similar to: fitting a gaussian to some x,y data

Hi! I need to fit a data that shows up as two gaussians partially superimposed to the corresponding gaussian distributions, i.e. data=c(rnorm(100,5,2),rnorm(100,-6,1)) I figured it out how to do it with mle or fitdistr when only one gaussian is necessary, but not with two or more. Is there a function in R to do this? Thank you very much in advance, Luis

Hello, I am trying to fit my histogram to a smooth Gaussian curve(the data closely resembles one except a few bars). This is my code : #!/usr/bin/Rscript out_file = "irc_20M_opencl_test.png" png(out_file) scan("my.csv") -> myvals hist(myvals, breaks = 50, main = "My Distribution",xlab = "My Values") pdens <- density(myvals, na.rm=T) plot(pdens,

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

Hello,I'm still working with this data set, and trying to fit it with a nonlinear model. Here is my data > small <- c(507680,507670,508832,510184,511272,513380,515828,519160,525046,534046,547982,567124,590208,614506,637876,656846,669054,672976,668800,656070,637136,614342,590970,570752,554480,542882,535630,531276,528682,527682,527020,526834,526802,526860) test <- glm(dnorm(x),

Hi R-helpers, I'm trying to model a univariate as a bi-modal normal mixtures. I need to estimate the parameters of each gaussian (mean and sd) and their weights. What's the best way to do this in R? Thanks, Xiao-Jun

Hi, I have a data set (weight) that does not follow the Gaussian (Normal) distribution. However, I have to transform the data before applying the Gaussian distribution. I used this syntax and used log(weight) as: posJy.model<-glm(log(weight) ~ factor(pos), family=gaussian(link='identity'), subset=Soil=="Jy"). This syntax COULD NOT transform the data. But if I transform the

Hi, As far as I know, there exist some programs via the function INLA, but I'm so curious if there is a specific function directly used to fit the log Gaussian Cox process model and predict the latent Gaussian field. That is, if I have a data points, then I input it in the function and don't need to revise the program. Thanks for your help. Joseph -- View this message in context:

Is there a way to obtain the parameters (mean, sd, amplitude) of the gaussian functions obtained in a density fit to data. The faithful $waiting times is a standard example. The 2-gaussian fit is very nice, but how can I obtain the parameters? Thanks for your help. Regards, Victor Bloomfield

I have a one-variable data set in R. The plot of histogram of my numerical variable suggests an inverse gaussian distribution. How can I obtain best estimation for the two parameters of inverse gaussian based on my data? Thanks. -- View this message in context: http://n4.nabble.com/estimate-inverse-gaussian-in-R-tp949692p949692.html Sent from the R help mailing list archive at Nabble.com.

Hi all, Does anybody know any function that performs gaussian adapative quadrature integration of univariate functions? Thanks in advance, Regards, Caio __________________________________________________ [[alternative HTML version deleted]]

Dear R-Helpers, I am hoping to perform survival analyses using the "ex-Gaussian" distribution. I understand that the ex-Gaussian is a convolution of exponential and Gaussian distributions for survival data. I checked the "survreg.distributions" help and saw that it is possible to mix pre-defined distributions. Am I correct to think that the following code makes the

Two questions: 1. inverse.gaussian is up there as one of the glm families, but do people ever use it? There is no inverse.gaussian in the R distribution family, and when I checked McCullagh & Nelder, it only appeared twice in the book (according to subject index), once in the table on p. 30 and once on p. 38 in a passing sentence. Is there a good reference on this distribution? 2. When I

Hi there fellow R-users, Can anyone tell me how to build a prediction interval for a gaussian log-link model for the reponse variable?? I can find the standard error of the predictions but I cant seem to find the prediction interval. Is there a way I can calculate the prediction interval from the standard errors?? Here's the example: logX<-rnorm(100)

Dear All, I am struggling to find an R package which contains a function for building a Gaussian Process model for binary classification which may produce prediction intervals for predicted probabilities. I would be grateful if somebody could point me to such package. Thank you very much in advance. DK [[alternative HTML version deleted]]

Dear Knowledgeable R Community Members, Please excuse my ignorance, I apologize in advance if this is an easy question, but I am a bit stumped and could use a little guidance. I have a finite mixture modeling problem -- for example, a 2-component gaussian mixture -- where the components have a large overlap, and I am trying to use the "mclust" package to solve this problem. I need

Hello, I know that Mclust does the fitting on its own but I am trying to implement an optimization with the aim to generate a the mixture gaussian with the combine moments as closed as possible to the moment of my return distribution. The objective is to Min Abs((Mean Ret - MeanFit)/Mean Fit) + Abs((Std Ret -Stdev Fit)/Stdev) + Abs((Sk Ret-Sk fit)/Sk Fit) + Abs((Kurt Ret- Kurt Fit)) Taking

Dear all, I am using the book "Generalized Linera Models and Extension" by Hardin and Hilbe (second edition, 2007) at the moment. The authors suggest that instead of OLS models, "the log link is generally used for response data that take only positive values on the continuous scale". Of course they also suggest residual plots to check whether a "normal" linera model

I have kindly asked for help and I am sad to receive such a reply from some on the r-help list. I did google it prior to sending my request, and I could not find any R package which provides GP classification model which produces prediction intervals for each sample. I would be grateful if anybody could inform me about it. Thank you. ________________________________ From: Bert Gunter

Hi, Is there any package that I can use to simulate the Gaussian Mixture Model , which is a mixture modeling method that is widely used in statistical learning theory. I know there is a mclust, however, I think it is a little bit different from my problem. Thanks very much.. regards. -------------------------- Peng Jiang ?? Ph.D. Candidate Antai College of Economics &

Can anyone advise me on some pratical papers or books On Gaussian Copulas? Anything in the genre of Copulas Dummies Would be a help. As simpe, and approachable with minimal pedantic style. Thanks, Neil -------------------------------------------------------- This information is being sent at the recipient's reques...{{dropped:16}}