similar to: derivatives in R

Why fails nls with "singular gradient" here? I post a minimal example on the bottom and would be very happy if someone could help me. Kind regards, ########### # define some constants smallc <- 0.0001 t <- seq(0,1,0.001) t0 <- 0.5 tau1 <- 0.02 # generate yy(t) yy <- 1/2 * ( 1- tanh((t - t0)/smallc) * exp(-t / tau1) ) + rnorm(length(t))*0.01 # show the curve

Dear R listers, I am learning the MLE utility optim() in R to program ordered logit models just as an exercise. See below I have three independent variables, x1, x2, and x3. Y is coded as ordinal from 1 to 4. Y is not yet a factor variable here. The ordered logit model satisfies the parallel regression assumption. The following codes can run through, but results were totally different from what I

Hi, the function outer can not apply a constant function as in the last line of the following example: > xg <- 1:4 > yg <- 1:4 > fxyg <- outer(xg, yg, function(x,y) x*y) > fconstg <- outer(xg, yg, function(x,y) 1.0) Error in outer(xg, yg, function(x, y) 1) : dims [product 16] do not match the length of object [1] Of course there are simpler ways to construct a constant

I have a file: x y z 0.025 0.025 1.65775 0.025 0.050 1.62602 0.025 0.075 1.63683 0.025 0.100 1.91847 0.025 0.125 2.00913 0.025 0.150 1.82222 0.025 0.175 1.70901 0.025 0.200 1.39759 0.025 0.225 1.39089 0.025 0.250 1.04762 If I read the file like this: data<-read.table("file.dat") How do I access the vectors x,y,z that are inside the dataframe data? I studied Venables and

> Or is this a bad idea? I don't like the proposal. I have seen code like the following (in fact, I have written such code, where I had forgotten a function was not vectorized) where the error would have been discovered much later if outer() didn't catch it. > outer(1:3, 11:13, sum) Error in outer(1:3, 11:13, sum) : dims [product 9] do not match the length of object [1]

thumbnailer/swfdec-thumbnailer.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) New commits: commit a31d0686b78df2da96b9d8d1e3220e63978bba30 Author: Benjamin Otte <otte at gnome.org> Date: Wed Nov 7 20:02:04 2007 +0100 s/swfdec_player_get_image_size/swfdec_player_get_default_size/ diff --git a/thumbnailer/swfdec-thumbnailer.c b/thumbnailer/swfdec-thumbnailer.c index

Hi I need to find the Hessian matrix for a complicated function from a certain kind of data but i keep getting this error Error in f1 - f2 : non-numeric argument to binary operator the data is given by U<-runif(n) Us<-sort(U) tau1<- 2 F1tau<- pgamma((tau1/theta1),shape,1) N1<-sum(Us<F1tau) X1<- Us[1:N1]

I?m trying to run a logistic joinpoint regression utilising the ljr package. I?ve been using the forward selection technique to get the number of knots for the analysis, but I?m uncertain as to my results and the interpretation. The documentation is rather brief ( in the package and the stats in medicine article is quite technical) and without any good examples. At the moment I?m thinking 1)find

Simple question. For a simple linear regression, I obtained the "standard error of predicted means", for both a confidence and prediction interval: x<-1:15 y<-x + rnorm(n=15) model<-lm(y~x) predict.lm(model,newdata=data.frame(x=c(10,20)),se.fit=T,interval="confidence")$se.fit 1 2 0.2708064 0.7254615

Hello,   ###  I created two classes "A" and "B". "A" is the superclass of "B".   setClass("A", representation(s1="numeric"),prototype=prototype(s1=8)) setClass("B",contains="A",representation(s2="character"),prototype=list(s2="hi")) myA=new("A") myB=new("B")   ####  I created

Hi Can we get the result of an intigration without the absolute error? for example f1<-function(x1){(1/gamma(alpha))*x1^(alpha-1)*exp(-x1)*log(x1)} I1<-integrate(f1, 0, (max(cc)-tau1+(theta2/theta1)*tau1)/theta2) I1 0.08007414 with absolute error < 7.2e-05 I need the answer 0.08007414 withou the other part(with absolute error <7.2e-05) how can we do that? thank you and take care

Hi, I have a set of x,y data points and each data point lies between (0,0) and (1,1). Of this set I have selected all those that lie in the lower triangle (of the plot of these points). What I would like to do is to divide the region (0,0) to (1,1) into cells of say, side = 0.01 and then count the number of cells that contain a point. My first approach is to generate the coordinates of these

Dear R users: I was trying to fit a HMM with mixture of Gaussian into the dataset, and I tried to implement it by R2Winbugs. But I got the following errer. * Error in FUN(X[[1L]], ...) : .C(..): 'type' must be "real" for this format* Does anybody know what's the problem? Does R2Winbugs accept some matrix as inits? I would really appreciate your help. Thank you very much.

Dear R help; I'll preface this by saying that the example I've provided below is pretty long, turgid, and otherwise a deep dive into a series of functions I wrote for a simulation study. It is, however, reproducible and self-contained. I'm trying to do my first simulation study that's quite big, and so I'll say that the output of this simulation as I'd like it to be is

Hi all, I'm trying to define and log-likelihood function to work with MLE. There will be parameters like mu_i, sigma_i, tau_i, ro_i, for i between 1 to 24. Instead of listing all the parameters, one by one in the function definition, is there a neat way to do it in R ? The example is as follows: ll<- function(mu1=-0.5,b=1.2,tau_1=0.5,sigma_1=0.5,ro_1=0.7) { if (tau1>0 &&

Dear R-users, Suppose I have defined a likelihood function as ll(tau), how can I plot this likelihood function by calling it by plot? I want to do it like this: ll <- function(tau) { w <- 1 / (s^2 + tau^2) mu <- sum(theta * w) / sum(w) -1/2*sum((theta-mu)^2*w -log(w)) } plot(ll, 0, 2) But have the following error: Error in xy.coords(x, y, xlabel, ylabel, log) :

Hi All, I'm new to R and am trying to run a unit root test on the vector "y" (a time series of inflation (i.e. changes in the Consumer Price Index quarter on quarter)). I've run the Augmented-Dickey-Fuller Test below (R's URCA package). It gives me an error that it cannot find the function ur.df unless I comment out the third last line of code (see below). I try to call

I'm calling a list of symbols and then using a function to build a data frame from that symbol list. It works great until I introduce this index symbol from yahoo '^GSPC'. When and index symbol is introduced I get and error which is below. > Data <- symbolFrame(symbols) Error in get(S) : object '^GSPC' not found Since R does not like the ^ in front of a name it

To use the modify the solution from Tony and I so that you can pass the name of the function, rather than the function itself, like this: x <- 7 fx <- function(y) print(x*y) f <- function(fun, x) { fun <- get(fun) environment(fun) <- environment() do.call("fun",list(3)) } f("fx",2) --- Date: Thu, 11 Mar 2004 08:22:45 +0100 From:

I have two data sets about lots of companies' stock and fiscal data. One is monthly data with about 144,000 lines, and the other is quaterly with about 56,000. Each data set takes different company code. I need to merge these two together. I read both ask cvs. And the other file with corresponding firm code. Now I have three data sets. return$PERMNO, account$GVKEY. id is the data frames