a LDMS line. I have readed the larct docs about multihomed conections to internet, but I''m

Dear all, Does any know how to solve PDE with R? The archive list refers to the use of ODE if PDE are parabolic. I am not a mathematician and this does not mean anything for me! help would be very appreciated. Many thanks -- ___________________________________________________ Christophe NGUYEN UMR 1220 INRA-ENITAB Transfert sol-plante et cycle des ?l?ments min?raux dans les ?cosyst?mes cultiv?s"...

Hi, I've got a matrix, Z, of values representing (as it happens) optical power at each pixel location. Since I know in advance I've got a single, convex peak, I would like to do a 2D parabolic fit of the form Z = poly((x+y),2) where x and y are the x,y coordinates of each pixel (or equivalently, the row, column numbers). Is there an R function that lets me easily implement that? I've started down the path of something like zvec <- as.vector(Z), and creating applicable x,y v...

...gment, and a > review of > the best block heaters for your wi-fi fones. > Oh, we're gonna have a good time next Thursday. We need to get Molson Canadian to sponsor us and find Bob & Doug for the event? By the way, eh. It's hard to get the moose to cooperate. When you put the parabolic antenna on his antlers you have to ride him backwards when you're leaving your cabin, eh. dbc. -- David Cook

...city threshold. 3) Determine the minimum p-value from the previous step, which should be the velocity threshold -- the p-value at this threshold is the significance of the threshold. Does this make sense? Is there a better way of doing this? When I ran this on the data, you see a nice, nearly parabolic relationship around what the threshold appears to be of p-value vs. threshold. --j -- Jonathan A. Greenberg, PhD Postdoctoral Scholar Center for Spatial Technologies and Remote Sensing (CSTARS) University of California, Davis One Shields Avenue The Barn, Room 250N Davis, CA 95616 Cell: 415-7...

...ata sets that need to be scanned for 'interesting' spatial variations, selecting the datasets that are beyond some criterion of flatness. My thought was to fit a 2'nd order polynomial with least-squares or some such metric, and scan for coefficients bigger than some cutoff. I think a parabolic surface is probably as complex a surface as the small amount of data merits. Is there functionality in R that would be appropriate? Is there some other approach anyone would suggest for the general task? I'm not very experienced in data crunching, so any suggestion would be appreciated....

...is going on. I tried fitting models adjusted for other covariates that I have in the data frame. This did not appreciably affect the coefficients for the implant variable. I ran cox.zph on the two models shown above and plotted the results. In both cases, the point estimate of Beta(t) is sort of parabolic in that the curves are monotonically increasing to a local maximum after which they are monotonically decreasing (the CIs are a bit more wiggly). I would interpret this to mean that the effect of implant is probably time-dependent. If so, how do I actually get a "proper" estimate of b...

Hi, everyone I am trying to estimate 3 parameters for my survival function. It's very complicated. The negative loglikelihood function is: l<- function(m1,m2,b) -sum( d*( log(m1) + log(m2) + log(1- exp(-(b + m2)*t)) ) + (m1/b - d)*log(m2 + b*exp(-(b + m2)*t) ) + m1*t - m1/b*log(b+m2) ) here d and t are given, "sum" means sum over these two vairables. the parameters

Hello, I want to solve some two dimensional equation system with R. Some systems are not solvable analytically. Here is an example: (I) 1/n*sum{from_i=1_to_n}(Xi) = ln lambda + digamma(c) (II) mean(X) = x / lambda I want to find lambda and c, which R-function could do that task? Carsten [[alternative HTML version deleted]]

Sorry, For me a vector is a matrix with mx1 dimmensions. But it is true that it is not the way I correctly must talk in R. Can you guide me in what I?m trying to do? I?m trying to find what I want in Excel using something like a parabolic function but dind?t get yet (then to try to replicate in R). 2017-06-02 7:01 GMT+02:00 Bert Gunter <bgunter.4567 at gmail.com>: > Bert Gunter > > "The trouble with having an open mind is that people keep coming along > and sticking things into it." > -- Opus (aka...

Subject: Eliminate repeated components from a vector X-Mailer: VM 7.00 under 21.4 (patch 6) "Common Lisp" XEmacs Lucid Reply-To: fjmolina at lbl.gov FCC: /home/f/.xemacs/mail/sent Does anyone know how I can eliminate repeated elements from a vector?

...btain about 5 different sample, depending by a parameter, so I have about 200 different function to fit: I can't do this work for each function :( 1)There is a family of functions wich is more general and strong to fit most of my data? (most of my functions are very similar to arctg, or exp or parabolic: they are not bad :) 2)How can I use nls to do the same thing (I hope nls is stronger than my implementation of nlm: or not?) Thank you very much! -- ___________________________________________________________________ | _____ _ _ _ ___ AGOSTINO MANZATO | | |...

Is there a package in R which implements numerically solves pde ? Thanks, Tolga

Hi, anyone knows about any functions in R can get multidimensional integration not over a multidimensional rectangle (not adapt). For example, I tried the following function f(x,n)=x^n/n! phi.fun<-function(x,n) { if (n==1) { x }else{ integrate(phi.fun, lower=0, upper=x, n=n-1)$value } } I could get f(4,2)=4^2/2!=8, but failed in f(4,3)=4^3/3! Thanks Best, Lynette

...maxx, but it may not be 0 < y < minx. Thus small, non-zero values are removed. The last criteria is that the solution must be very fast to compute (e.g. 1/3 second for vector of 5000). I coded something up using the L-BFGS-B method of optim where I penalized values between (0, minx) with a parabolic cost function. While reasonably fast and accurate, I occasionally get the message "ERROR: ABNORMAL_TERMINATION_IN_LNSRCH". I believe this is because the gradient is discontinuous at 'minx', so optim finds the gradient calculation unsatisfactory around that value. Not supplying t...

which is the purpose of this functions? _________________________________________________________________ Busca desde cualquier p?gina Web con una protecci?n excepcional. Consigue la Barra de herramientas de Windows Live hoy mismo y GRATUITAMENTE. http://www.toolbar.live.com -------------- next part -------------- An HTML attachment was scrubbed... URL:

Dear all, I'm trying to produce a log-likelihood profile for a GPD estimate with the ismev package. When following the examples, everything is working fine, i.e. I get a nice parabolic log-likelihood curve. When using my own data (intensity of hurricanes at landfall), the log-likelihood curve looks like a step function. Am I doing something wrong here? Is my data not suited for the GPD fit? Any help is highly welcome. Furthermore: how can I extract the data at which the log-li...

Hi R-users, I am using arima to fit a time series. Now I would like to include an intervention component "It (0 before intervention, 1 after)" using different types of impacts, that is, not only trying the simple abrupt permanent impact (yt = w It ) with the xreg option but also trying with a gradual permanent impact (yt= d * yt-1 + w * It ), following the filosophy of Box and Tiao