search for: finition

Hi All, I think there may be some possible confusion about what allowsInf would be reporting (or maybe its just me :) ) if we did this. Consider a class "myclass", S3, for starters, with setMethod("allowsInf", "myclass", function(obj) FALSE) Then, what would myclassthing <- structure(1.5, class = "mything") myclassthing[1] <- Inf do. Assumely it

Heiner Schwarte has helped me with the AIX problem that I and Janusz Kawczak recently reported here. Heiner had found out earlier that there was a problem with the 'finite' c-library function [Heiner's test code see below] when gcc (and older versions of xlc) is used. He also found that instead, AIX defines a FINITE(.) macro itself in fp.h and that this would work in any case.

Hi all, I noticed that `range.default()` has a nice `finite = TRUE` argument, but it doesn't actually apply to Date or POSIXct due to how `is.numeric()` works. ``` x <- .Date(c(0, Inf, 1, 2, Inf)) x #> [1] "1970-01-01" "Inf" "1970-01-02" "1970-01-03" "Inf" # Darn! range(x, finite = TRUE) #> [1] "1970-01-01"

> ?is.finite `is.finite' and `is.infinite' return a vector of the same length as `x', indicating which elements are finite or not. is.finite() of a list seems to return a vector of the length of the list, but with value FALSE if any list element isn't finite. Is this intended? > l4 <- list(NA,1,2,3); l4; length(l4); is.finite(l4) [[1]] [1] NA [[2]] [1] 1

Hi, OK, if you try to explicitly make them generic, you are told that they are implicitly already generic: > setGeneric("is.finite", function(from, ...) standardGeneric("is.finite")) Error in setGeneric("is.finite", function(from, ...) standardGeneric("is.finite")) : "is.finite" is a primitive function; methods can be defined, but the

A tiny nit-pick: Seems to me that end date = NA would mean the event has not yet ended, whilst Inf would mean that the event is known to never terminate, ie: an eternal fact, or physical law. On Fri, Apr 28, 2023 at 10:12?AM Davis Vaughan via R-devel < r-devel at r-project.org> wrote: > Hi all, > > I noticed that `range.default()` has a nice `finite = TRUE` argument, > but it

Dear R-helpers, running a zeroinflated model of the following type: zinb = zeroinfl(count=response ~., x = ~ . - response, z = ~. - response, dist = "negbin", data = t.data, trace = TRUE) generates the following message: Zero-Inflated Count Model Using logit to model zero vs non-zero Using Negative Binomial for counts dependent variable y: Y 0 1 2 3 359 52 7 3 generating

The following example, extracted from pp. 38-42 of Chambers (1998) Programming with Data (Springer), works for me in S-Plus 6.2 but not R 1.9.0pat, which returns the indicated error messages: > setClass('track', representation(x="numeric", y="numeric")) [1] "track" > tr1 <- new("track", x=1:3, y=4:6) > setMethod("plot", +

This kind of error seems to surprise R users. It surprises me that it doesn't happen much more frequently. The "BFGS" method of optim() from the 1990 Pascal version of my book was called the Variable Metric method as per Fletcher's 1970 paper it was drawn from. It really works much better with analytic gradients, and the Rvmmin package which is an all-R version that adds bounds

Hi list! I'm getting an error message when trying to fit an accelerated failure time parametric model using the aftreg() function from package eha: > Error in optim(beta, Fmin, method = "BFGS", control = list(trace = > as.integer(printlevel)), : > non-finite finite-difference value [2] This only happens when adding four specific covariates at the same time in the

Hello, I'm trying to use fitdistr() from the MASS package to fit a gamma distribution to a set of data. The data set is too large (1167 values) to reproduce in an email, but the summary statistics are: Min. 1st Qu. Median Mean 3rd Qu. Max. 116.7 266.7 666.7 1348.0 1642.0 16720.0 The call I'm trying to make is: fitdistr(x,"gamma") and the error is: Error in optim(x =

Dear All: I used optim() to minimise the loglikelihood function for fitting data to negative binomial distribution. But there initial value of log-likelihood and iteration 10 value are reasonable. for example: initial value 1451657.994524 iter 10 value 47297.534905 iter 20 value -623478636.8236478 Then the iter 20 vlaue suddelnly changes to a negative value and in the end the error mesage is

Dear all, I am using the optim() function which it stops with the following error messagge: error in optim(...) non-finite finite-difference value I was wondering if somebody might suggest me a way to fix it please. Thanks in advance to all of you. Kind regards, Tom __________________________________________________ [[alternative HTML version deleted]]

Hi! I was wondering about the behavior of the range function wrt. logical NAs: > range(c(0L, 1L, NA), finite=T) [1] 0 1 > range(c(F, T, NA), finite=T) [1] NA NA The documentation is quite clear that "finite = TRUE includes na.rm = TRUE?, so that I would have assumed that these two snippets would produce the same result. - Lukas

The current situation is as follows. AIX: * I still need f77 for linking. Using ld with the suggested flags gives a binary with `exec format error'. The problem seems to be with an unreferenced __start. * The floating point (finite|isnan) stuff is strange, but under control, I think. Plain cc works, gcc has a problem and seems to need having prototypes turned off for IEEE fp.

Dear R users, I have a question about numerical integration in R. I am facing the 'non-finite function value' error while integrating the function xf(x) using 'integrate'. f(x) is a probability density function and assumed to follow the three parameter (min = 0) beta distribution for which I have estimated the parameters. The function is integrated

Dear R-help, I am doing clustering via finite mixture model. Please suggest some packages in R to find clusters via finite mixture model with continuous variables. And also I wish to verify the distributional properties of the mixture distributions by fitting the model with lognormal, gamma, exponentials etc,. Thanks in advance,  warm regards,Ms.Karunambigai M PhD Scholar Dept. of Biostatistics

Dear all, when applying the optim function the following error occured "non-finite finite-difference value" Therefore I would like to ask how one can try to handle such a problem and which strategies have proven useful. (There is only litte guidance on the help list for this kind of problem.) Thank you in advance for your efforts! Best, Martin --

Dear all, There have been one or two questions posted to the list regarding the optim error "non-finite finite-difference value [4]." The error apparently means that the 4th element of the gradient is non-finite. My question is what part(s) of my program should I fiddle with in an attempt to fix it? Starting values? Something in the log-likelihood itself? Perhaps the data

Hi I try to plot a ROC curve, using the ROC library from bioonductor (using the example in the help). Everything runs nicely except, that finally I get the error: "Error in plot.window(xlim, ylim, log, asp, ...) : need finite xlim values" --- > set.seed(123) > state <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, + 1, 1, 1) > markers <- c(1, 2, 1, 1, 2,