search for: den

...ndication on its console, other than it looks like it's waiting at a login prompt. I did some Googling, which suggested similar issues have been around a while, but the results I found tended to be for CentOS 6 and older kernels. Can anyone help me understand what this means? Jul 31 16:58:15 den-nagios kernel: ------------[ cut here ]------------ Jul 31 16:58:15 den-nagios kernel: WARNING: at lib/list_debug.c:33 __list_add+0xac/0xc0() Jul 31 16:58:15 den-nagios kernel: list_add corruption. prev->next should be next (ffff880174795358), but was 64756f6c6373646e. (prev=ffff880806a44458). J...

Dear all, I created a bivariate normal distribution: set.seed(138813) n<-100 x<-rnorm(n); y<-rnorm(n) and plotted a scatterplot of it: plot(x,y) Now I'd like to add the 2D-standard deviation. I found a thread regarding plotting arbitrary confidence boundaries from Pascal H?nggi http://www.mail-archive.com/r-help at r-project.org/msg24013.html which cites the even older thread http://tolstoy.newcastle.edu.au/R/help/03b/5384.html As I am unfortunately only a very poor R programmer, the code of Pascal H?nggi is a myth to me and I am not sure...

In the following code, the only difference between the two plots is the order the variables are plotted. In this case, the plot of "cdata.den" in plot #1 is different from its plot in #2. Specifically, "cdata.den" spans the x-axis from -5 to 30 in plot #1 and from 0 to 20 in plot #2. Does anyone understand why these two plots do not yield the same result? #Make density objects pre.den<- density( preclaims[p...

Hi All, I have a for loop inside the function and I cannot get UUU to give me an updated grid.dens object when I run the function (it does update when I run just the for loop). Here's a simplified version of my function: UUU=function(pop, grid.dens) { for (i in 1:10){ Food=grid.dens[pop$yloc[i],pop$xloc[i]] #use initial grid.dens values Consumed=(pop$weight[i]*0.25) Left=Food-Consumed...

...is a KDE beginner's question. I have this distribution: > length(cap) [1] 200 > summary(cap) Min. 1st Qu. Median Mean 3rd Qu. Max. 459.9 802.3 991.6 1066.0 1242.0 2382.0 I need to compute the sum of the values times their probability of occurence. The graph is fine, den <- density(cap, from=min(cap), to=max(cap), give.Rkern=F) plot(den) However, how do I compute sum(values*probabilities)? The probabilities produced by the density function sum to only 26%: > sum(den$y) [1] 0.2611142 Would it perhaps be ok to simply do > sum(den$x*den$y) * (1/sum...

...is started at bootup from a link in the rc3.d dir that looks like this on the firewall: ---/etc/init.d/asmb--- #!/bin/sh # chkconfig: 345 35 65 # description: The local samba shares starter start() { echo Starting share coyote: mount -t smbfs -o username=root,password=xxxxxxxxx //coyote.coyote.den/public /mnt/coyote mount -t smbfs -o username=root,password=xxxxxxxxx //coyote.coyote.den/root /mnt/coyoteroot mount -t smbfs -o username=root,password=xxxxxxxxx //coyote.coyote.den/home /mnt/coyotehome mount -t smbfs -o username=root,password=xxxxxxxxx //coyote.coyote.den/usr /mnt/coyoteusr...

Hello, My name is Craig and I need help plotting a "line" for a multiple linear regression in R. Here is my sample data (filename: convis.txt) Output of convis.txt is (vis and density being predictors of either avoidance or entrance): vis den avoid entrance 1 10 1 0.0000 0.0000 2 10 3 0.8750 0.0000 3 8 3 0.8180 0.0300 4 8 3 0.6670 0.0667 5 8 1 0.2110 0.0000 6 6 1 0.2500 0.0000 7 10 1 0.3000 0.0000 8 10 1 0.1050 0.0000...

Hello All, I have a panel date - here a small-scale example: df <- data.frame(cbind(rep(c("AUT","BEL","DEN","GER"),4),cbind(rep(c(1999,2000,2001,2002),4)),sample(10,16,replace=T))) names(df) <- c("country","year","x") SORT <- c("GER","BEL","DEN","AUT") I need to compute the correlation between countries in the v...

Hello, I'm trying to use the bam() function in the R mgcv package for a large set of grouped binary data. However, I have found that this function does not take data in the format of cbind(numerator, denominator) on the left hand side of the formula. As an example, consider the following dat1 <- data.frame(id=rep(1:6, each=3), num=rbinom(18, size=10, prob=0.8), den=rbinom(18, size=5, prob=0.5), x=rnorm(18)) m1.1 <- gam(cbind(num, den) ~ x+s(id, bs="re"), data=dat1, family=binomial)...

I got this figured out much faster than I thought I would. Thanks to all of your help, Peter, Mattias, Thomas and Helmut. And Thomas, that 8 partition live OS sounds right up my alley, and I will definitely check it out. The second partition is working well with `parted' and `fdisk', so I am quite pleased. Below is, again, "what I came up with". I tried to integrate each of your

Dear developers, the current implementation of hist.default() calculates 'density' (and 'intensities') as dens <- counts/(n*h) where h has been calculated before as h <- diff(fuzzybreaks) which results in 'fuzzy' values for the density, see e.g. > tmp <- hist(1:10,breaks=c(-2.5,2.5,7.5,12.5),plot=FALSE) > print(tmp$density,digits=15...

Hi, I'm using the densCols function for a scatterplot and cannot figure out 1) how to extract the computed densities, and 2) how to create a legend based that represents the upper and lower ranges of the densities. For example: movers.den <- densCols(move$x, move$y) table(movers.den) #08306B #083775 #083B7C #083D7...

...d(428) X=mvrnorm(n,m2,c4) X.pca<-princomp(X) loadings(X.pca) pc=X.pca$loadings[,1] ## Part B num1=rep(1:n) for(i in 1:n)num1[i]=pc[1]%*%X[i,1] num2=rep(1:n) for(i in 1:n)num2[i]=pc[2]%*%X[i,2] num3=rep(1:n) for(i in 1:n)num3[i]=pc[3]%*%X[i,3] num4=rep(1:n) for(i in 1:n)num4[i]=pc[4]%*%X[i,4] den=rep(1:n) for(i in 1:n)den[i]=pc%*%X[i,] r1=num1/den r2=num2/den r3=num3/den r4=num4/den MLAV=sum(r2>r1 & r2>r3 & r2>r4) MLAV Best regards, ayu -- View this message in context: http://n4.nabble.com/How-to-repeat-for-function-tp1458806p1458806.html Sent from the R help mailing...

In The American Statistician vol 42 (1988) pages 257 - 280, Yoav Benjamini investigates some variations on the box plot, including vaseplots, which maek the width of each box vary proportionally to he estimated density at a particular point. Has anyone implemented these in R ? Thanks as always Peter

Hi all, I've got a density graph made with the following commands: win.graph(width=13,height=6) par ( fin=c(13,3) ,mai=c(1,1,0.5,0.5) ,mfrow=c(1,2) ,cex.axis=1.5 ,cex.lab=1.5) dens<-density(DATA1.y[2,]-mean(DATA1.y[2,]),kernel="gaussian") xlimit<-range(dens$x) ylimit<-range(dens$y) hist( DAT...

...ry(maxLik) fr <- function(x) { con1 = rbind(x[1],x[5],x[6]) con2 = rbind(x[2],x[5],x[6]) con3 = rbind(x[3],x[5],x[6]) con4 = rbind(x[5],x[6]) rho = exp(x[7])/(1+exp(x[7])) ey = exp((yop%*%con1)/rho) ed = exp((dan%*%con2)/rho) eh = exp((hil%*%con3)/rho) ew = exp((wt%*%con4)/rho) ev = ey+ed+eh+ew den=(ey+ed+eh+ew) iv = rho*log(den) pp=exp(x[4]+iv)/(1+exp(x[4]+iv)) pr1 =pp*(ey/den) pr2 =pp*(ed/den) pr3 =pp*(eh/den) pr4 =pp*(ew/den) pnp=1/(1+exp(x[4]+iv)) likelihood = (pnp*yogurt[,1])+(pr1*yogurt[,2])+(pr2*yogurt[,3])+(pr3*yogurt[,4])+(pr4*yogurt[,4]) lsum = log(likelihood) return(colSums(lsum))...

So i successfully upgraded my Debian vm''s to Debian 6 on XCP 1.0, but when i try to install a new clean one it always fails. Why is this? The VM won''t even start, while i used the Debian 6 squeeze template. Is this even possible? Thanks, Peter _______________________________________________ Xen-users mailing list Xen-users@lists.xensource.com

Hai - kann mir jemand sagen, wie ich den Modalwert in R berechne?! IRgendwie finde ich den Befehl nicht.... greetz und herzlichen Dank Sonja

Hi there, Does anyone know how to extract elements from the table returned by Manova()? Using the univariate equivalent, Anova(), it's easy: a.an<-Anova(lm(y~x1*x2)) a.an$F This will return a vector of the F-values in order of the terms of the model. However, a similar application using Manova(): m.an<-Manova(lm(Y~x1~x2)) m.an$F Returns NULL. So does any attempt at calling the

...s of this project and while comparing quick and dirty grid-search-methods and more exact optim()/optimize()-based methods to find the maximum of a part of the RLR-Test-Statistic i stumbled upon the following problem: It seems to me that R produces different results depending on whether originally identical numbers involved in the exact same computations are read from a matrix or a list. (I need both in order to do quick vectorized computation for the grid-search with matrices and "list-based" computation so that i can put the function to be maximized in something like mapply(...,optim(...