similar to: integral with error:non-finite function value

Hi, I have this lame question. I want to convert a list (each with varies in length) to matrix with same row length by eliminating vectors outside the needed range. For example: l<-list(NULL) l[[1]]=1,2,3.7 l[[2]]=3,4,5,6,3 l[[3]]=4,2,5,7 l[[4]]=2,4,6,3,2 l[[5]]=3,5,7,2 #so say I want to only have 4 rows and 5 column in my matrix (or data.frame) and eliminating the 5th index value in l[[2]]

I've been testing plotmath. But I'm getting some funny output one one computer. The problem is that characters are 'jumbled' and overstrike when symbols are introduced. Sample code: mu <- 440.0 sigma <- 12.5 myx <- seq( mu - 4*sigma, mu+ 4*sigma, length.out=500) myDensity <- dnorm(myx,mean=mu,sd=sigma) # Here's one way to retrieve the values of mu and sigma and

Hello, I''ve spent a lot of time during the past month trying to get negative multinomial regression models for clustered event counts as described in (Guang Guo. 1996. "Negative Multinomial Regression Models For Clustered Event Counts." Sociological Methodology 26: 113-132., abstract at http://depts.washington.edu/socmeth2/4abst96.htm) implemented in R. A FORTRAN version of the

Here's another example of my plotmath whipping boy, the Normal distribution. A colleague asks for a Normal plotted above a series of axes that represent various other distributions (T, etc). I want to use vectors of equations in plotmath to do this, but have run into trouble. Now I've isolated the problem down to a relatively small piece of working example code (below). If you would

Hi guys, I'm trying to use lmomco package. first I did the manual calculation on what is the estimates scale and location parameter given L-CV=0.2, L1=1000 L-moments and k (shape parameter) =- 0.1. so what i get is: location: 821.0445 scale: 260.7590 shape: -0.1000 #I assign this as GEV vectors using vec2par GEVpara2<-vec2par(c( 821.0445 , 260.7590 ,-0.1),'gev') #then I

Hi, I would like to make a replication of 10 of a linear, first order Autoregressive function, with respect to the replication of its innovation, e. for example: #where e is a random variables of innovation (from GEV distribution-that explains the rgev) #by using the arima.sim model from TSA package, I try to produce Y replicates, with respect to every replicates of e, #means for e[,1], I want

Hi, I got a small problem on how to define the vector index without manually inspect the vector. example: y=c(2,3,5,2,4,6,8,3,6,2,5) #I have ten set of this kind of vectors (with different values but same length) that I would also like to run the routine below #say; v=the first index in y where the value is larger than 4, in this case index 3, value 5 #what I would like to do is take y[1:v]

Hi R-users, I'm trying to repeat the same procedure to 1000 data set. I know this is very easy, but I got stuck finding the right and fastest way in running it. IID50=Riidf[1:50,1:1000] #where IID50 is a dataframe consist of 1000 time series(as column) and 50 time scales (row). #what I tried to do: estIID50=rep(NA,1000) for (i in 1:1000) estIID50[i]=pargev(lmom.ub(IID50[1:50,i])) #warning

Hi all, Running simulations, I'm generating market response to 2 factors X&Y.. There is no closed form for the market response.. The results are store in a matrix Z(X <- seq(.02,.98,.02), Y <- seq(.01,.19,.01)).. For optmization purpose I need to approximate the values for any factor X in 0,02-0,98 and Y in 0,01-0,19 How can I do it ? For one factor : Xn-1 < x <= Xn

Hi, I have the following problem. It has two parts. 1. I need to calculate the stationary probabilities of a Markov chain, eg if the transition matrix is P, I need x such that xP = x in other words, the left eigenvectors of P which have an eigenvalue of one. Currently I am using eigen(t(P)) and then pick out the vectors I need. However, this seems to be an overkill (I only need a single

Hello folks, Any ideas how to do this? data.frame is a data frame with column names "x1",...,"xn" y is a response variable of length dim(data.frame)[1] I want to write a function function(y, data.frame){ lm(y~x1+...+xn) } This would be easy if n was always the same. If n is arbitrary how could I feed the x1+...+xn terms into lm(response~terms)? Thanks Richard -- Dr.

Hi to all, how could I to rotate automatically a data sheet which was imported by read.xls? x1 x2 x3 .... xn y1 1 4 7 ... xn/y1 y2 2 5 8 .... xn/y2 y3 3 6 9 ....xn/y2 yn ... ... ... Xn/Yn to y1 y2 y3 .... yn x1 1 2 3 ..... Yn/x1 x2 4 5 6 .... Yn/x2 x3 7 8 9 .... Yn/x2 xn ... ... ... ..... Yn/xn Kind regards Knut

The programming language mosml comes with foldr that 'accumulates' a function f over a list [x1,x2,...,xn] with initial value b as follows foldr f b [x1,x2,...,xn] = f(x1,...,f(xn-1,f(xn,b))...) Observe that "list" should have same elements so in R terminology it would perhaps be appropriate to say that the accumulation takes place over a 'vector'. I wonder if R

Dear All, I have a data frame with n columns: X1, X2, ., Xn. Now I want to create a new column: if X1 = X2 = . = Xn, the value is 1; Otherwise, the value is 0. How to do that in a quick way instead of doing (n choose 2) comparisons? Thank you, Frank [[alternative HTML version deleted]]

Hi, I have written a small C++ function and compile it. However in R I can't see the function I have defined in C++. I have read some web-pages about Rcpp and C++ but it is a bit confusion for me. Anyway, This is the C++-code: #include <Rcpp.h> using namespace Rcpp; // [[Rcpp::export]] List compute_values_cpp(int totalPoints = 1e5, double angle_increment = 0.01, int radius =

Hello, I'm trying to figure out the proper way of passing parameters to my program that is being run by WINE. I've been trying different things I've found in Google, but none have seemed to work yet. The parameters work in a Windows environment, so I do believe the program is okay. This is on a SUSE/SLES 9 machine using WINE .917. If I want to pass the parameter

Hi all, New to R, so this may be obvious to some. I've been trying to figure this out for a while, I have a dataset "events" that looks something like this: Area NAME DATE X Xn Y 1 X 1/10/10 1 1 0 1 Y 1/11/10 0 0 1 1 X 1/12/10 1 0 0 1 X 1/12/10 1 0 0 1 X 1/12/10 1 0 0 2 X 2/12/10 1 1 0

Dear all, The documentation for `all.equal.numeric` says Numerical comparisons for ?scale = NULL? (the default) are done by first computing the mean absolute difference of the two numerical vectors. If this is smaller than ?tolerance? or not finite, absolute differences are used, otherwise relative differences scaled by the mean absolute difference. But the actual behaviour

Hello, Can anyone give me some suggestion in term of calculating the sum below. Is there a function in R that can help doing it faster? x1, x2, ...xn where xi can be 0 or 1. I want to calculate the following: sum{ beta[a+sum(xi), b+n-sum(xi) ]* [ (1-x1)dnorm(0,1)+x1dnorm(2,1) ]* [ (1-x2)dnorm(0,1)+x2dnorm(2,1) ]* ...* [ (1-xn)dnorm(0,1)+xndnorm(2,1) ] } The sum in the beginning is over all

Can someone offer some advice on how to properly evaluate a SYMSXP from a .Call ? I have the following in R: variable xn, with an attribute "mu" which references the variable mu in the global environment. I know "references" is a loose term; mu was defined in this fashion as a way to implement deferred binding: foo <- function(x,mu) { attr(x,"mu") <-