search for: dbetas

Full_Name: Hans Peter Wolf Version: 1.2.1 OS: hpux10.20 Submission from: (NULL) (129.70.84.25) dbeta computes a wrong result with parameters (1.3,1) > version platform hppa2.0-hp-hpux10.20 arch hppa2.0 os hpux10.20 system hppa2.0, hpux10.20 status major 1 minor 2.1 year

Hi all How can I change the limits (xlim or ylim) in a plot that has been already created? For example, consider this naive example curve(dbeta(x,2,4)) curve(dbeta(x,8,13),add=T,col=2) When adding the second curve, it goes off the original limits computed by R for the first graph, which are roughly, c(0,2.1) I know two obvious solutions for this, which are: 1) passing a sufficiently large

>>>>> "MM" == Martin Maechler <maechler@stat.math.ethz.ch> writes: >>>>> "TL" == Thomas Lumley <thomas@biostat.washington.edu> writes: TL> On Thu, 24 Aug 2000, Troels Ring wrote: >>> Dear friends. >>> >>> Is this as expected ? Is alpha and beta too large simply ? >>>

>>>>> "MM" == Martin Maechler <maechler@stat.math.ethz.ch> writes: >>>>> "TL" == Thomas Lumley <thomas@biostat.washington.edu> writes: TL> On Thu, 24 Aug 2000, Troels Ring wrote: >>> Dear friends. >>> >>> Is this as expected ? Is alpha and beta too large simply ? >>>

Hi, sorry for repeating the question but this is kind of important to me and i don't know whom should i ask. So as noted before when I do a parameter fit to the beta distr i get: fitdist(vectNorm,"beta"); Fitting of the distribution ' beta ' by maximum likelihood Parameters: estimate Std. Error shape1 2.148779 0.1458042 shape2 810.067515 61.8608126 Warning

>>>>> "TL" == Thomas Lumley <thomas@biostat.washington.edu> writes: TL> On Thu, 24 Aug 2000, Troels Ring wrote: >> Dear friends. >> >> Is this as expected ? Is alpha and beta too large simply ? >> >> > dbeta(.1,534,646) >> [1] NaN >> Warning message: >> NaNs produced in:

Dear all, we received a bug report for betareg, that in some cases the optim call in betareg.fit would hang the R session and the command cannot be interrupted by Ctrl-C? We narrowed down the problem to the dbeta function which is used for the log likelihood evaluation in betareg.fit. Particularly, the following command hangs the R session to a 100% CPU usage in all systems we tried it (OS X

dbeta(1, a,b) would return 1 instead of 0. Here is the patch for ..../src/math/dbeta.c : --- dbeta.c~ Sun Nov 24 23:43:10 1996 +++ dbeta.c Tue Apr 15 21:25:30 1997 @@ -23,9 +23,7 @@ { if (a <= 0.0 || b <= 0.0) DOMAIN_ERROR; - if (x <= 0) + if (x <= 0 || x >= 1.0) return 0.0; - if (x >= 1.0) - return 1.0; return MATH_CHECK(pow(x, a - 1) * pow(1.0 - x, b - 1.0) /

Dear friends. Is this as expected ? Is alpha and beta too large simply ? > dbeta(.1,534,646) [1] NaN Warning message: NaNs produced in: dbeta(x, shape1, shape2, log) Best wishes Troels -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

Hi, Interpretation problem ! so what i did is by using the: >fit1 <- fitdist(vectNorm,"beta") Warning messages: 1: In dbeta(x, shape1, shape2, log) : NaNs produced 2: In dbeta(x, shape1, shape2, log) : NaNs produced 3: In dbeta(x, shape1, shape2, log) : NaNs produced 4: In dbeta(x, shape1, shape2, log) : NaNs produced 5: In dbeta(x, shape1, shape2, log) : NaNs produced 6: In

Hi, I guess that my problem has an obvious answer, but I have not been able to find it. Suppose I create a custom function, consisting of two beta-distributions: myfunction <- function(x) { dbeta(x,2,6) + dbeta(x,6,2) } How can I calculate the quantiles of myfunction? I have not seen any continous function treated in the docs, and applying the "quantile function" gives me an

Suppose i want to compute a 95% highest density for a beta distribution beta(a,b) the two end points x1 and x2 shoudl satisfy the following two equations: pbeta(x1,a,b)-pbeta(x2,a,b)=95% dbeta(x1,a,b)=dbeta(x2,a,b) Is there any fast way to compute x1 and x2 in R? [[alternative HTML version deleted]]

Hello, I am trying to use R's integrate function to calculate the following integral for z=423: integrate(function(y,z){ sapply(y, function(y,z){ integrate(function(x,z) 1/x*dbeta(0.01,x/(0.005/1.005),(1-x)/(0.005/1.005))*dbeta(y,x/(0.005/1.005),(1-x)/(0.005/1.005))*(1-y)^z,0,1,423)$value }) },0,1,423)$value but I receive an error message saying that the maximum number of subdivisions is

Hi all, I compiled 64-bit R on an Apple Mac G5 running OS X, but it failed make check. Simon Urbanek suggested I post results to R-devel. > On Nov 6, 2007, at 10:23 PM, Steven McKinney wrote: > > > Hi Simon, > > > > Would you be able to give more guidance on how to compile 64-bit > > libiconv for Tiger, > > You can get the sources from Apple and compile

Dear All, I got these results from the example in the function "dbeta": >x <- seq(0, 1, length=21) > dbeta(x, 1, 1) [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Any Idea? TIA Giovanni dr. Giovanni Parrinello Section of Medical Statistics Department of Biosciences University of Brescia 25127 Viale Europa, 11 Brescia Italy Tel: +390303717528 Fax: +390303701157

Hello, I am new to R and have two easy questions. How can you plot multiple density functions in one graph? I have five beta densities that I would like to plot in one graph. I understand how to plot one beta density as a line: plot (x,(dbeta(x,shape1=,shape2=,), type ="l") Does the Pareto distribution need to be added to R with an additional package? thanks, John [[alternative

Dear All, There is one problem I encountered when I used ADAPT to compute some 2-D integral w.r.t beta density. For example, when I try to run the following comments: fun2<-function(theta){return(dbeta(theta[1],0.005,0.005)*dbeta(theta[2],0.005,0.005))} int.fun2<-adapt(ndim=2,lo = c(0,0), up = c(1,1),functn = fun2,eps = 1e-4) It seems it will take very long time to run. Acturally, I

...storm.fm))) > dv <- deviance(storm.fm) > par(pty = "s") > b1 <- bc[1] + seq(-3*se[1], 3*se[1], length = 51) > b2 <- bc[2] + seq(-3*se[2], 3*se[2], length = 51) > bv <- expand.grid(b1,b2) > ssq <- function(b) + sum((Time - b[1]*Viscosity/(Wt-b[2]))^2) > dbetas <- apply( bv, 1 , ssq) > attach(stormer) > cc <- matrix(Time - rep(bv[,1],rep(23,2601)) * + Viscosity/(Wt - rep(bv[,2], rep(23, 2601))), 23) > dbetas <- matrix(drop(rep(1,23) %% cc^2), 51) > fstat <- matrix( ((dbetas -dv)/2) / (dv/21) , 51, 51) > plot(b1, b2, type = &quo...

Hi R User, I was trying to use R for WINBUGS using following model and data (example), but I am new with WINBUGS and don't know how we perform the analysis. I wonder whether I can run the following the example data and Winbugs Model in R. Your help will be highly appreciated. Sincerely, SN PANDIT === library(R2WinBUGS) #Model model{ #likelihood for(i in 1:N){ a1[i] ~ dnorm(a11[i],tau)

Hi R User, I was trying to use R for WINBUGS using following model and data (example), but I am new with WINBUGS and don't know how we perform the analysis. I wonder whether I can run the following the example data and Winbugs Model in R. Your help will be highly appreciated. Sincerely, SN PANDIT === library(R2WinBUGS) #Model model{ #likelihood for(i in 1:N){ a1[i] ~ dnorm(a11[i],tau)