search for: 0.9999

dears R-users, I'm interested in model selection problem, and i have faced some problems that i would like to ask for help. well, this is a very small example with 4 variable (just one var. is the response - z) with 100 individuals i would like to do a stepwise search, for the "best" model, and a use BIC criteria. I know when I have a lot of variables, let's say 120, I know,

Hi, Newbie question about the pls package. Setup: Mac OS 10.3.9 R: Aqua GUI 1.01, v 2.0.1 I want to get R^2 and Q^2 (LOO and Leave-10-Out) values for each component for my model. I was running into a few problems so I played with the example a little and the results do not match up with the comments in the help pages. $ library(pls) $ data(NIR) $ testing.plsNOCV <- plsr(y ~ X, 6, data =

Dear List, This inquiry probably does not directly pertain to R. I am using library(evir) to learn EVT. Based on my reading of things, it is my understanding that if one wants to calculate quantiles of GPD, one could use either riskmeasures() or qgpd(). However, using data(danish) as an example, the quantile estimates produced by riskmeasures() are considerably different from those produced by

My routine (below) works OK but misbehaves if the on-screen plot is made wider using the mouse. The problem is caused by using par("usr")[1] - 0.07 * (par("usr")[2] - par("usr")[1]) to locate two items on the y-axis. The rest of the labeling is controlled by the "line=0" parameter setting. Of course resizing changes the absolute plot width, and

Thanks Ben for verifying the issue. It is always reassuring to hear when others can reproduce the problem. I wrote a small patch that fixes the issue (https://github.com/r-devel/r-svn/pull/11): diff --git a/src/nmath/qnbinom.c b/src/nmath/qnbinom.c index b313ce56b2..d2e8d98759 100644 --- a/src/nmath/qnbinom.c +++ b/src/nmath/qnbinom.c @@ -104,6 +104,7 @@ double qnbinom(double p, double size,

Dear all, I have a question concerning the ks.test() function. I tryed to calculate the example given on the German wikipedia page. xi <- c(9.41,9.92,11.55,11.6,11.73,12,12.06,13.3) I get the right results when I calculate: ks.test(xi,pnorm,11,1) Now the question: shouldn't I obtain the same or a very similar result if I commpare the sample and a calculated sample from the distribution?

Hello: I have a vector with 120,000 reals between 0.00000 and 0.9999 They are not sorted but the vector index is the time-order of my measurements, and therefore cannot be lost. How do I use R to find the starting and ending index of ANY and ALL the "series" or "sequences" in that vector where ever there are 5 or more members in a row between 0.021 and 0.029 ? For

Hi Martin, thanks for verifying. I agree that the Cornish-Fisher seems to struggle with the small size parameters, but I also don't have a good idea how to replace it. But I think fixing do_search() is possible: I think the problem is that when searching to the left y is decremented only if `pnbinom(y - incr, n, pr, /*l._t.*/TRUE, /*log_p*/FALSE)) < p` is FALSE. I think the solution is

>>>>> Constantin Ahlmann-Eltze via R-devel >>>>> on Mon, 10 Aug 2020 10:05:36 +0200 writes: > Thanks Ben for verifying the issue. It is always reassuring to hear > when others can reproduce the problem. > I wrote a small patch that fixes the issue > (https://github.com/r-devel/r-svn/pull/11): > diff --git

Hi the following gave me a shock: > atan(2) [1] 1.107149 > atan(2+0i) [1] -0.4636476+0i > or, perhaps more of a gotcha: > atan(1.0001+0i) [1] -0.7853482+0i > atan(0.9999+0i) [1] 0.7853482+0i > evidently atan()'s branch cuts aren't where I thought they were. Where do I look for documentation on this? -- Robin Hankin Uncertainty Analyst National

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))

Dear r-users,   I conducted a chi-square test using chi.test code and found that my p-value = 1.  My question is, is it possible to get the decimals form of the p-value?  Maybe the p-value is actually 0.9999 before rounding the value.   thank you so much for any help given.   [[alternative HTML version deleted]]

Could someone please point me to where information about the two entries %\VignetteDepends{} and %\VignettePackage{} are documented? I'm getting an error with %\VignetteDepends{} with my package name in braces. Looking at the vignettes on my system, some include the package name and some don't. Should it be there? If so, why might I be getting an error doing so? $ R CMD check SuperCurve

Happy new year everybody, I'm getting the following while trying to use the agrep() function: > pattern <- "XXX" > subject <- c("oooooo", "oooXooo", "oooXXooo", "oooXXXooo") > max <- list(ins=0, del=0, sub=0) # I want exact matches only > agrep(pattern, subject, max=max) [1] 4 OK > max$sub <- 1 # One allowed

Hello, I've encountered a very weird issue with the method subset(), or maybe this is something I don't know about said method that when you're subsetting based on the columns of a data frame you can only use constants (0.1, 2.3, 2.2) instead of variables? Here's a look at my data frame called 'ea.cad.pwr': *>ea.ca.pwr[1:5,] MAF OR POWER 1 0.02 0.01 0.9999 2 0.02

help(qt) states that: "ncp non-centrality parameter delta; currently except for rt(), only for abs(ncp) <= 37.62" so I would expect that calling qt with non-centrality parameter exceeding 37.62 should fail, instead e.g. calling > mapply(function(x) qt(p = 0.9, df = 55, ncp = x),35:45) gives: [1] 40.21448 41.35293 42.49164 43.68862 44.82945 45.97048 47.11170 48.25310 [9]

In the first example you are performing a one-sample test against a continuous cumulative distribution (in this case a normal distribution). In the second case you are performing a two-sample test. You drew your values for x non-randomly by specifying fixed intervals along a normal distribution, but ks.test() just sees that you have provided two samples, not one sample and values along a

Stock returns and other financial data have often found to be heavy-tailed. Even Cauchy distributions (without even a first absolute moment) have been entertained as models. Your qq function subtracts numbers on the scale of a normal (0,1) distribution from the input data. When the input data are scaled so that they are insignificant compared to 1, say, then you get essentially the

Hi List, I have to compare a relationship between y and x for two locations. I found logistic regression fits both datasets well, but I am not sure how to test if relationships for both sites are significantly different. I searched the r site, however no answers exactly match the question. I used Tukey's HSD to compare two means, but the relationship in my study was not simply linear. So I

Dear, I would like to post the following question to the r-help on Nabble (thanks in advance for the attention, Gustavo Vieira): Hi there. I have a data set on hands with 5,220 cases and I'd like to automate some procedures (but I have almost no programming knowledge). The data has some continuous variables that are grouped by 2 others: the name of species and the locality where they were