similar to: R project

Hi, I'm new to R (and statistics) and my boss has thrown me in the deep-end with the following task: We want to evaluate the impact that sampling size has on our ability to create a robust model, or evaluate how robust the model is to sample size for the purpose of cross-validation i.e. in our current project we have collected a series of independent data at 250 locations, from which

Hello, The other day Justin Peter presented a mini program to plot a topographic map with an overlay of the worldHires. I seemed interesting so I checked the ETOPO5 site and find that there is a new file ETOPO1 with a 1 minute grid. I downloaded it and tried a similar procedure. Now the ETOPO1.gz is 1 Gb and the uncompressed file is 5 Gb. They do not fit into my laptop. I tried the following

I'm trying to connect a sip call from sipgate to Asterisk A to Asterisk B. Both are behind NAT, but port forwarded. I get the connection, but no voice - either in or out. I can call on SIP from A to B (and from B to A). Do it all the time. Asterisk A receives SIP calls from Junction and Teliax. CLI on A looks right: == Using SIP RTP TOS bits 184 == Using SIP RTP CoS mark 5 ==

I have two internal analogue extensions off a TDM400P. If the first is busy, I'd like to ring the second. So: [incoming] exten =>s,1,Answer() exten =>s,n,Dial(${mainline},60) exten =>s,n,ExecIf($["${DIALSTATUS}" = "BUSY"]?Dial(${secondline},30)) But it doesn't work because * first tries Call Waiting on the main line. Here I dial out: -- Starting

On 1.6.0.18-rc3 using app_fax.so, spandsp-0.0.5, anytime I use SendFAX asterisk restarts: [Nov 15 19:00:36] VERBOSE[17013] logger.c: -- Executing [s at fax-tx-test:1] ESC[1;36;40mNoOpESC[0;37;40m("ESC[1;35;40mSIP/nhi-rive rside-sip-00000000ESC[0;37;40m", "ESC[1;35;40mContext fax-tx-testESC[0;37;40m") in new stack [Nov 15 19:00:36] VERBOSE[17013] logger.c: --

Dear R users, I've written the following functions to implement Silverman's modality test ("Using kernel density estimates to investigate multimodality", J.R. Stat. Soc. B, 43, (1981), 97-99.), and I tested them on the Chondrite data set (Good & Gaskins, J. Amer. Stat. Ass., 75, (1980), 42-56). Values for the critical window width seem OK, which is not the case for the

Hi, I am new to clustering and was wondering why pvclust using "maximum" as distance measure nearly always results in p-values above 95%. I wrote an example programme which demonstrates this effect. I uploaded a PDF showing the results Here is the code which produces the PDF file: ------------------------------------------------------------------------------------- s <-

Dear R help, I am trying solve an MLE convergence problem: I would like to estimate four parameters, p1, p2, mu1, mu2, which relate to the probabilities, P1, P2, P3, of a multinomial (trinomial) distribution. I am using the mle2() function and feeding it a time series dataset composed of four columns: time point, number of successes in category 1, number of successes in category 2, and

Dear R Help, I have two nested negative log-likelihood functions that I am optimizing with the spg function [BB package]. I would like to perform model selection on these two objective functions using AIC (and possibly anova() too). However, the spg() function returns a list and I need a fitted model object for AIC(), ICtab() [bbmle package], or anova(). How can I perform AIC-based model

Hello R Help, I am trying solve an MLE convergence problem: I would like to estimate four parameters, p1, p2, mu1, mu2, which relate to the probabilities, P1, P2, P3, of a multinomial (trinomial) distribution. I am using the mle2() function and feeding it a time series dataset composed of four columns: time point, number of successes in category 1, number of successes in category 2, and

Hi, I'm trying to use ad.test or ad2.test to test whether a given data set is exponential. I see that one of the function inputs is "distn", but I try "exponential" and 5 other variants, but I still get the error message: ad.test(test2, distn="exponential", fit=list(estimate = 0.167685), H=NA, sim=100) Error in ad.test(test2, distn = "exponential",

Upgraded from 16.2.14 to 1.6.2.15 on Fedora 13, with aastra 9133i and 57i. On 9133i and 57i: #<extension># works for a blind transfer. Xfer<extension>Xfer doesn't! All this worked on 1.6.2.14. Nothing useful on cli, verbose 3, DEBUG. Here extension 169 answers an outside call, and tries to transfer it to 145 using the Xfer button: -- SIP/169-0000009c answered

Hi all, I have a problem with getting my code to do what I want! This is the code I have: create.means.one.size<-function(nsample,var,nboot){ mat.x<-matrix(0,nrow=nboot,ncol=nsample) for(i in 1:nboot){ mat.x[i,]<-sample(var,nsample,replace=T) } mean.mat<-rep(0,nboot) for(i in 1:nboot){ mean.mat[i]<-mean(mat.x[i,]) } sd.mean<-sd(mean.mat) return(mean.mat) } where

On Sun, 27 Aug 2023 19:54:23 +0100 John Logsdon <j.logsdon at quantex-research.com> wrote: > Not so although it did lower the gc() time to 95.84%. > > This was on a 16 core Threadripper 1950X box so I was intending to > use library parallel but I tried it on my lowly windows box that is > years old and got it down to 88.07%. Does the Windows box have the same version of R

I wonder if someone could help me adjusting the following code to parallelized snow code: #Creating a data set (not needed to be parallel) n<-100 p<-100 x<-matrix(rnorm(n*p),p) y<-rnorm(n) # Bootstrapping nboot<-1000 alpha<-0.05 rhoboot <- array(0, dim=c(p,nboot)) bootranks <- array(0, dim=c(p,nboot)) bootsamples <- array( floor(runif(n*nboot)*n+1), dim=c(n,nboot)) for

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

Hi everybody, I'm trying to analyse a set of data with a non-normal response, 2 fixed effects and 1 nested random effect with strong heteroscedasticity in the model. I planned to use the function lmer : lmer(resp~var1*var2 + (1|rand)) and then use permutations based on the t-statistic given by lmer to get p-values. 1/ Is it a correct way to obtain p-values for my variables ? (see below)

Hello, If you want to bootstrap a statistic, I suggest you use base package boot. You would need the data in a data.frame, see how you could do it. library(boot) bootMedianSE <- function(data, indices){ d <- data[indices, ] fit <- rq(crp ~ bmi + glucose, tau = 0.5, data = d) ypred <- predict(fit) y <- d$crp median(y - ypred)^2 } dat <-

I am using bootstrap and cannot figure how to tell R to ignore missing values (NA) I tried this syntax and it did not work. The name of the file is banks the column two. bootmean(banks$two,na.rm = True, conf = 90,nrep = 1000) -- View this message in context: http://r.789695.n4.nabble.com/Missing-Values-tp4643059.html Sent from the R help mailing list archive at Nabble.com.

Dear All, I may look ridiculous, but I am puzzled at the behavior of the nls with a fitting I am currently dealing with. My data are: x N 1 346.4102 145.428256 2 447.2136 169.530634 3 570.0877 144.081627 4 721.1103 106.363316 5 894.4272 130.390552 6 1264.9111 36.727069 7 1788.8544 52.848587 8 2449.4897 25.128742 9 3464.1016 7.531766 10 4472.1360 8.827367 11