similar to: Fitting parabolic function to data

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 advice.

Dear R Users, I use for my data crunching a combination of MySQL and GNU R. I have to handle huge/ middle seized data which is stored in a MySql database, R executes a SQL command to fetch the data and does the plotting with the build in R plotting functions. The (low level) calculations like summing, dividing, grouping, sorting etc. can be done either with the sql command on the MySQL side or

Dear R users and experts, I need to modify an exisiting R function from a package. The function/ variable is inside a namespace and I have problems accessing it. I only need to get access to to corresponding sourcecode. I localised the "RDX" and "RDB" files. But it seems to be a binary format. How can I convert it to an ASCII format to open and extract the code from a text

Dear R Beginners, Experts and Users, I already asked this question on the R-sig-DB mailing list, but sadly didn't got a reply :-( . Keeping fingers crossed that I have more luck on the main R mailinglist :) . I'm trying to connect to a MySQl database via Gnu R using the RODBC interface. The conenction workes but I get an empty result string. Gnu R and the MySql DB are running on a 64 bit

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 have an array of floating-point measurements on a square (5 by 5) 2d grid. The data are nominally constant, and somewhat noisy. I need to find any significant spatial trend, e.g. bigger on the left, bigger in the middle, etc. I have many thousands of these data sets that need to be scanned for 'interesting' spatial variations, selecting the datasets that are beyond some criterion of

Hi all. I was having some trouble with a for loop and I found the problem is the following. Does anyone have some idea why I got the following R result? Since mone is equal to 3, why mu1 only have 2 components? library(MASS) > p0 <- seq(0.1, 0.9,by=0.1) > m <- 10 > > > p0 <- p0[7] > > ## data generation > > mzero <- p0*m > mone <- m-mzero >

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

Quoting asterisk-users-request@lists.digium.com: > > Would it be considered trolling to start a thread on Cleaning Maple > > Syrup off of Dial Pads, or Wiring your Moose for Wi-Fi? > > Let's not forget the weekly "tooques and telephony" segment, and a > review of > the best block heaters for your wi-fi fones. > Oh, we're gonna have a good time next

Hi R experts, Could you please help me to fit a linear, cubic and quadratic curve in a figure? I was trying to show all these three fitting curves with different colour in one figure. I spent substantial time to figure it out, but I could not. I have given here a example and what I did for linear, but no idea for cubic and quadratic fitting curve > dput(test) structure(list(sp = c(4L, 5L,

Rers: I have some ecological data (stream velocity vs. % cover of submerged weeds) that shows strong evidence of a thresholding step-function, e.g. below some velocity, % cover ranges from 0% to 100% (with no apparent relationship to velocity within this range of velocities), but above a certain "threshold" velocity, the % cover does not appear to exceed, say, 10%. There are good

I have two ideas about it. 1- i) Entering variables in quadratic form is done with the command I (variable ^ 2) - plsr (octane ~ NIR + I (nir ^ 2), ncomp = 10, data = gasTrain, validation = "LOO" You could also use a new variable NIR_sq <- (NIR) ^ 2 ii) To insert a square variable, use syntax I (x ^ 2) - it is very important to insert I before the parentheses. iii) If you want to

Below. -- Bert Bert Gunter On Thu, Jul 13, 2017 at 3:07 AM, Luigi Biagini <luigi.biagini at gmail.com> wrote: > I have two ideas about it. > > 1- > i) Entering variables in quadratic form is done with the command I > (variable ^ 2) - > plsr (octane ~ NIR + I (nir ^ 2), ncomp = 10, data = gasTrain, validation = > "LOO" > You could also use a new variable

Hi, I'm using R to find esplicit functions fitting set of data. The data contains about 30 points, which have different weights (number of cases represented from the point). I plot the points, choose "by eye" a function made with exp or arctg or polinomial and use nlm to minimaze the root mean error with correct the weights. For Example: Err <- function(p) sum((weight*(y

Dear all, I am using the pls package of R to perform partial least square on a set of multivariate data. Instead of fitting a linear model, I want to fit my data with a quadratic function with interaction terms. But I am not sure how. I will use an example to illustrate my problem: Following the example in the PLS manual: ## Read data data(gasoline) gasTrain <- gasoline[1:50,] ## Perform

> On Jul 13, 2017, at 7:43 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > > Below. > > -- Bert > Bert Gunter > > > > On Thu, Jul 13, 2017 at 3:07 AM, Luigi Biagini <luigi.biagini at gmail.com> wrote: >> I have two ideas about it. >> >> 1- >> i) Entering variables in quadratic form is done with the command I >>

Dear List: I have a data frame prepared in the couting process style for including a binary time-dependent covariate. The first few rows look like this. PtNo Start End Status Imp 1 1 0 608.0 0 0 2 2 0 513.0 0 0 3 2 513 887.0 0 1 4 3 0 57.0 0 0 5 3 57 604.0 0 1 6 4 0 150.0 1 0 The outcome

?? If I haven't misunderstood, they are completely different! 1) NIR must be a matrix, or poly(NIR,...) will fail. 2) Due to the previously identified bug in poly, degree must be explicitly given as poly(NIR, degree =2,raw = TRUE). Now consider the following example: > df <-matrix(runif(60),ncol=3) > y <- runif(20) > mdl1 <-lm(y~df*I(df^2)) > mdl2

> On Jul 12, 2017, at 6:58 PM, Ng, Kelvin Sai-cheong <kscng at connect.hku.hk> wrote: > > Dear all, > > I am using the pls package of R to perform partial least square on a set of > multivariate data. Instead of fitting a linear model, I want to fit my > data with a quadratic function with interaction terms. But I am not sure > how. I will use an example to

Hello, I have some xy data which clearly shows a non-monotonic, peaked triangular trend. You can get an idea of what it looks like with: x<-1:20 y<-c(2*x[1:10]+1,-2*x[11:20]+42) I've tried fitting a quadratic, but it just doesn't the data-structure with the break point adequately. Is there anyway to fit a triangular or 'tent' function to my data in R? Some sample code