similar to: no implicit call of the print function within loops?

A colleague pointed out the following error in D(): f <- expression(exp(-2*x*y)) Dx <- D(f, 'x') Dy <- D(f,'y') Dxy <- D(Dx, 'y') Dyx <- D(Dy, 'x') Then > Dx # this is right -exp(-2 * x * y) * (2 * y) > Dy # this is right exp(-2 * x * y) * (-2 * x) > Dxy # this is wrong in the sign of the second term!!! -exp(-2 * x * y) *

>>>>> "VW" == Viechtbauer Wolfgang (STAT) <Wolfgang.Viechtbauer at STAT.unimaas.nl> >>>>> on Fri, 26 Sep 2008 18:00:19 +0200 writes: VW> Hi all, I'm trying to fit a marginal (longitudinal) VW> model with an exponential serial correlation function to VW> the Orange tree data set. However, R crashes frequently VW>

Dear Friends. I found something very puzzling with constOptim(). When I change the parameters for ConstrOptim, the error messages do not seem to be consistent with each other: > constrOptim(c(0.5,0.3,0.5), f=fit.error, gr=fit.error.grr, ui=ui,ci=ci) Error in constrOptim(c(0.5, 0.3, 0.5), f = fit.error, gr = fit.error.grr, : initial value not feasible > constrOptim(c(0.5,0.9,0.5),

Hello R Help, I am trying solve an MLE convergence problem: I would like to estimate four parameters, p1, p2, mu1, mu2, which relate to the probabilities, P1, P2, P3, of a multinomial (trinomial) distribution. I am using the mle2() function and feeding it a time series dataset composed of four columns: time point, number of successes in category 1, number of successes in category 2, and

Dear listers, I am trying to fit a model using nlsList() using alternately a SSfol() selfstart function or its developped equivalent formulae. This preliminary trial works well mydata<-groupedData(Conc~Tps|Organ,data=mydata) mymod1<-nls(Conc~SSfol(Dose,Tps,lKe,lKa,lCl),data=mydata) as well as a developped form: mymod2<-nls(Conc~Dose * exp(lKe+lKa-lCl) *

# This works fine: > a <- 1 > b <- 2 > c <- 3 > E <- expression(a * exp(b*X) + c) > X <- c(0.5, 1.0, 2.0) > eval(E) [1] 5.718282 10.389056 57.598150 > D(E, "b") a * (exp(b * X) * X) > eval(D(E, "b")) [1] 1.359141 7.389056 109.196300 # But if (a,b,c) are replaced with (A[1], A[2], A[3]), how can I get a derivative

Error in model.frame When I run the following nls model an error message appears and I dont know how to solve that. Could you help me?? mat = c(1,2,3,4,5,6,7,8,9,12,16,24,36,48,60) for (i in 1:length(j30)) { bliss = nls(c(j[i,1:length(mat)]) ~ b0 + b1*((1-exp(-k1*mat))/(k1*mat)) + b2*(((1-exp(-k2*mat))/(k2*mat))-exp(-k2*mat)), start = list(k1=0.1993, k2=0.1993, b0= 22.0046,

HI, As I'm trying to compute Taylor series, I'm having problems in adding and multiplying unevaluated expressions. I searched for a solution but found none. my Taylor function works fine for evaluating functions as you can see here: rTaylorVal=function(exp,x0,dx,n) { ls=list(x=x0) newexp=eval(exp,ls) exp0=exp for (i in 1:n){ exp0=D(exp0,"x")

Full_Name: Joerg Polzehl Version: 2.3.1 OS: x86_64, linux-gnu Submission from: (NULL) (62.141.176.22) I observed an incorrect behavior of function deriv when evaluating arguments of dnorm deriv(~dnorm(z,0,s),"z") expression({ .value <- dnorm(z, 0, s) .grad <- array(0, c(length(.value), 1), list(NULL, c("z"))) .grad[, "z"] <- -(z * dnorm(z))

I have the following problem. I have an automatically generated named list with "stringified" names: a <- list("A"=..., "B"=..., "C"=..., ) then I want to refer to the elements of the list, stored as an vector of names: nn <- c("A", "B", "C"), so that I could get list elements like a$nn[1], a$nn[2], etc. Obviously it

hi list, used versions: 2.12.1 and 2.14.0 under ubuntu and macosx. I recently stumbled over a problem with `nls', which occurs if the model is not specified explicitly but via an evaluation of a 'call' object. simple example: 8<-------------------------------------------------------------------------------------- nlsProblem <- function (len = 5) {

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Hi, I am trying to convert the following NLMIXED code to NLME, but am running into problems concerning 'Singularity in backsolve'. As I am new to R/S-Plus, I thought I may be missing something in the NLME code. NLMIXED *********** proc nlmixed data=kidney.kidney; parms delta=0.03 gamma=1.1 b1=-0.003 b2=-1.2 b3=0.09 b4=0.35 b5=-1.43 varu=0.5; eta=b1*age+b2*sex+b3*gn+b4*an+b5*pkn+u;

Hi R-experts, suppose I have a list with containing data frame elements: [[1]] (Intercept) y1 y2 y3 y4 -6.64 0.761 0.383 0.775 0.163 [[2]] (Intercept) y2 y3 -3.858 0.854 0.834 Now I want to put them into ONE dataframe like this: (Intercept) y1

Ok I guess it's time to ask. So I want to plot my data. It's my data from a frequency table, "temp". My formula is just a Gaussian eq. I have done the nls function to get my parameters and now I want to do the whole plot (...) and then lines(..) This is what I have done. > temp bin x 1 -4.0 0 2 -3.9 0 3 -3.8 0 4 -3.7 0 5 -3.6 0 6 -3.5 0 .... and so

So thanks for the help, I have a matrix (AB) which in the first column has my bin numbers so -4 - +4 in 0.1 bin units. Then I have in the second column the frequency from some data. I have plotted them and they look roughly Gaussian. So I want to fit them/ find/optimize "mu", "sigma", and "A". So I call the nls function : nls_AB <- nls(x ~ (A/sig*sqrt(2*pi))*

Hello, I probably have a syntax error in trying to generate an expected survival curve from a weighted cox model, but I can't see it. I used the help sample code to generate a weighted model, with the addition of a "weights=albumin" argument (I only chose albumin because it had no missing values, not because of any real relevance). Below are my code with the resulting error

I am having trouble with optim. It claims to have converged to a minimum, yet it has in the course of the optimization visited many points which are closer to optimal. I would be grateful for any explanation of this behaviour. I'm trying to estimate the parameters in the model X ~ Binomial(1,p) * NegBin(mu,theta). So I define a log likelihood function, and invoke optim thus: o <- optim

I would like to be able, inside a function, to create a new function, and use it as part of a formula as an argument to, say, gnls or nlme. for example: MyTop <- function(data=dta) { Cexp <- function(dose,A,B,m){...} Model <- as.formula(paste("y","~ Cexp(",paste(formals(Cexp),collapse =", "),")")) MyCall <-

List gurus, I'm trying to code a Gompertz growth curve function as part of a larger project and have run across a problem due to my ignorance of environments. Some sample data and the function are as follows: growth <- data.frame(age = c(1.92, 3, 5.83, 3.17, 15.5, 1.17, 5.58, 13.33, 14.29, 5.83, 13.79, 6.33, 13.75, 16.83, 13, 11.67, 0.25, 1.73, 9.46, 5.67), length = c(157, 165, 179,