similar to: normalized kernel question:

I fit my CDF to sum of exponentials and now I want to take the numerical derivative of this function to obtain probability density.I will really appreciate your help reagrding the error messages I am getting which I don't understand. * * > fitterma <- function(xtime) { a <- -0.09144115 b <- -0.01335756 c <- -2.368057 d <- -0.00600052

Hello: In the example below (or for a censored data) using survfit.coxph, can anyone point me to a link or a pdf as to how the probabilities appearing in bold under "summary(pred$surv)" are calculated? Do these represent acumulative probability distribution in time (not including censored time)? Thanks very much, parmee *fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)*

Hello All, Does anyone know why length(fit1$time) < length(fit2$n) in survfit.coxph output? Why is the predicted time length is not the same as the number of samples (n)? I tried: example(survfit.coxph). Thanks, parmee > fit2$n [1] 241 > fit2$time [1] 0 31 32 60 61 152 153 174 273 277 362 365 499 517 518 547 [17] 566 638 700 760 791

Dear Tom, Short answer, if your using spec.pgram(), use the smoothing kernel to get a better estimate at the frequency centered in the bandwidth. If your frequency bin of interest is wider than the bandwidth of the kernel, average across frequencies (I think). The estimate appears to be normalized already. If you are calculating your PSD independently, then oversample (e.g. 2, perhaps 4 or more

Dear All, I need to write an R function which computes values of Probabilities for the (standard) normal distribution, ¦µ(z) for z > 0 by summing this power series. (We should accumulate terms until the sum doesn't change). I also need to make the function work for vector of values z. The initial fomular is ¦µ(z) = ( 1/ sqrt(2¦Ð) )* ¡Ò e^(-t^2/2)dt (¡Ò is from -¡Þ, z)

HI, I'm having trouble with a piece of Rscript which keeps outputting incorrectly. it's something like this: the code reads in from a file which contains (reformated) input >file<-read.table(file="input_file.txt",sep="\t")[,c(1,3:5)] > >file.rows<- c(nrow(file)/288) # "input_file.txt" contains 288 reformatted lines for each original data file

Hello Jean-Marc, Thanks for the quick reply and comments. On Thu, May 26, 2016 at 5:41 PM, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi Michael, > > Here's some more minor comments below. As long as you address the two > comments from my previous email (254 -> 2 and the draft name), the draft > is good for submitting as initial version on the IETF website (even

Hello, I have noticed a bug with LSSVM implementation in R. It could be a bug with the LSSVM itself that causes this problem. I thought I should post this message to see if anyone else is familiar with this problem and explain why the result is different for odd and even number of cases. Once the hyperplane is found using LSSVM, the prediction results vary when you predict odd or even number of

---------- Forwarded message ---------- From: <r-help-bounces@r-project.org> Date: Mon, May 11, 2009 at 10:24 PM Subject: The results of your email commands To: das.moumita.online@gmail.com The results of your email command are provided below. Attached is your original message. - Results: Ignoring non-text/plain MIME parts - Unprocessed: What is the function for partial

Hi, Being new to R I'm asking for some advice on how to optimize the performance of the following piece of code: > alpha_c <- function(lambda=600e-9,alpha_s=1e-14,N=400,spacing=1e-7){ > > k<-2*pi/lambda > ri<-c(0,0) # particle at the origin > x<-c(-N:N) > positions <- function(N) { > reps <- 2*N+1 > matrix(c(rep(-N:N, each = reps), rep(-N:N,

Hi all, I'm quite new to Vorbis, so please excuse me if I'm writing complete nonsense (and, if possible, tell me where I'm wrong). While searching for optimization possibilities in lib/psy.c, I found the following piece of code in _vp_compute_mask(): if(p->vi->smoothp){ /* compute power^.5 of three neighboring bins to smooth for peaks that get split twixt

I would like to add an extension to the current name of a variable to create a new variable that is its sqrt transform. Each piece of the equation below works independently, but the left side definition fails on run. I also tried creating the variable name first, but ended up with an object that toString() did not fix. Better ideas? Example 1

Hi, I run this code to get the power of the test for modified bartlett's test..but I'm not really sure that my coding is right.. #normal distribution unequal variance asim<-5000 pv<-rep(NA,asim) for(i in 1:asim) {print(i) set.seed(i) n1<-20 n2<-20 n3<-20 mu<-0 sd1<-sqrt(25) sd2<-sqrt(50) sd3<-sqrt(100) g1<-rnorm(n1,mu,sd1) g2<-rnorm(n2,mu,sd2)

Hello, I'd like to reproduce the standard biplot with pc.biplot=TRUE using xyplot in lattice in order to assign different symbols to groupings of observations (similar to the example in fig. 11.2 on page 285 in DAAG). In order to reproduce the biplots I need to know how to scale the observations and variables. In ?biplot.princomp I found that the variables are scaled by 'lambda ^

I am trying to solve the integration equation, for different values of K from 4 to 25, the integration is with respect to u, Here is the equation: gamma(k/2) / ( sqrt(k-1)*gamma((k-1)/2) ) * integrate(f= (1+u^2/k-1)^(-k/2), lower=0, upper= sqrt(a^2*k/(k+1-a^2)) ) = the similar expression as te left hand except k becomes k+1 my code is below, I don't know why R keep telling me the syntax

I'm trying to implement a recursive function using integrate, and I suspect I need a Vectorize somewhere, but I can't suss it out. Any help would be appreciated. I've tried traceback() and various debugging ideas to no avail (most likely due to my inexperience with these tools.) Here's what I have. Nk <- function(m, C) { if (length(m) > 1) { rho <- C[1, -1]

I have done a fair bit of spectral analysis, and hadn't finished collecting my thoughts for a reply, so hadn't replied yet. What exactly do you mean by normalize? I have not used the functons periodogram or spectrum, however from the description for periodogram it appears that it returns the spectral density, which is already normalized by frequency, so you don't have to worry about

Hi, dear R pros I try to understand eigen(). I have seen, that eigen() gives the eigenvectors normalized to unit length. What shall I do to get the eigenvectors not normalized to unit length? E.g. take the example: A [,1] [,2] V1 0.7714286 -0.2571429 V2 -0.4224490 0.1408163 Calculating eigen(A) "by hand" gives the eigenvectors (example from Backhaus,

Hi all, I've been working with a friend's model that is a spatial model consisting of 4 patches. She uses the code found below to add migration between the patches for the three species of concern. When I run a script incorporating this code, all four patches run independently without migration occuring. When I type m1[1,2] into the workspace to see if it will return a figure, it returns,

Dear all, First, my aim is to achieve VoIP using VBR and DTX under Win32. I face a problem using speex 1.0.4 and need some help. My voice is ok and no background noise when I do NOT normalize 16 bit value to floating value. Normalized means dividing the 16 bit value by 32767. Turning on VBR is also ok but DTX has no effect. However, the speak is has a continous background beep sound AFTER I