search for: 0.95

Hi, there, I cannot get accurate value for calculation. for example: ld<-sqrt(1*0.05*0.95*0.05*0.95) 0.05*0.95-ld=-6.938894e-18 0.05*0.95-ld==0 is False. I met this problem in my program, how can I handle it. Thanks. xj.

Folks, The contour() function wants x and y to be in increasing order. I have a situation where I have a grid in x and y, and associated z values, which looks like this: x y z [1,] 0.00 20 1.000 [2,] 0.00 30 1.000 [3,] 0.00 40 1.000 [4,] 0.00 50 1.000 [5,] 0.00 60 1.000 [6,] 0.00 70 1.000 [7,] 0.00 80 0.000 [8,] 0.00 90

Hi Keep your messages in the list, you increase your chance to get some answer. I changed your data to groupedData object (see below), but I did not find any problem in it. plot(wlg) gives reasonable picture and I am not such expert to see any problem with data. Seems to me, that something has to be wrong with nlsList function. > wheat.list <- nlsList(Prop ~ SSlogis(end,Asym, xmid,

Folks, A few days ago, I had asked a question on this mailing list about making a contour plot where a function z(x,y) is evaluated on a grid of (x,y) points, and the data structure at hand is a simple table of (x,y,z) points. As usual, R has wonderful resources (and subtle complexity) in doing this, and the gurus of the list showed me the way. Here's a complete working example. One might

I am using R Version 2.6.2007-11-23. I do not know if the problem exists for other values or versions I encountered a problem when searching for the number 0.94 and 0.95 in a vector of values. I illustrate it below by creating a sequence of values 2 ways and then looking for the value 0.94 (same result for 0.95). It looks like the vector generated by seq( ) is a bit off: >

Dear all, I?m having some problems getting optim with method="Nelder-Mead" to work properly. It seems like there is no way of controlling the step size, and the step size seems to depend on the *difference* between the initial values, which makes no sense. Example: f=function(xy, mu1, mu2) { print(xy) dnorm(xy[1]-mu1)*dnorm(xy[2]-mu2) } f1=function(xy) -f(xy, 0,

Hi, I have a question about the computation of confidence intervals in the zyp package, in particular using the functions zyp.sen and confint.zyp, or zyp.yuepilon. (1) I'm a bit confused about the confidence intervals given by zyp.sen and confint.zyp. When I request a certain confidence interval in the function, the R output seems to deliver another confidence interval, e.g. when I set

my programe is below: a=c(1,2,1,1,1); b=c(1,2,3,4,1); c=c(3,4,3,3,3); d=c(1,2,3,5,1); e=c(1,5,3,5,1) data.f=data.frame(a,b,c,d,e) origin.data<-data.f cor.matrix<-cor(origin.data) origin.cor<-cor.matrix m<-0 for(i in 1:(cor.matrix[1]-1)) { for(j in (i+1):(cor.matrix[2])) { if (cor.matrix[i,j]>=0.95) { data.f<-data.f[,-i]; i<-i+1 }

Hi, I am trying to run some bootstraps with the boot package. When I run it with 400 replicates it does it ok, but then I need to run the same analysis but with 89, 86, 102 and 106 samples (for four different environments), and then is when I get the error message: > mybootstrap <- boot(Datos, mystat, 2000) Error in t.star[r, ] <- statistic(data, i[r, ], ...) : number of items to

Thanks Adrian and Thierry (from the previous answer). I was aware of the all.equal function, but there is nothing similar for <= (e.g. all.smallerEqual)? cheers, jo On 05 Apr 2016, at 14:31, Adrian Du?a <dusa.adrian at unibuc.ro<mailto:dusa.adrian at unibuc.ro>> wrote: Yes, that does have to do with floating point representation. I use this function for these types of

Hi, Can anyone tell me why I am not getting the correct intervals for fixed effect terms for the following generalized linear mixed model from HPDinterval: > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252

Hi all, Can anyone tell me why I am getting different results in calculating SD of 2 numbers ? > (1.25-0.95)/2 [1] 0.15 > sd(c(1.25, 0.95)) [1] 0.2121320 # why it is different from 0.15? Regards, Send instant messages to your online friends http://uk.messenger.yahoo.com [[alternative HTML version deleted]]

Hi R-users, I have a list mylist <- list(c(0.79, 0.92, 0.91, 0.86, 0.96, 0.96, 0.95, 0.94, 0.99), c(0.28, 0.45, 0.59, 0.69, 0.80, 0.87, 0.95, 0.94, 0.98), c(0.29, 0.39, 0.59, 0.69, 0.68, 0.80, 0.93, 0.95, 0.98)) Is there a way to find the index of the list element that contains the lowest value among all the other elements? As the lowest value in each element is the first, the

Dear list I'm testing a predictor and I produced nice performance plots with ROCR package utilizing the 3 standard command pred <- prediction(predictions, labels) perf <- performance(pred, measure = "tpr", x.measure = "fpr") plot(perf, col=rainbow(10)) The pred object and the perfo object are S4 with the following slots An object of class "performance"

Hi All- This seems like such a pathetic problem to be posting about, but I have no idea why this testcase does not work. I have tried this using R 2.4.1, 2.4.0, 2.3.0, and 2.0.0 on several different computers (Mac OS 10.4.8, Windows XP, Linux). Below the signature, you will find my test case R code. My point in this folly is to take a dataframe of 300,000 rows, create a filter based on two

Dear All, I have the following problem: I have a function in which I check if the difference between values is smaller or equal to a certain threshold. I however realized that I might get there some unexpected results: > abs(1 - 0.95) >= 0.05 [1] TRUE ## So that?s fine, but: > abs(1 - 0.95) <= 0.05 [1] FALSE Apparently, abs(1 - 0.95) is not equal to 0.05, which I find however quite

Hi, I want to compute the quantiles of Chi^2 distributions with different degrees of freedom like x<-cbind(0.005, 0.010, 0.025, 0.05, 0.1, 0.5, 0.9, 0.95, 0.975, 0.99, 0.995) df<-rbind(1:100) m<-qchisq(x,df) and hoped to get back a length(df) times length(x) matrix with the quantiles. Since this does not work, I use x<-c(0.005, 0.010, 0.025, 0.05, 0.1, 0.5, 0.9, 0.95, 0.975,

Dear all, I am trying to apply the logistic regression to determine the limit of detection (LOD) of a molecular biology assay, the polymerase chain reaction (PCR). The aim of the procedure is to identify the value (variable "dilution") that determine a 95% probability of success, that is "positive"/"total"=0.95. The procedure I have implemented seemed to work looking

Habría que buscar la vuelta, yo no lo se, pero posiblemente lo siguiente da una pista. Nota: al mismo código le sume una línea al final datos<-c(2,3,4,5,6,7,8) quantile(datos) quantile(datos,probs = c(0.25, 0.75, 0.85, 0.90, 0.95)) as.matrix(quantile(datos,probs = c(0.25, 0.75, 0.85, 0.90, 0.95))) as.data.frame(quantile(datos,probs = c(0.25, 0.75, 0.85, 0.90, 0.95))) # ¿ y si solo solicita

Author: jongkis Repository: /hg/zfs-crypto/gate Revision: 8e73d99dab0952293f379aca3e88188d768b3a0b Log message: 6273535 SUNWgrub* packages have incorrect pkginfo(4) settings 6305469 READ_FAIL_STAGE2 in installgrub/message.h contains wrong message 6305481 installgrub miscounts stage2 size in sector by 1 if stage2 size is multiple of 512 6307439 make clobber in usr/src/grub and