similar to: Fitting data to gaussian distributions

Recently, I have been working with some data that look like two overlapping gaussian distributions. I would like to either 1) determine the mean and SD for each of the two distributions OR 2) get some (bayesian ?) statistic that estimates how likely an observation is to belong to the left-hand or right-hand distribution In case I'm using the wrong language, my data looks something like

I am trying to fit a normal linear model with response y and predictor x and two factors sex and group. I would like each combination of sex and group to have individual slopes and then subsequently have parallel slopes. I tried the model y ~ x*sex*group and it seemed to work for the first case.. Is this how it is supposed to be done? For the second the model y ~ sex + group seems to work. In

Dear [R]-list, I'm looking for a classic equivalent of the wle.gamma function (library wle) that estimate robustly the shape and the scale parameters of gamma data. I have a vector of iid gamma rv : >data=rgamma(100,shape=10,scale=3) and a vector of their weights: >weights=c(rep(.5/70,70),rep(.25/20,20),rep(.25/10,10)) and want to estimate the scale and shape of the gamma

Dear R-Experts, I was wondering how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function

Dear R Users?? I woould like to generate mixture model data. I try two type method as followings two method, can anyone tell me which is right? or better? 1. generate two vectors data y1 and y2 from f1 and f2 seperately, and calculate y=alpha1*y1+alpha2*y2, 2. generate prob=unif(1), if prob <alpha1 y=y1, else y=y2. Can anyone give me some idea? Thanks so much! Goeland goeland at

Please guide me through to resolve the error message that I get this is what i have done. >x1<- rnorm(100,2,1) >x1fitbeta<-fitdistr(x1,"beta") Error in fitdistr(x1, "beta") : '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

Dear all, I am attempting to model some one-dimensional data using a mixture model of non-central Student's t distributions. However, I haven't been able to find any R package that provides this functionality. Could there be a way to "manipulate" the EM algorithms from the mixdist or mixtools package to fit the model, or do you have any other suggestions? If anyone could help

Hello, I would like to fit a mixture of two Weibull distributions to my data, estimate the model parameters, and compare the fit of the model to that of a single Weibull distribution. I have used the mix() function in the 'mixdist' package to fit the mixed distribution, and have got the parameter estimates, however, I have not been able to get the log-likelihood for the fit of this model

Dear All, A simple question: packages like fitdistr should be ideal to analyze samples of data taken from a univariate distribution, but what if rather than the raw data of the observations you are given directly and only a histogram? I was thinking about generating artificially a set of data corresponding to the counts binned in the histogram, but this sounds too cumbersome. Another question is

Dear All, I have two questions regarding distribution fitting. I have several datasets, all left-truncated at x=1, that I am attempting to fit distributions to (lognormal, weibull and exponential). I had been using fitdistr in the MASS package as follows: fitdistr<-(x,"weibull") However, this does not take into consideration the truncation at x=1. I read another posting in this

Hi, I want to fit 3 beta distributions to my data which ranges between 0 and 1. What are the functions that I can easily call and specify that 3 beta distributions should be fitted? I have already looked at normalmixEM and fitdistr but they dont seem to be applicable (normalmixEM is only for fitting normal dist and fitdistr will only fit 1 distribution, not 3). Is that right? Also, my data has 26

I am new to R. So, please bear with me if this questions is already been asked and answered. I am looking for a R function that fit a dataset to common distributions such as Normla, Log-Normal, Poisson, Weibull, and Beta. Basically I am looking for ways to estimate distribution parameters rather than having to write a program for it. Thanks for your help. T. S. Ramanarayanan Aventis

Hello, I am currently writing a program whose goal is to fit usual distributions (estimating parameters and confidence intervals for a given distribution). After some research in R, R-help and google I have found most of what I was looking for (especially thanks to MASS - fitdistr() ), however there are still a few distributions I could not find R code for: Multinormal, Truncated normal,

Dear all, I have wrote some sample code that would allow me easier fit fast many distributions and check which of the fits performs better. My sample code (that you can of course execute it looks like that) distrList<-list(   "exponential",  "geometric", "log-normal",  "normal", "Poisson") fitfunction<-function(Type,x){     return

I apologize if this is redundant. I've been Googling, searching the archive and reading the help all morning and I am not getting closer to my goal. I have a series of data( xi, yi). It is not evenly sampled and it is messy (meaning that there is a lot of scatter in the data). I want to fit a normal distribution (i.e. a gaussian) to the data in order to find the center. (The data

Hi all, I'm trying to install the mixdist package with: install.packages("mixdist") But I'm getting the following errors: * installing *source* package ‘mixdist’ ... ** R ** data ** preparing package for lazy loading ** help *** installing help indices ** building package indices ... Warning in utils::data(list = f, package = package, lib.loc = lib.loc, envir = dataEnv) :

Hi all -- I'm seeing the following commands reliably produce a crash in RGui, version 2.0.1, for both my home and office machine: > rm(list = ls(all = TRUE)); > load("dataset.R"); > library("wle"); > data.wle = wle.lm(abortion ~ year * lib.con + age + gender + + urbanism + census + income + church.att + children + educ + + religion.imp, data =

Dear all I've got the dataset data:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491; ?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334 I know from other testing that it should be possible to fit the data with the exponentialdistribution. I tried to get parameterestimates for the exponentialdistribution with R, but as the values of the parameter

Dear R users, Please help me on using "mix" function under package "mixdist". My data distribution shows there are two components for the mixture distribution: left part is an exponential and right part is a normal. So I plan to use "gamma" mixture distribution to estimate the parameters. Here is what I am using for the "mix" function. Test<-mix(x,

Hello, I am a new user of R and found the function dtnorm() in the package msm. My problem now is, that it is not possible for me to get the mean and sd out of a sample when I want a left-truncated normal distribution starting at "0". fitdistr(x,dtnorm, start=list(mean=0, sd=1)) returns the error message "Fehler in "[<-"(`*tmp*`, x >= lower & x <= upper,