similar to: newbie question about beta distribution

Hi, Well, i need some help, practical and theoretical. I am wondering why the fitdistplus (mle function) is returning an error for this code: [code] x1 <- c(100,200,140,98,97,56,42,10,2,2,1,4,3,2,12,3,1,1,1,1,0,0); plotdist(x1); descdist(x1, boot =1000); y<- sum(x1); d= as.vector(length(x1)); for(i in 1:length(x1)){ d[i] = x1[i]/y; } fitdist(d, "beta") [/code] Error:

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

I want to fit some p-values to a beta distribution. But the problem is some of the values have 0s and 1's. I am getting an error if I use the MASS function to do this. Is there anyway to get around this? -- Thanks, Jim. [[alternative HTML version deleted]]

Dear All, I need to fit a custom probability density (based on the symmetric beta distribution B(shape, shape), where the two parameters shape1 and shape2 are identical) to my data. The trouble is that I experience some problems also when dealing with the plain vanilla symmetric beta distribution. Please consider the code at the end of the email. In the code, dbeta1 is the density of the beta

First let me confess that I am a R-novice. I am trying to fit a beta distribution for a dataset using fitdistr(MASS). I am having difficulties with it because the function tends to fit a distribution with a range of 0 to 1 (I guess). However, my dataset is not! Anytips or tricks will be very much appreciated. Many Thanks. T. S. Ramanarayanan, Ph.D. Aventis CropScience Research Triangle Park,

Dear all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) 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){

Dear R users, This is a KDE beginner's question. I have this distribution: > length(cap) [1] 200 > summary(cap) Min. 1st Qu. Median Mean 3rd Qu. Max. 459.9 802.3 991.6 1066.0 1242.0 2382.0 I need to compute the sum of the values times their probability of occurence. The graph is fine, den <- density(cap, from=min(cap), to=max(cap), give.Rkern=F)

Hi folks, I tried to retain the Weibull distribution using the `nls' function and proceeding along the lines of the example provided in the `SSweibull' help (at least I thought so): t <- (1:200)/100 v <- pweibull(t, shape=3, scale=1) df <- data.frame(Time=t, Value=v) Asym <- 1.0; Drop <- 1.0; lrc <- 0; pwr <- 1 df.estimate <- nls(Value ~ SSweibull(Time,

Dear all, I have a discrete distribution showing how age is distributed across a population using a certain set of bands: Age <- matrix(c(74045062, 71978405, 122718362, 40489415), ncol=1, dimnames=list(c("<18", "18-34", "35-64", "65+"),c())) Age_dist <- Age/sum(Age) For example I know that 23.94% of all people are between 0-18 years, 23.28%

Hi all, We know that Hermite polynomial is for Gaussian, Laguerre polynomial for Exponential distribution, Legendre polynomial for uniform distribution, Jacobi polynomial for Beta distribution. Does anyone know which kind of polynomial deals with the log-normal, Student抯 t, Inverse gamma and Fisher抯 F distribution? Thank you in advance! David [[alternative HTML version deleted]]

Hi to all I found an formular of an ** ***p-Value Calculator for the Chi-Square test* *http://www.danielsoper.com/statcalc/calc11.aspx* *with the formula* *http://www.danielsoper.com/statkb/topic11.aspx* *what's the gamma function of this formula in r?* *df=5* *ch2=25.50878* *the following code does not give the result <0.001 for the values above * *p=

Could somebody help me to understand the syntax of R's ppois function? I'm looking to calculate the cumulative probability density of an observed value (y) given the expected mean (mu) and the level of significance (alpha). I'm coming from using SAS to do this and don't recognize the descriptions of the arguments for ppois. The definitions of lambda and p as stated in the R manuals

Hi, I am facing the problem that I want to plot a histogram chart set freq to true and overlay with normal or weibull or exponential distribution curve. The sample code is shown as below: >samp<-c(-8.2262,-8.2262,-8.2262,-8.20209,-8.09294,-8.07321,-8.07321, -8.07321,-8.07175,-8.04948,-8.04948,-8.04948,-8.03848,-8.03848,

Hello all I'm trying to do a simulation that involves identifying the minimum point between two peaks of a (usually) bimodal distribution. I can do this easily if there are only two peaks: CnBdens<-density(Ys/Xs) #probability density function for ratio of Ys to Xs for(p in 1:512) ifelse(CnBdens$y[p]>CnBdens$y[p-1],peak1<-p,break) #identifies first peak in probability

Hello,   Could someone help me please and to tell how to get the probability from empirical DENSITY (not parametric) for each data value (R function). For example, for normal distribution there is such a function like:   “dnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)”   I need the same function only for the empirical DENSITY function (which does not correspond to any typical

Dear R users, I have a question about numerical integration in R. I am facing the 'non-finite function value' error while integrating the function xf(x) using 'integrate'. f(x) is a probability density function and assumed to follow the three parameter (min = 0) beta distribution for which I have estimated the parameters. The function is integrated

Hi, I have one basic doubt. Suppose X ~ N(50,10). I need to calculate Probability X = 50. dnorm(50, 50, 10) gives me [1] 0.03989423 My understanding is (which is bit statistical or may be mathematical) on a continuous scale, Probability of the type P(X = .....) are nothing but 1/Infinity i.e. = 0. So as per my understanding P(X = 50) should be 0, but even excel also gives 0.03989422. Obviously

Hello everyone, With th bar histogram (number of occurrences) hist<-c(24,7,4,1,2,1,1) of seven equally spaces classes ]1-4], ]5-8], ]9-12], ]13-16], ]17-20], ]21-24], ]25-28], I obtained shape=0.8276 and rate=0.1448. I would like to know how to build the continuous pdf of a this gamma distribution knowing these two estimated parameters such that I will be able to predict the pdf of any

this is probably not really a R specific question, if so apologies for off-topic posting: I'm interested in the probability density function of the maximum values from repeated samples of size N from a normal distribution: smp <- rnorm(N, meanval, stdev) with some mean 'meanval' and standard deviation 'stdev'. I would like to know what is the frequency distribution of

Hello,   Could someone help me please and to tell how to get the probability from empirical distribution (not parametric) for each data value (R function). For example, for normal distribution there is such a function like:   “pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)”   I need the same function only for the empirical distribution (which does not correspond to any typical