search for: shape2

Dear R users, first I take this opportunity to greet all the R community for your continuous efforts. I wrote a function to calculate the pdf and cdf of a custom distribution (mixed gamma model). The function is the following: pmixedgamma3 <- function(y, shape1, rate1, shape2, rate2, prev) { density.function<-function(x) { shape1=shape1 rate1=rate1 shape2=shape2 rate2=rate2 prev=prev fun<-prev*((rate1^shape1)/gamma(shape1)*(x^(shape1-1))*exp(-rate1*x)) + (1-prev)*((rate2^shape2)/gamma(shape2)*(x^(shape2-1))*exp(-rate2*x)) return(fun) } den&lt...

...ortant 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 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...

...scribo para ver si me podeis ayudar con un asunto en el que me he quedado un poco encallado. Lo que tengo que hacer es sacar los percentiles (0.001, 0.005, 0.95 y 0.999) de varias distribuciones beta, concretamente 418. Cada distribucion esta definida por los parametros "shape1" y "shape2". Por lo tanto tengo una base de datos de 418 filas y en cada una de ellas los parametros que me definen la distribucion beta, shape1 y shape2. Tengo el siguiente codigo: distrname="beta" shape1= 0.2 #ejemplo shape2= 0.3 #ejemplo quantvector=c(0.001,0.005,0.95,0.999) (paste(&quot...

...I can work around this, but I'm curious why it happens and whether there's a better workaround -- it doesn't seem to be in a particularly extreme corner of parameter space. It happens, e.g., for these parameters: phi <- 1.1 i <- 0.01 t <- 0.001 shape1 = i/phi ## 0.009090909 shape2 = (1-i)/phi ## 0.9 qbeta(t,shape1,shape2) ## 5.562685e-309 ## brute-force uniroot() version, see below Qbeta0(t,shape1,shape2) ## 0.9262824 The qbeta code is pretty scary to read: the warning "full precision may not have been achieved" is triggered here: https://github.com/wch/r-...

...cript as shown below: aov.R ------ data1<-c(49,47,46,47,48,47,41,46,43,47,46,45,48,46,47,45,49,44,44,45,42,45,45,40 ,49,46,47,45,49,45,41,43,44,46,45,40,45,43,44,45,48,46,40,45,40,45,47,40) matrix(data1, ncol= 4, dimnames = list(paste("subj", 1:12), c("Shape1.Color1", "Shape2.Color1", "Shape1.Color2", "Shape2.Color2"))) Hays.df<-data.frame(rt = data1, subj=factor(rep(paste("subj", 1:12, sep=""), 4)), shape=factor(rep(rep(c("shape1","shape2"), c(12, 12)), 2)), color=factor(rep(c("color1",&quot...

Hi folks, I have a question about how to efficiently produce random numbers from Beta and Binomial distributions. For Beta distribution, suppose we have two shape vectors shape1 and shape2. I hope to generate a 10000 x 2 matrix X whose i th rwo is a sample from reta(2,shape1[i]mshape2[i]). Of course this can be done via loops: for(i in 1:10000) { X[i,]=rbeta(2,shape1[i],shape2[i]) } However, the above code is time consuming. It would be better to directly generate X without any loo...

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 dbeta(x, shape1, shape2, log) : NaNs produced ##Is this a real...

...d") but what about beta-prime? In R it exists only pbeta which is intended only for the beta distribution (not beta-prime) This is what I used for the estimation of the parameters: mleBetaprime <- function(x,start=c(1,1)) { mle.Estim <- function(par) { shape1 <- par[1] shape2 <- par[2] BetaprimeDensity <- NULL for(i in 1:length(posT)) BetaprimeDensity[i] <- posT[i]^(shape1-1) * (1+posT[i])^(-shape1-shape2) / beta(shape1,shape2) return(-sum(log(BetaprimeDensity))) } est <- optim(fn=mle.Estim,par=start,method="Nelder-Mead&quot...

...to binary ‘operator/’ make: *** [file74d8254b96d4.o] Error 1 Below is a mock function that can reproduce this error. I wonder if anyone can tell me what is the problem here. Thank you in advance!! foo<-cppFunction(' NumericVector foo(NumericVector q, NumericVector shape1, NumericVector shape2, Function pbeta, Function sequence){ NumericVector output(q.size()); output=pbeta(sequence(q.size())/q.size(), shape1, shape2); return output; } ') Best, Xiao [[alternative HTML version deleted]]

...scale = 2) logis(location = 4, scale = 2) PASSED [1] TRUE > > dkwtest("t",df = 1) t(df = 1) PASSED [1] TRUE > dkwtest("t",df = 10) t(df = 10) PASSED [1] TRUE > dkwtest("t",df = 40) t(df = 40) PASSED [1] TRUE > > dkwtest("beta",shape1 = 1, shape2 = 1) beta(shape1 = 1, shape2 = 1) PASSED [1] TRUE > dkwtest("beta",shape1 = 2, shape2 = 1) beta(shape1 = 2, shape2 = 1) PASSED [1] TRUE > dkwtest("beta",shape1 = 1, shape2 = 2) beta(shape1 = 1, shape2 = 2) PASSED [1] TRUE > dkwtest("beta",shape1 = 2, shape2 =...

...fun2) force(n) p1<-unlist(par1) p2<-unlist(par2) force(p1) force(p2) localfun1 <- function() fun1(n, p1) localfun2 <- function() fun2(n, p2) vp<-replicate(nsim,t.test(localfun1(), localfun2())$p.value) return(vp) } f2(fun1=rbeta,par1=list(shape1=2,shape2=2),fun2=rbeta,par2=list(shape1=1,shape2=2)) Thank you for your help. Kenneth

...whether there's a better > workaround -- it doesn't seem to be in a particularly extreme corner of > parameter space. It happens, e.g., for these parameters: > phi <- 1.1 > i <- 0.01 > t <- 0.001 > shape1 = i/phi ## 0.009090909 > shape2 = (1-i)/phi ## 0.9 > qbeta(t,shape1,shape2) ## 5.562685e-309 > ## brute-force uniroot() version, see below > Qbeta0(t,shape1,shape2) ## 0.9262824 > The qbeta code is pretty scary to read: the warning "full precision > may not have been achieved" is...

......" to CDF function (for instance, see ks.test above) in the "right form". The "..." is passed (to "goftests") as a list object and it should be converted to an "argument list". For instance: ##### BEGIN CODE SNIP ##### parms <- list( shape1 = 2, shape2 = 5 ); goftests( x, "beta", parms[["shape1"]], parms[["shape2"]] ); # Works! goftests( x, "beta", parms ) # Don't Works! parms <- list( aNum = 5, aVector = c(1,2,3), aMatrix = matrix(c(4,5,6,7,8,9),nrow=2,byrow=T) ); goftests( x, "my-special-dis...

...rning: this will hang the R session dbeta(0.9, 1e+308, 10) Furthermore, through a trial and error investigation using the following code ## Warning: this will hang the R session x <- 0.9 for (i in 0:100) { a <- 1e+280*2^i b <- 10 cat("shape1 =", a, "\n") cat("shape2 =", b, "\n") cat("Beta density", dbeta(x, shape1 = a, shape2 = b), "\n") cat("===\n") } I noticed that: * this seems to happen when shape1 is about 1e+308, seemingly irrespective of the value of shape2 (run the above with another value of b), and as i...

Good-day Sir, I am R.Language users but am try to? estimate parameter of beta distribution particular dataset but give this error, which is not clear to me: (Initial value in "vmmin" is not finite) beta.fit <- fitdistr(data,densfun=dbeta,shape1=value , shape2=value) kindly assist. expecting your reply:

...oda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){ sum(dbeta(beta,shape1,shape2, log=T)) } posterior <- MCMClogit(nausea~nsaid*diuretic, data=w, verbose=2000,burnin = 1000, mcmc = 10000, user.prior.density=logpriorfun,shape1=1,shape2=1) user.prior.density(beta.start) == -Inf. Error in MCMClogit(nausea ~ nsaid * diuretic, data = w, verbose...

...<=1 ) with a U-shaped histogram and try to fit a beta distribution using fitdistr. In fact, hist(rbeta(100,0.1,0.1)) looks a lot like my data. The equivalent to the example in the manual sometimes work: > a <- rbeta(100,0.1,0.1) > fitdistr(x=a, "beta", start=list(shape1=0.1,shape2=0.1))1) > shape1 shape2 0.09444627 0.12048753 (0.01120670) (0.01550129) but sometimes does not: > a <- rbeta(100,0.1,0.1) > fitdistr(x=a, "beta", start=list(shape1=0.1,shape2=0.1))1) > Error in optim(start, mylogfn, x = x, hessian = TRUE, ...) : Fun...

...s correctly. By the way, the Feller-Pareto distribution (see actual) is among the best distributions for the Danish dataset. You may also consider composite distributions, see https://CRAN.R-project.org/view=Distributions Kind regards, Christophe dmixgam <- function(x, prob, shape1, scale1, shape2, scale2) prob*dgamma(x, shape1, scale=scale1)+ (1-prob)*dpareto(x, shape2, scale=scale2) pmixgam <- function(q, prob, shape1, scale1, shape2, scale2) prob*pgamma(q, shape1, scale=scale1)+ (1-prob)*ppareto(q, shape2, scale=scale2) library(actuar) library(fitdistrplus) data("danishuni&q...

...o that the mean nausea rate is alpha/(alpha+beta) > min<-10 > max<-60 > n<-as.integer(runif(3,min,max)) > for(i in 1:k) + x<-cbind(x,rbinom(5,n[i],p[i])) > > # Exact Confidence Interval > > l_cl_exact<-qbeta(.025, x, n-x+1) Warning message: In qbeta(p, shape1, shape2, lower.tail, log.p) : NaNs produced > u_cl_exact<-qbeta(.975, x+1, n-x) Warning message: In qbeta(p, shape1, shape2, lower.tail, log.p) : NaNs produced > x [,1] [,2] [,3] [1,] 8 12 14 [2,] 5 15 13 [3,] 5 12 12 [4,] 8 21 12 [5,] 8 14 12 > n [1] 10...

...a") : 'start' must be a named list Yes, I do understand that sometime for the distribution to converge to the given set of data, it requires initial parameters of the distribution, to start off with. Hence, i tried this >x1fitbeta<-fitdistr(x1,densfun=dbeta, start=list(shape1=2,shape2=3)) Error in optim(x = c(1.89074018737135, 1.52649293971978, 2.19950245230280, : initial value in 'vmmin' is not finite I tried with "f" and "chi-square" what i did with "t". Please find below the output. > x1fitt<-fitdistr(x1,"t") Error...