similar to: documentation bug for isoreg example (PR#10352)

Dear list, As can be seen below, adding a NA to the y values in a call to isoreg results in a segfault. ir4 <- isoreg(1:10, y4 <- c(5, 9, 1:2, 5:8, NA, 8)) Adding missing values to the x values, on the contrary, gives an error, but maybe the error message could be tailored to this particular situation. y <- c(5, 9, 1:2, 5:8, 3, 8) x <- c(1:9, NA) isoreg(x, y) ## error message:

Full_Name: Benjamin Tyner Version: 2.6.0 RC 2007-10-01 r43043 OS: WinXP Submission from: (NULL) (171.161.224.10) platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status RC major

Full_Name: Petr Klasterecky Version: 1.8.1 OS: Windows XP, Linux Submission from: (NULL) (195.113.27.212) The isoreg() function causes R to crash when called repeatedly. Consider the following simple script: { library(modreg) N <- 10 x <- rnorm(N) print("Original x values:") print(x) for(n in (1:N)){print(y <- isoreg(x[1:n])$yf)} } I am able to run (call) it several

I believe there is a memory leak in isoreg in the current version of R, as I believe the following shows > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 120405 3.3 350000 9.4 350000 9.4 Vcells 78639 0.6 786432 6.0 392463 3.0 > for(k in 1:100) { + + y <- runif(10000) + isoreg(x,y) + } > rm(x) > rm(y) > gc() used (Mb) gc

Before re-inventing the wheel I would like to ask: does anyone know about an optimizer in R which can reliably identify which value of X (Xopt) leads to Y (Yopt) closest to Ytarget in Y <- MonotoneStepFun(X) optionally with the restriction that Yopt <= Ytarget (at least if any Y <= Ytarget, otherwise any Yopt > Ytarget would be the preferred answer) If none is known, I will write

Hi all, I'm extracting the name of the term in a regression model that dropterm specifies as the least significant one, and I'm assigning this name to an object. However, when I use update(), it ignores this object. Is there a way I can make it not ignore it? A reproducible example is below: > lm(x1~1+y1*y2+y3+y4,data=anscombe)->my.lm >

Dear R-users; Previously I posted a question about the problem of rank deficiency in summary.manova. As somebody suggested, I'm attaching a small part of the data set. #*************************************************** "test" <- structure(.Data = list(structure(.Data = c(rep(1,3),rep(2,18),rep(3,10)), levels = c("1", "2", "3"), class =

I wish to rearrange the matrix, df, such that all there are not repeated x values. Particularly, for each value of x that is reated, the corresponded y value should fall under the appropriate column. For example, the x value 3 appears 4 times under the different columns of y, i.e. y1,y2,y3,y4. The output should be such that for the lone value of 3 selected for x, the corresponding row entries

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

Hi Does rJava offer a way to instruct the JVM to perform a garbage collection? Regards Ben

I was hoping to fit along the lines of g<-gl(20,5) y<-runif(100) fit<-lme(fixed=y~g,correlation=corAR1(0,~1|g)) But I get the error "Incompatible formulas for groups in "random" and "correlation"" Any help would be greatly appreciated. Ben

I didn't do the compile; is there a way to check whether that was used? If not, I'll inquire with our sysadmin and report back. In any case, my suggestion was motivated by the fact that some parts of R use OpenMP while others do not, in the hope that the former could have their OpenBLAS omelet without breaking the OpenMP eggs, so to speak. On 01/09/2018 06:41 PM, Keith O'Hara

Hi, I would like to create a plot of y1,y2,y3,y4 against x for several subjects such that y1 and y2 are plotted against x in one panel and y3 and y4 against x in another panel. Thus if there are 3 subjects I should end up with 6 panels. Is there a simple way of doing so (i.e. without calling xyplot() several times, and then padding the results together)?? Regards S?ren

Full_Name: Ben Tyner Version: 2.2.0 OS: i686-pc-linux-gnu Submission from: (NULL) (128.210.141.240) My appologies if this has already been fixed, but I didn't see it in the tracking system yet so I thought I'd report it. Demonstration: xyplot(Petal.Length ~ Petal.Width | Species, iris, strip = strip.custom(style = 1, var.name = expression(beta),

Hi, I'm a newbie to the world of HMMs and HMMs in R. I've had a look at the hmm package and the RHmm package but I couldn't see anything straightforward on how a labelled sequential dataset with observed values and underlying states might be used to construct and train a HMM based on that data and no pre-computed values for the transition, emission or initial state distributions. Does

Using > methods("plot") [1] plot.Date* plot.HoltWinters* plot.POSIXct* [4] plot.POSIXlt* plot.TukeyHSD plot.acf* [7] plot.data.frame* plot.decomposed.ts* plot.default [10] plot.dendrogram* plot.density plot.ecdf [13] plot.factor* plot.formula* plot.hclust* [16] plot.histogram* plot.isoreg* plot.lm [19] plot.medpolish*

RNG in R and Splus 3.4 Prof. Ripley asked the details of the example. We were doing parametric bootstrap, so it is similar to simulation. Anyway here is the details. We start with a sample of 19 positive numbers. We know the sample is from truncated exp(0.3)...only the truncation point, theta, is unknown. In other words, the sample can be generated from something like x1 <- rexp(100,

RNG in R and Splus 3.4 Prof. Ripley asked the details of the example. We were doing parametric bootstrap, so it is similar to simulation. Anyway here is the details. We start with a sample of 19 positive numbers. We know the sample is from truncated exp(0.3)...only the truncation point, theta, is unknown. In other words, the sample can be generated from something like x1 <- rexp(100,

So imagine rpois is changed, such that the storage mode of its return value is sometimes integer and sometimes numeric. Then imagine the case where lambda is itself a realization of a random variable. Do we really want the storage mode to inherit that randomness? On 1/19/20 10:47 AM, Avraham Adler wrote: > Maybe there should be code for 64 bit R to use long long or the like? > > On

Hi Yves lavaan looks like a very nice package. From the tutorial introduction I see you create path diagrams for some of the models you describe. How did you do this? I don't see a function for this in the package. I know there is a path.diagram function in the sem package that uses dot to draw the diagram, but I've always found the layouts from dot somewhat strange for path diagrams