similar to: Solving quadratic functions

Hello, I'm looking at porting code written for Matlab to R. However, I don't have sufficient knowledge of Matlab to know whether it's feasible or appropriate. If there is anyone who has any experience in this and could provide some advice, I'd be grateful. TIA, Peter [[alternative HTML version deleted]]

Dear listers, I would like to know whether a maximum size is set for data sets in R. I'm planning on analysing data sets which have 330000 points for a sample. The response is from GCxGC output. Also, with data analysis, is there a time-cost saving by performing the analysis in Linux as against doing it in Windows XP? Many thanks, Peter [[alternative HTML version deleted]]

Hello. I am trying to do a non-linear fit using the 'nls' command. The data that I'm using is as follows pH k 1 3.79 34.21 2 4.14 25.85 3 4.38 20.45 4 4.57 15.61 5 4.74 12.42 6 4.92 9.64 7 5.11 7.30 8 5.35 5.15 9 5.67 3.24 with a transformation of pH to H <- 10^-pH When using the nls command for a set of parameters - a, b and c, I receive two sets of errors: >

Surprisingly, I played around with some test code and below actually creates equations that look correct. tempmat<-matrix(10,nrow=6,ncol=6) restrictmat<-diag(6) colnames(tempmat)<-c("AUD.l1","CHF.l1","CAD.l1","GBP.l1","EUR.l1","JPY.l 1")

I'm trying to solve a system of 3 equations as part of a sub-routine in R, ie first eqn a/x-b*sqrtx+c=log(1/dx+1/e(sqrtx); snd eqn (f*y)/z-g/y-h=-log(2/x+(z/y)/(i*x) and third eqn is of the form zz=x/(j-k(z/y) where a..k inclusive are constants, x,y,z and zz are inputs. How can this be done in R? [[alternate HTML version deleted]]

Hello. I am still a newbie in R. Excuse me if I am asking something obvious. My efforts to get an answer through browsing the mailing archives failed. I want to perform an augmented Dickey-Fuller test and to obtain AIC and BIC and to be able to impose some linear restrictions on the ADF regression so as to decide the correct order of autoregression. However I could find no obvious way to impose

I've been trying to figure out how to get the names of the parameter vector variables when inside the function that nlm calls to return the objective function value: knls <- function( theta, eqns, data, fitmethod="OLS", instr=NULL, S=NULL ) { ## print( names( theta ) ) # returns NULL ## get the values of the parameters for( i in 1:length( theta ) )

Dear R programming folks: I'm trying to finish off a package for non-linear simultaneous system estimation and I've been trying to figure out how to get the names of the parameter vector variables when inside the function that nlm calls to return the objective function value: knls <- function( theta, eqns, data, fitmethod="OLS", instr=NULL, S=NULL ) { ## print(

Hi, I am doing a small experiment to test if I can use MathJax ( official site: http://www.mathjax.org ) in R's html help pages (i.e. options(help_type='html')) and it seems working with some minor modifications. The screenshot (rendered by Firefox 4.0 beta and with STIX fonts) of the help page in html format with MathJax enabled is at the following url:

The 'systemfit' function in systemfit 0.7-3 CRAN package seems to have a problem with formulas that contain transformed (eg. log) variables. If I have my data in a data frame, apparently systemfit doesn't "pass" the information of where the variables should be taken to the transforming function. I'm not entirely sure if this is a bug or just a limitation, I was just

I've been trying to figure out how to build a list of terms from a nonlinear model (terms() returns a error). I need to compute and evaluate the partial derivatives (Jacobian) for each equaiton in a set of equations. For example: > eqn <- q ~ s0 + s1 * p + s2 * f + s3 * a > sv2 <- c(d0=3,d1=4.234,d2=4,s0=-2.123,s1=0.234,s2=2.123,s3=4.234) > names( sv2 ) [1] "d0"

Dear all, I am attempting to use RSM in order to optimise the analysis of volatile compounds. After changing two variables and performing a series of measurements, I get the following: X1 X2 resp 1 -0.8 -1.0 1101039 2 -0.8 0.0 809947 3 -0.8 1.0 814664 4 0.0 -1.4 759484 5 0.0 0.0 665916 6 0.0 0.0 654054 7 0.0 0.0 641134 8 0.0 0.0 673143 9 0.0 0.0

I've been working on a new package and I have a few questions regarding the behaviour of the nlm function. I've been (for better or worse) using the nlm function to fit a linear model without suppling the hessian or gradient attributes in the objective function. I'm curious as to why the nlm requires 31 iterations (for the linear model), and then it doesn't work when I try to add

I should'nt have sent the last mail so fast. Same problem with \eqn{u_j = a_j + b x + c x^2, \quad j=1, \ldots, r-1} {u[j] = a[j] + b*x + c*x^2 j = 1,\dots,r-1} I thought the problem in the first case could have to do with the use use of \mbox{} (with the braces) within the arguments of \eqn, but here there are none braces in the arguments of \eqn{}{}. Another case giving the

Full_Name: Ross Boylan Version: 2.2.0 OS: Linux Submission from: (NULL) (65.175.48.58) \eqn{{\bf\beta}_j}{b(j)} in my .Rd file produces this error -------------------------------------------- ! Missing $ inserted. <inserted text> $ l.7 \eqn{{\bf\beta}_j}{\bf\beta}_ jnormal-bracket5bracket-normal{b(j)} -- ! Missing $ inserted. <inserted

--=-hiYzUeWcRJ/+kx41aPIZ Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: 8bit Hi, on March 10 I filed a wishlist bug report asking for the inclusion of some changes to factanal() and the associated print method. The changes were originally proposed by John Fox in 2005; they make print.factanal() display factor correlations if factanal() is called with rotation =

'Writing R Extensions' tells you that $ needs to be escaped in Rd files outside \code and similar. So I was surprised to find that ca 80 CRAN packages have constructions like (from ISwR) \item{\code{folate}}{ a numeric vector, folate concentration ($\mu$g/l). } This does not render sensibly in non-latex conversions, and it is what we have \eqn{} for. That $ needs to be

I want to create a clean .rpm package for R built with MKL and ICC. I follow Fedora instrcutions[0] to create the package. As a base, I use the R-3.2.2.src.rpm. I am left with this error: ------------------------------------------ installing R info pages ... updating '/usr/share/info/dir' ... make[1]: Leaving directory '/home/poisonivy/rpmbuild/BUILD/R-3.2.2/doc/manual' + mv

I've got a package I would like to send out for testing and have noticed a "problem" with the \tabular portion of the help "compiler" the code for inserting vertical lines, in the tabular environment, causes the build to choke, but not the check command. C:\>rcmd build --binary -docs=all nlsystemfit \tabular{|l|c|c|c|}{ \hline Method \tab Instruments \tab

It seems that MASS suggest to judge on the basis of sum(residuals(mode,type="pearson"))/df.residual(mode). My question: Is there any rule of thumb of the cutpoiont value? The paper "On the Use of Corrections for Overdispersion" suggests overdispersion exists if the deviance is at least twice the number of degrees of freedom. Are there any further hints? Thanks. -- Ronggui