similar to: Logarithm interpolation

Hi, In order to use the mle2-function, one has to define the likelihood function itself. As we know, the likelihood function is a sum of the logarithm of probability density functions (pdf). I have implemented myself the pdfs that I am using. My problem is, that the pdfs values are negative and I cann't take the logarithm of them in the log-likelihood function. So how can one take the

Dear List I have notice this kind of problem between my two * box. My scenario is : Iax GSM IaxClient----->PBX1------------>PBX2-->TDM today CVS Stable V1 I use as Client FireFly with IAX2/GSM and try to call my PBX1 this server call PBX2 to terminate the call trought a TDM line (TE410P) but after PBX2 join the two call i can see the log below from my PBX1, i can speak for

Hi I have a collection of about 16 time series with occasional missing data. A few of these time-series start later than the rest. There is a relatively high correlation between them (they are hourly temps at various locations around the UK). The longest series contains about 40000 points I have constructed each time series into a zoo object and then merged each of these objects in to one (zoo

Hi, In continuation of the discussion of melange comments,about negative value returned in matcher due to logarithm of probabilities. *I**f we make K suitably large, we could clamp each log(K.Pi) to be >= 0, and this change will only affect really low probability terms (those with Pi < 1/K, so you can adjust K to suit):* *W' = sum(i=1,...,n, max(log(K.Pi), 0))* Did you mean for low

Is it the tick labels that you want to change? -----Original Message----- From: "Elisabeth Bjerke Rastad" <ebr024 at post.uit.no> To: "r-help at r-project.org" <r-help at r-project.org> Sent: 5/10/10 11:20 AM Subject: [R] [Fwd: Re: Plotting log-axis with the exponential base to a plot with the default logarithm base 10] Hello! Thank you for answering! What I am

Dear R helpers, I need to find out maximized log likelihoods, parameters estimates and standard errors (in parentheses) of r largest-order statistics model, with different values of r by using the function rlarg.fit. I want to specify required number of order statistics to the model. I attached my data file with this mail.please help me. Ruposh --- r-help-request at stat.math.ethz.ch wrote:

Hi, I am quite new to R and I have two questions regarding ROCR. 1. I have tried to understand how to extract area-under-curve value by looking at the ROCR document and googling. Still I am not sure if I am doing the right thing. Here is my code, is "auc1" the auc value? " pred1 <- prediction(resp1,label1) perf1 <- performance(pred1,"tpr","fpr") plot(

Hello I want to calculate natural logarithm of sum of combinations as follow: (R code) { com_sum=choose(2000000,482)*choose(1000000,118)+choose(2000000,483)*choose(1000000,117)+...+choose(2000000,i)*choose(1000000,600-i)+...+choose(2000000,600)*choose(1000000,0) #calculate the sum result=log(com_sum) #calculate the log of the sum } But

Hi I'm sorry about the triviality of my problem. I have a vector (v) of three columns (logA, logB, id). I want to compute (and plot) the correlation between logA and logB for different thresholds of id (e.g. >30, etc). So I tried: for(i in 1:100){ points(cor(v$logA[v$id>i], v$logB[v$id>i], use="complete.obs"), i)) } (i created a plot object already) but it comes with

Dear all; First of all, this is probably a more conceptual question than a R-related one, but still want to give it a try. When working with the interpolation function "interp" from the package akima and the triangulation function "tri.mesh" from package tripack I've got NA's for the interpolation and "error" for the triangulation. The data is arranged in a

Hi R experts, I have the following timeseries data: #example data structure a <- c(NA,1,NA,5,NA,NA,NA,10,NA,NA) c <- c(1:10) df <- data.frame(timestamp=a, sequence=c) print(df) where i would like to linearly interpolate between the points 1,5, and 10 in 'timestamp'. Original timestamps should not be modified. Here the code I use to run the interpolation (so far): # linear

hi guys I need to interpolate values for the zero coupon yield curve. Following data is given date days rate 1996 01

Hi R community I need to interpolate precipitation data for a natural park. I have precipitation data from some climate stationts. (I know the table is not complete but I only need to show you X,Y, Altitude and PrepJul) X Y Altitude PrepJan PrepFeb PrepMar PrepAp PrepMay PrepJun PrepJul 597706 4093438 41 0 9 77,8 63,1 17,5 0 2,6 597535

Dear all, I'm having trouble interpolating a number of gridded datasets that I have. I'm quite new to R so any help/advice that can be offered would be much appreciated! Firstly I'll describe my dataset. The data is a grid of the planet at 1 degree spatial resolution, with each grid cell containing a value describing a particular variable (e.g. population density) for coordinate

I''m in the process of rolling out a new homegrown ENC (we''ve decided on using an ENC over hiera; I know it''s not the way a lot of you are going, but it''s right for us) and am running into some issues with variable interpolation in ENC data. To give a concrete example, I''m using modules to configure puppetdb, puppet master, and puppet clients. So,

As part of a project I am doing, I had to present a 2D table as a surface. This was fairly straightforward (using persp), but I needed to interpolate points to make it sort of pretty. This was done by using quadratic interpolation between the existing points to smooth out the surface a bit. As I couldn't find an R function that did this, I wrote an admittedly nasty pair of functions that

hi, thanks for answers to my previous qns. have some more of them. hope it's ok to ask questions on the basics of CELP here. let me know if it's not. 1) synthesized filter stability after interpolation of LSFs I read from some resources that if LSF representation of LPCs is used for interpolation, stability of the synthesis filter is guaranteed. from another source, i read that the

I have a time series of more than 2500 points. I also have observed data set but it is not continuous. I want to use the observed data set and find out the modeled data from the time series data. After the modeled data has been found out for the same time I want to plot the correlation plot and display the equation and coefficient of correlation on the plot. The sample time series can be found on

Hi there, I have been using the Tps function (within the Fields package) for a while now to interpolate different sedimentary units. Due to the method of formation of the units I know that at some edges the thickness of the unit decreases to zero. I was wondering if there was someway to specify that the interpolation only occurs within the convex hull of the data, outside of which the the values

Hi, I have three matrices (X,Y,P) with the same dimension. The X,Y grid is regular and I want to perform linear interpolation to pick out certain points. In matlab appropriate call is something like Pout=interp2(X,Y,P,Xout,Yout, method="linear") where Xout and Yout are the locations where I want the Pout data (typically a different grid). (Scipy has this routine in