similar to: R interface to C API Rf_logspace_{add,sub}?

>>>>> jing hua zhao >>>>> on Mon, 24 Jun 2019 08:51:43 +0000 writes: > Hi All, > Thanks for all your comments which allows me to appreciate more of these in Python and R. > I just came across the matrixStats package, > ## EXAMPLE #1 > lx <- c(1000.01, 1000.02) > y0 <- log(sum(exp(lx))) > print(y0) ## Inf

>>>>> William Dunlap via R-devel >>>>> on Sun, 23 Jun 2019 10:34:47 -0700 writes: >>>>> William Dunlap via R-devel >>>>> on Sun, 23 Jun 2019 10:34:47 -0700 writes: > include/Rmath.h declares a set of 'logspace' functions for use at the C > level. I don't think there are core R functions that call

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 R help, This seems to be a commonly asked question and I am able to run examples that have been proposed, but I can't seems to get this to work with my own data. Reproducible code is below. Thank you in advance for any help you can provide. The main problem is that I can not get the confidence lines to plot correctly. The secondary problem is that predict is not able to find my object

Hi All, Thanks for all your comments which allows me to appreciate more of these in Python and R. I just came across the matrixStats package, ## EXAMPLE #1 lx <- c(1000.01, 1000.02) y0 <- log(sum(exp(lx))) print(y0) ## Inf y1 <- logSumExp(lx) print(y1) ## 1000.708 and > ly <- lx*100000 > ly [1] 100001000 100002000 > y1 <- logSumExp(ly) > print(y1) [1] 100002000

Hi all, given this example #start a<-c(0,70,50,100,70,650,1300,6900,1780,4930,1120,700,190,940, 760,100,300,36270,5610,249680,1760,4040,164890,17230,75140,1870,22380,5890,2430) length(a) b<-c(0,0,10,30,50,440,1000,140,70,90,60,60,20,90,180,30,90, 3220,490,20790,290,740,5350,940,3910,0,640,850,260) length(b) out<-ks.test(log10(a+1),log10(b+1)) # max distance D

I agree with many the sentiments about the wisdom of computing very small p-values (although the example below may win some kind of a prize: I've seen people talking about p-values of the order of 10^(-2000), but never 10^(-(10^8)) !). That said, there are a several tricks for getting more reasonable sums of very small probabilities. The first is to scale the p-values by dividing the

include/Rmath.h declares a set of 'logspace' functions for use at the C level. I don't think there are core R functions that call them. /* Compute the log of a sum or difference from logs of terms, i.e., * * log (exp (logx) + exp (logy)) * or log (exp (logx) - exp (logy)) * * without causing overflows or throwing away too much accuracy: */ double Rf_logspace_add(double

Hi, I would like to know how (if) I can extract some of the information from the summary of my nlme. at present, I get a summary looking something like this: > summary(fit.nlme) Nonlinear mixed-effects model fit by maximum likelihood Model: MLKYLD ~ W4(DIM, logA, B, C) Data: ADHIS.x0 AIC BIC logLik 265314 265401.6 -132647 Random effects: Formula: list(logA ~ 1 , B ~

Hi My code is as following: #include <R.h> #include <Rinternals.h> //* the Projector part *// void Projector(double *L, int *dimL, double *G, int *dimG, double *W, int *dimW, int *xymod, int *dimxy, double *modif, int *dimif, double *Lsum) { ...} //* the interface part *// #define getDim(A) INTEGER(coerceVector(getAttrib(A,R_DimSymbol), INTSXP)) SEXP Projector5(SEXP L, SEXP G,

I'm new R-programming user, I need to use gam function. y<-gam(a~s(b),family=gaussian(link=log),data) y<-gam(loga~s(b), family =gaussian (link=identity),data) why these two command results are different? I guess these two command results are same, but actally these two command results are different, Why? -- View this message in context:

The replay gain code has dos line endings in CVS, which causes problems for the Sun compiler, among others. Attached is a patch for the lazy, but it's probably easier to fix locally and commit. -r

Dear r helpers I run the following code for nested logit and got a message that Error in optim(c(0, 0, 0, 0, 0.1, -2, -0.2), fr, hessian = TRUE, method = "BFGS") : initial value in 'vmmin' is not finite What does this mean? and how can I correct it? Thank you June > yogurt = read.table("yogurtnp.csv", header=F,sep=",")> attach(yogurt)>

Dear R users, I have two n*1 integer vectors, y1 and y2, where n is very very large. I'd like to compute elbp = 4^(y1) * 5^(y2) * sum_{i=0}^{max(y1, y2)} [{ (y1-i)! * (i)! * (y2-i)! }^(-1)]; that is, I need to compute "elbp" for each (y1, y2) pair. So I made R code like below, but I don't think it's efficient Would you plz tell me how to avoid this "for"

Dear Rhelpers I have the problem with initial values, could you please tell me how to solve it? Thank you June > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2))) Error in maxRoutine(fn = logLik, grad = grad, hess = hess, start = start, : NA in the initial gradient > p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2),method="BFGS")) Error in optim(start, func, gr =

Dear List, I am looking for a possibility to fit a mixture model under R using maximum likelihood estimation. Venables and Ripley describe a solution working under S+ (in MASS, 3. ed., p. 263) which requires the D system function and deriv3. This solution does not seem to be portable to R or at least I do not realise how. Is there anyone who a) knows how one could make the MASS-method run under

Hello there, I am encountering segfaults with following: ruby 1.8.6 (2008-08-11) [i386-mswin32] on windows 7, rails 2.3.2, mysql (2.7.1) gem and MySQL 5.0.87-community-nt-log Tried with MySQL 5.1 and mysql gem 2.8.1 with no luck. Loga are showing: C:\Users\Alex\Documents\IT\ruote-web2>ruby script\server --port=3333 => Booting Mongrel => Rails 2.3.2 application starting on

Hi all, I'm working on a term structure estimation using state-space modeling for 1, 2 and 3 factor models. When I started to read the functions on R, I got to the function ss on the library sspir. From what I understood this function is similar to SsfFit from S-PLUS. But for my models purpose there is something left to be desired. Its formulation follow these equations: *Y_t = F_t^T *

Hi there, I was hoping to post this message. Im hoping I;ve got the right spot?! Hi there, sorry for basic question but im very new to R. Im trying to run a lme model with two categorical variables, each having 6 (for the explanatory variable C.f) and 5 levels (for expl. variable D.f) respectively. The lme runs fine, summary and anova commands are ok. But now i've tried running posthoc

Hi, Can anybody briefly explain the significance of the updated moduli file? Is this a critical update? Should all existing installations update their moduli file? Thanks in advance, -- Dan