similar to: Adding "sum" to derivatives table

Is there a way of calculating the derivative of a function returned by splinefun()? Such a function is a cubic spline, whence it has a calculable derivative, but is there a (simple) way of getting at it? One workaround that I have thought of is to take a fine grid of points, evaluate the function returned by splinefun() at these points, put an interpolating spline through these points using

Readers, A data set comprises A B C 10 6 .2 20 7 .4 30 8 .16 40 9 .0256 My requirement is to obtain the derivative for values of A with respect to B, create a function in R and plot this derivative against another variable (e.g. values in column C). Have searched the mailing list and found reference to a function 'D', but the help accessed via '?D' and '?deriv' does not

The deriv() function takes an 'expression' as its first argument). I was wondering if the this function can take an array or a vector of expressions as its first argument. Aside, I saw how to give a vector argument to the second argument. like to have something like: deriv(c(~x^2+y^3, ~x^5+y^6), c("x","y")) the documentation for this function talks about being able to

Hi wizards, I need to know how can I generate this numbers I have n numbers N1,N2 , Nn , but Nn = 1- sum(N1+N2+...+Nn-1) and sum(N1+N2+..Nn)=1 and N1,N2,..Nn with 0<N<1 . Does somebody know how to generate it ? Some distribution or algorithm. Thanks in advance. -- Web Page http://www.geocities.com/ricardo_rios_sv/index.html

In checking over the solutions to some homework that I had assigned I observed the fact that in R (version 2.4.0) pnorm(-1.46) gives 0.07214504. The tables in the text book that I am using for the course give the probability as 0.0722. Fascinated, I scanned through 5 or 6 other text books (amongst the dozens of freebies from publishers that lurk on my shelf) and found that some agree with R

Hello, For verbose coding I'd like to do something like: > myfunction <- function(x){ > if (a){ > stop(paste(myfunction_name_here,"requires xyz!") > } Is that possible? Thanks for any hints, Joh

Apologies for the off-topic question; as usual I'm trying to draw upon the unparalleled knowledge and sagacity of the r-help list. Please reply off-list if you can help me out. A collaborator of mine found a formula we need, on sheets which he had photocopied out of a book, some years ago. He cannot remember which book (he's getting to be as senile and forgetful as I am, poor bloke!).

Hi everyone, I am using R 2.4.1 on a MacOS X ("Tiger") operating system. In the last few day I was trying to estimate the parameters of a mixture of two normal distributions using Maximum Likelihood. The code is from Modern Applied Statistics with S (4th edition), chapter 16 ("Optimization"), the dataset is available under MASS in R. Unfortunately, when I tried out the

I've had to struggle a bit to get the prime/dash derivative notation into a plotmath expression. The use of expression(K'(t)) gives me a syntax error. The best I've been able to come up with is expression(paste(K,"'",(t),sep="")) Is this the ``accepted'' procedure, or is there a more direct route? cheers, Rolf Turner rolf at

Hi, I am facing the problem that I want to plot a histogram chart set freq to true and overlay with normal or weibull or exponential distribution curve. The sample code is shown as below: >samp<-c(-8.2262,-8.2262,-8.2262,-8.20209,-8.09294,-8.07321,-8.07321, -8.07321,-8.07175,-8.04948,-8.04948,-8.04948,-8.03848,-8.03848,

Colin Bleay wrote: > last week i sent an e-mail about dealing with errors thrown up from a > glm.nb model carried out on multiple random datasets. > > every so often a dataset is created which results in the following error > after a call to glm.nb: > > "Error: NA/NaN/Inf in foreign function call (arg 1) > In addition: Warning message: > Step size truncated due

The issue is that without an extensible derivative table or the proposed extensions, it is not possible to automatically produce (without manual modification of the deriv3 output) a function that avoids catastrophic cancellation regardless of the working range. Manual modification is not onerous as a one-time exercise, but can be time consuming when it must be done numerous times, for example

I have just been bitten by a quirk in the behaviour of model.matrix. I used model.matrix inside a function, and passed to it a formula that was built elsewhere. The formula was of the form ``y ~ x + w + z''. Now, model.matrix cheerfully accepts formulae of this form, although it only ***needs*** the right hand side, i.e. ``~ x + w + z'' --- the ``y'' can be dropped (but

I gather from reading the back-issues of r-help that it should be possible (modulo a number of caveats) to read an excel (yuck!) file into R using RODBC. I have obtained and installed ODBC and the RODBC package, but cannot for the life of me figure out how to go about it. Can anyone give me a simple recipe? I have an excel file on cdrom, say: /mnt/cdrom/melvin.xls I have started R and loaded

I don't know from the nortest package, but it should ***always*** be the case that you test hypotheses H_0: The data have a normal distribution. vs. H_a: The data do not have a normal distribution. So if you get a p-value < 0.05 you can say that ***there is evidence*** (at the 0.05 significance level) that the data are not from a normal distribution. If the nortest package does

A colleague asked me if there is a way to specify with a ***variable*** (say ``cflag'') whether there is an intercept in a linear model. She had in mind something like lm(y ~ x - cflag) where cflag could be 0 or 1; if it's 0 an intercept should be fitted, if it's 1 then no intercept. This doesn't work ``of course''. The cflag just gets treated as another predictor

I've built R functions to ``effect'' a particular distribution, and would like to find out if that distribution is already ``known'' by an existing name. (I.e. suppose it were called the ``Melvin'' distribution --- I've built dmelvin, pmelvin, qmelvin, and rmelvin as it were, but I need a real name to substitute for melvin.) The distribution is really just a toy

What R functionnalities are there to do missing values imputation (substantial proportion of missing data)? I would prefer to use maximum likelihood methods ; is the EM algorithm implemented? in which package? Thanks Anne ---------------------------------------------------- Anne Piotet Tel: +41 79 359 83 32 (mobile) Email: anne.piotet@m-td.com

As I have previously asked, in response to a similar question: Is this a homework problem? cheers, Rolf Turner rolf at math.unb.ca

Dimitris Rizopoulos wrote: > look at ?rowMeans; you can also use "apply(mat, 1, mean)" but > rowMeans() is better. By my reading of the question, this is not what Ezhil wants. He said: ``I have a 992 x 74 matrix. I would like to form a new matrix by averaging each 4 rows from the original one.'' I.e. he wants (I think) the first row of the new matrix to be the