search for: pfun

...9;m still getting familiar with lapply I have this date sequence x <- seq(as.Date("01-Jan-2010",format="%d-%b-%Y"), Sys.Date(), by=1) #to generate series of dates I want to apply the function for all values of x . so I use lapply (Still a newbie!) I wrote this test function pFun <- function (x) { print(paste("This is: ",x,sep="")) } When I run it: > lapply(x,pFun(x)) It gives the result correctly.. but end with an error Error in match.fun(FUN) : 'pFun(x)' is not a function, character or symbol Question 1: What is the issue here...

While I think total compatibility is not possible or even very important I have become aware recently of some fairly low level incompatibilities that have rather serious consequences for interchange between systems. Some of these are possibly well known, but I put them on on record here just in case. (I do not expect any action on these, of course, but people may want to note them, and correct

While I think total compatibility is not possible or even very important I have become aware recently of some fairly low level incompatibilities that have rather serious consequences for interchange between systems. Some of these are possibly well known, but I put them on on record here just in case. (I do not expect any action on these, of course, but people may want to note them, and correct

Hi, I see different results in png() and pdf() for polypath() on Windows when using the "winding" rule ## overlapping, both clock-wise x <- cbind(c(.1, .1, .6, .6, NA, .4, .4, .9, .9), c(.1, .6, .6, .1, NA, .4, .9, .9, .4)) pfun <- function() { plot(x) polypath(x * 0.8 + 0.2, rule = "winding", col = "#BEBEBE80") polypath(x, rule = "winding", col = "#BEBEBE80") } ## output "windows.png/pdf" or "unix.png/pdf" label <- .Platform$OS.type png(sprintf(&q...

...different results in png() and pdf() for polypath() on Windows >> when using the "winding" rule >> >> ## overlapping, both clock-wise >> x <- cbind(c(.1, .1, .6, .6, NA, .4, .4, .9, .9), >> c(.1, .6, .6, .1, NA, .4, .9, .9, .4)) >> >> pfun <- function() { >> plot(x) >> polypath(x * 0.8 + 0.2, rule = "winding", col = "#BEBEBE80") >> polypath(x, rule = "winding", col = "#BEBEBE80") >> } >> >> ## output "windows.png/pdf" or "unix.png/p...

...> wrote: > Hi, I see different results in png() and pdf() for polypath() on Windows > when using the "winding" rule > > ## overlapping, both clock-wise > x <- cbind(c(.1, .1, .6, .6, NA, .4, .4, .9, .9), > c(.1, .6, .6, .1, NA, .4, .9, .9, .4)) > > pfun <- function() { > plot(x) > polypath(x * 0.8 + 0.2, rule = "winding", col = "#BEBEBE80") > polypath(x, rule = "winding", col = "#BEBEBE80") > } > > ## output "windows.png/pdf" or "unix.png/pdf" > label <...

...>> >> when using the "winding" rule >> >> >> >> ## overlapping, both clock-wise >> >> x <- cbind(c(.1, .1, .6, .6, NA, .4, .4, .9, .9), >> >> c(.1, .6, .6, .1, NA, .4, .9, .9, .4)) >> >> >> >> pfun <- function() { >> >> plot(x) >> >> polypath(x * 0.8 + 0.2, rule = "winding", col = "#BEBEBE80") >> >> polypath(x, rule = "winding", col = "#BEBEBE80") >> >> } >> >> >> >> ## o...

...) for polypath() on Windows > >> when using the "winding" rule > >> > >> ## overlapping, both clock-wise > >> x <- cbind(c(.1, .1, .6, .6, NA, .4, .4, .9, .9), > >> c(.1, .6, .6, .1, NA, .4, .9, .9, .4)) > >> > >> pfun <- function() { > >> plot(x) > >> polypath(x * 0.8 + 0.2, rule = "winding", col = "#BEBEBE80") > >> polypath(x, rule = "winding", col = "#BEBEBE80") > >> } > >> > >> ## output "windows.png...

....nls* plot.nmGroupedData* plot.pdMat* [31] plot.ppr* plot.prcomp* plot.princomp* [34] plot.profile.nls* plot.ranef.lme* plot.ranef.lmList* [37] plot.shingle* plot.simulate.lme* plot.spec [40] plot.stepfun plot.stl* plot.table* [43] plot.trellis* plot.ts plot.tskernel* [46] plot.TukeyHSD plot.Variogram* Non-visible functions are asterisked > pmeth <- methods("plot") > grep("plot.mgcv.smooth&...

Is there any R function that computes the convolution of the double exponential distribution? If not, is there a good way to integrate ((q+x)^n)*exp(-2x) over x from 0 to Inf for any value of q and for any positive integer n? I need to perform the integration within a function with q and n as arguments. The function integrate() is giving me this message: "evaluation of function gave a

...y x <- rnorm(5) curve(quantile(x,p), xname="p") The breakpoints for the default quantiles are n points evenly spread on [0,1], including the endpoints; i.e., for n=5, (0, .25, .5, .75, 1) So: x <- rnorm(5) br <- seq(0, 1, ,5) qq <- quantile(x, br) ## actually == sort(x) pfun <- approxfun(qq, br) (q <- quantile(x, .1234)) pfun(q) There are variations, e.g. the one-liner approx(sort(x), seq(0,1,,length(x)), q)$y -pd > On 16 Jun 2017, at 01:56 , Andras Farkas via R-help <r-help at r-project.org> wrote: > > David, > > thanks for the respo...

David, thanks for the response. In your response the quantile function (if I see correctly) runs on the columns versus I need to run it on the rows, which is an easy fix, but that is not exactly what I had in mind... essentially we can remove t() from my original code to make "res" look like this: res<-apply(z, 1, quantile, probs=c(0.3)) but after all maybe I did not explain

...y x <- rnorm(5) curve(quantile(x,p), xname="p") The breakpoints for the default quantiles are n points evenly spread on [0,1], including the endpoints; i.e., for n=5, (0, .25, .5, .75, 1) So: x <- rnorm(5) br <- seq(0, 1, ,5) qq <- quantile(x, br) ## actually == sort(x) pfun <- approxfun(qq, br) (q <- quantile(x, .1234)) pfun(q) There are variations, e.g. the one-liner approx(sort(x), seq(0,1,,length(x)), q)$y -pd > On 16 Jun 2017, at 01:56 , Andras Farkas via R-help <r-help at r-project.org> wrote: > > David, > > thanks for the respo...

hello, please can anyone help me out. Am a new user of R program. Am having problem with this code below, not getting the expected results. 1. Each m, the cumulative sum should be 1.000 but the 2nd and 3rd m returned 2.000 and 3.000 instead of 1.000. 2. to get the LCL(m) and UCL(m) for each m base on these instructions if out.cum > 0.025 then LCL(m)= y-1 if out.cum >0.975

I am estimating a large model by groups. How do you save the results and?returns the associated quantiles? For this example I need a data frame n?? ?xi??????? mu????????beta 1?? 0.1033614? 2.5389580 0.9092611 2? ?0.3401922? 0.5192882 1.5290615 3?? 0.5130798? 0.5668308 1.2105666 I also want to apply gevrlevelPlot() for each "n" or group. ? #Example n <- c(1, 1, 1, 1, 1, 1, 2, 2, 2,

...PMBuilder.Inliner = createFunctionInliningPass(Opt == CodeGenOpt::Aggressive ? 250 : 200); } pFPasses = new FunctionPassManager(pMod); pFPasses->add(new TargetData(*TD)); PMBuilder.populateFunctionPassManager(*pFPasses); pFPasses->doInitialization(); pFPasses->run(*pFun); pFPasses->doFinalization(); delete pFPasses; pMPasses = new PassManager(); pMPasses->add(new TargetData(*TD)); pMPasses->add(createCFGSimplificationPass()); pMPasses->add(createBlockPlacementPass()); PMBuilder.populateModulePassManager(*pMPasses); pMPas...

hi I have a problem with  Median in Survival. when I use S1=survfit(Surv(Time,Status)) the result shows the median but I cannot use it as numeric! S1$median in Null   Could u pleas help me. Many Many Tank You. Niloofar. [[alternative HTML version deleted]]

SEE ALSO ORIGINAL POSTING IN PR#2933 On May 6, 2003 03:58 pm, Thomas Lumley wrote: > > Looking at a wider context in the code > > pfun <- function(coef, theta, ndeath) { > if (theta == 0) > list(recenter = 0, penalty = 0, flag = TRUE) > else { > recenter <- log(mean(exp(coef))) > coef <- coef - recenter > nu <- 1/theta > li...

Hi, How can I dynamically change the code generation optimization level (e.g., None) of a JIT in other to recompile a function with a new optimization level (e.g., Default)? Thank you. Best regards, Nurudeen.

...ch was shown to lead to the same EM solution as penalty (1) -- at least > in the case of a Cox proportional hazard model (Therneau and Grambsch, > 2000. Modeling Survival Data, Extending the Cox Model. Springer, New York. > Page 254, Eq. (9.8).). Looking at a wider context in the code pfun <- function(coef, theta, ndeath) { if (theta == 0) list(recenter = 0, penalty = 0, flag = TRUE) else { recenter <- log(mean(exp(coef))) coef <- coef - recenter nu <- 1/theta list(recenter = recenter, first = (ex...