similar to: FW: NaN from function

Dear Helpers, I wrote a simple function to standardise variables if they contain more than one value. If the elements of the variable are all identical, then I want the function to return zero. When I submit variables whose elements are all identical to the function, it returns not zero, but NaNs. zt=function(x){if (length(table(x)>1)) y=(x-mean(x))/sd(x) else if (length(table(x)==1)) y=0;

Dear all; I have a function written in R that returns as a list of values as output that has associated some user defined attributes to it. How can hide these attributes when printing the output on screen? I'm using R-2.8.1 on WinXP....it's like hiding the attr of the output from the scale function.... Thanks in advance PM

Hello, I'm trying to develop a box plot of time series data to look at the range in the data values over the entire period of record. My data initially starts out as a list of hourly data, and then I've been using this code to make this data into the final ts array. # Read in the station list stn.list <- read.csv("/home/kbennett/fews/stnlist3", as.is=T, header=F) # Read in

On 21/03/2005, at 10:09 PM, David Firth wrote: > I am sorry that I wasn't clear. All that I meant was that *this* > problem can result in different behaviour in "ordinary" statistical > applications. For example, if the objective function in a call to > optim() involves calling one of these linear algebra routines, the > result may be NaN (on systems other than Mac

Greetings. I somehow had the impression that an infinite number, as obtained by dividing by zero, for instance, would be flagged as both missing ("NA") and not a number ("NaN"). It appears that I was wrong on both counts, although the is.finite function correctly returns FALSE in such a case. Please see the appended for some details. I guess that the bottom line is that R

As a side note, Splus makes sin(x) NA, with a warning, for abs(x)>1.6*2^48 (about 4.51e+14) because more than half the digits are incorrect in sin(x) for such x. E.g., in R we get: > options(digits=16) > library(Rmpfr) > sin(4.6e14) [1] -0.792253849684354 > sin(mpfr(4.6e14, precBits=500)) 1 'mpfr' number of precision 500 bits [1]

Hi, The code below is giving me this error message: Error in while (err > eps) { : missing value where TRUE/FALSE needed In addition: Warning messages: 1: In dnbinom(x, size, prob, log) : NaNs produced 2: In dnbinom(x, size, prob, log) : NaNs produced I know from the help files that for dnbinom "Invalid size or prob will result in return value NaN, with a warning", but I am not able

This question is more out of curiosity than a complaint or suggestion, but I'm just wondering. The behavior of R on calculations that result in NaN seems a bit inconsistent. # this is expected: > 0/0 [1] NaN # but this gives a warning > sin(Inf) [1] NaN Warning message: In sin(Inf) : NaNs produced # and this again does not > exp(NaN) [1] NaN Conceptually, I like to think that R

I'm having trouble grokking complex NaN's. This first set examples using complex(re=NaN,im=NaN) give what I expect > Re(complex(re=NaN, im=NaN)) [1] NaN > Im(complex(re=NaN, im=NaN)) [1] NaN > Arg(complex(re=NaN, im=NaN)) [1] NaN > Mod(complex(re=NaN, im=NaN)) [1] NaN > abs(complex(re=NaN, im=NaN)) [1] NaN and so do the following > Re(complex(re=1,

Hi All, I have some data where I am doing fairly simple calculations, nothing more than adding, subtracting, multiplying and dividing. I’m running into a problem when I divide one variable by another and when they’re both 0 I get NaN. I realize that if you divide a non-zero by 0 then you get Inf, which is, of course, correct. But in my case I never get Inf, just NaN because of the structure

Hi everyone, ----------------------------- Coefficients: ar1 ar2 ma1 ma2 sar1 intercept drift 1.5283 -0.7189 -1.9971 0.9999 0.3982 0.0288 -9e-04 s.e. 0.0869 0.0835 0.0627 0.0627 0.1305 NaN NaN sigma^2 estimated as 0.04383: log likelihood = 4.34, aic = 7.32 Warning message: NaNs produced in: sqrt(diag(object$var.coef))

Hi all I'm getting a NaN returned on using dffits, as explained below. To me, there seems no obvious (or non-obvious reason for that matter) reason why a NaN appears. Before I start digging further, can anyone see why dffits might be failing? Is there a problem with the data? Consider: # Load data dep <-

Full_Name: Bill Simpson Version: 0.64.1 OS: linux Submission from: (NULL) (193.62.250.209) Here is the data file: x y z 1 1 1 1 2 2 2 1 NaN 2 2 4 >data<-read.table("~/junk.dat",header=TRUE) > data x y z 1 1 1 1 2 1 2 2 3 2 1 NaN 4 2 2 4 > matrix(data$z,length(y),length(x)) [,1] [,2] [1,] 1 4 [2,] 2 3 This is not the correct matrix. It seems

Hi! In a current project, I am fitting loess models to subsets of data in order to use the loess predicitons for normalization (similar to what is done in many microarray analyses). While working on this I ran into a problem when I tried to predict from the loess models and the data contained NAs or NaNs. I tracked down the problem to the fact that predict.loess will not return a value at all

Hi! I noticed that cumsum behaves different than the other cumulative functions wrt. NaN values: > values <- c(1,2,NaN,1) > for ( f in c(cumsum, cumprod, cummin, cummax)) print(f(values)) [1] 1 3 NA NA [1] 1 2 NaN NaN [1] 1 1 NaN NaN [1] 1 2 NaN NaN The reason is that cumsum (in cum.c:33) contains an explicit check for ISNAN. Is that intentional? IMHO, ISNA would be better

hi , lik <- function(nO, nA, nB, nAB){ loglik <- function(par) { p=par[1] q=par[2] r <- 1 - p - q if (c(p,q,r) > rep(0,3) && c(p,q,r) < rep(1,3) ) { -(2 * nO * log (r) + nA * log (p^2 + 2 * p * r) + nB * log (q^2 + 2 * q * r) + nAB * (log(2) +log(p) +log(q))) } else NA } loglik }

Hi I am having a problem using varImpPlot in randomForest. I get the error message "Error in plot.window(xlim = xlim, ylim = ylim, log = "") : need finite 'xlim' values" When print $importance, several variables have NaN under %IncMSE. There are no NaNs in the original data. Can someone help me figure out what is happening here? Thanks! [[alternative HTML

On Linux when I compile R 2.10.0(devel) (src/main/arithmetic.c in particular) with gcc 3.4.5 using the flags -g -O2 I get noncommutative behavior when adding NA and NaN: > NA_real_ + NaN [1] NaN > NaN + NA_real_ [1] NA If I compile src/main/arithmetic.c without optimization (just -g) then both of those return NA. On Windows, using a precompiled R 2.8.1 from CRAN I get NA for

Dear R users, When running the program below I receive the following error message: fit <- optim(parm, objective, yt = tyield, hessian = TRUE) Error in as.vector(data) : no method for coercing this S4 class to a vector I can't figure out what the problem is exactly. I imagine that it has something to do with "tyield" being a matrix. Any help on explaining what's going on

Hi, I would like to know what should I garantee about P and GGt in order to have F = Z %*% P %*% t(Z) + GGt always as a positive definite matrix. Being more precise: I am trying to find minimum likelihood parameters by using the function 'optim' to find the lowest value generated by $LogLik from the function 'fkf' (http://127.0.0.1:27262/library/FKF/html/fkf.html). The