similar to: Getting parameters of Gaussian fit in density

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

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:

Hello, I have done some research about breakpoints (I am not a statistician) and I found out about the breakpoint, strucchange and segmented packages in R allowing to find breakpoints assuming linear model. However, I would like to fit a periodic time series with a non linear (periodic) model, and I was wondering how I could find breakpoints for this model in R. Is it even possible ? My model

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 requested help a couple of weeks ago creating a dipole field in R but receieved no responses. Eventually I opted to create a 3d sinusoidal plot and concatenate this with its inverse as a means for a "next best" situation. It seems that this isn't sufficient for my needs and I'm really after creating a continuous 3d gaussian mesh with a "positive" and

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 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,

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

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),

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

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.

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

Hello, I'm Emanuela, I'm implemented a survival analysis and I'm trying to use a frailty model with inverse gaussian distribution, but I'm not able to find the right code, because it seems to be only a gamma and a gaussian distribution. Is there also the inverse gaussian distribution? Thanks a lot Emanuela Rossi

>From what I understand, loess in R uses the standard tri-cube function. SAS/INSIGHT offers loess with Gaussian weights. Is there a function in R that does the same? Also, can anyone offer any references comparing properties between tri-cube and Gaussian weights in LOESS? Thanks. - Andr? -- View this message in context:

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)

Hi, I have a sample of size 100 from a function in interval [0,1] which can be assumed to come from a scalar-valued stationary Gaussian process. There are about 500 observation points in the interval. I need an effective and fast way to simulate from the Gaussian process conditioned on the available data. I can of course estimate the mean and 500x500 covariance matrix from data. I have searched

> On Dec 11, 2017, at 8:06 AM, Damjan Krstajic <dkrstajic at hotmail.com> wrote: > > I have kindly asked for help and I am sad to receive such a reply from some on the r-help list. > > Well, you only said you were `struggling' to find a package. Bert may well have done the Google search himself and found numerous resources on such models including links to R (as I

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

Hello, I'm wondering if there are any packages/functions that can perform conditional sequential gaussian simulation. I'm following an article written by Grunwald, Reddy, Prenger and Fisher 2007. Modeling of the spatial variability of biogeochemical soil properties in a freshwater ecosystem. Ecological Modelling 201: 521 - 535, and would like to explore this methodology.