similar to: Network Drives Dropping Out

Dear all, I tried a simple naive Bayes classification on an artificial dataset, but I have troubles getting the predict function to work with the type="class" specification. With type= "raw", it works perfectly, but with type="class" I get following error : Error in as.vector(x, mode) : invalid 'mode' argument Data : mixture.train is a training set with 100

Hi, I'm using Samba 3.0.5 on a Fedora Linux box. I have in my smb.conf logon home = \\%L\%u\.profile logon path = \\%L\profiles$\%u logon drive = H: [profiles$] comment = Windows XP profile directory path = /home/profile [homes] comment = home dirs browseable = no writeable =yes I have a mixture of Win95 and WinXP clients The Win95 clients map the home drive correctly to the users home

Hi all, I can see the RH7.3 linux box I've configured with the latest Redhat RPM samba package in the Windows 2000 network neighbourhood, but when I double click the W2000 computer icon I get the message "\\Linuxbox is not accessible, The network path was not found." The network is a small switched lan, sadly with only one unix box (RH7.3) and a mixture of Windows2000 and 98

Dear R-help, I am doing clustering via finite mixture model. Please suggest some packages in R to find clusters via finite mixture model with continuous variables. And also I wish to verify the distributional properties of the mixture distributions by fitting the model with lognormal, gamma, exponentials etc,. Thanks in advance,  warm regards,Ms.Karunambigai M PhD Scholar Dept. of Biostatistics

I apologise for not including a reproducible example with this query but I hope that I can make things clear without one. I am fitting some finite mixture models to data. Each mixture component has p parameters (p=29 in my application) and there are q components to the mixture. The number of data points is n ~ 1500. I need to select a good q and I have been considering model selection methods

I am trying to generate a set of data points from a Gaussian mixture model. My mixture model is represented by a data frame that looks like this: > gmm weight mean sd 1 0.3 0 1.0 2 0.2 -2 0.5 3 0.4 4 0.7 4 0.1 5 0.3 I have written the following function that generates the appropriate data: gmm_data <- function(n, gmm) { c(rnorm(n*gmm[1,]$weight, gmm[1,]$mean,

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

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 Weibull mixture -- where the components have a large overlap, and I am trying to adapt the "mclust" package which concern to normal

Offlist, because this is (a) an opinion and (b) about statistics and therefore offtopic. I don't know whether any such package exists, but I would predict that this is likely to be overdetermined (too many parameters) and therefore unlikely to be a successful strategy. Fitting a mixture of Gaussians is already difficult enough. Feel free to ignore, of course, and no need to reply. Cheers,

Dear list, Can anyone provide a package or code for estimating the parameters of a mixture of c (c >=2) univariate normal distributions? I've tried the algorithm provided by Venables & Ripley (1999) p 263, for the mixture of two normal, but I don't find the "ms" function in R. I've used nls instead, but I'm not sure if it works the same. The data I have is very

I don?t see how neither a) or b) applies to this question nor the technical merit of the remark about mixture models. Do you have a suggestion for a more appropriate forum for this issue/question? (stackoverflow basically sent me here). Kind regards > On 29. Jun 2017, at 16:58, Bert Gunter <bgunter.4567 at gmail.com> wrote: > > Offlist, because this is (a) an opinion and (b)

Hi, I wonder if the function "density" outputs the gaussian mixture formula that is estimated from the input data, assuming a gaussian model is used at each data point ? I want to take the derivative of the finally estimated gaussian mixture formula for further analysis. Thanks in advance for any help that you can offer me! Hui

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

Hello! I am new to R (before used python exclusively and would actually call the R solution for this issue inside a python notebook, hope that doesn?t disqualify me right of the batch). Right now I am looking for a piece of software to fit a 1D data sample to a mixture of t-distributions. I searched quite a while already and it seems to be that this is a somehwat obscure endeavor as most

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 &

Would package "teigen" help? Ranjan On Thu, 29 Jun 2017 14:41:34 +0200 vare vare via R-help <r-help at r-project.org> wrote: > Hello! > > I am new to R (before used python exclusively and would actually call the R solution for this issue inside a python notebook, hope that doesn?t disqualify me right of the batch). > > Right now I am looking for a piece of

Hi everybody, we are a pool of phd students and we have a question about AIC. We are interested in calculating the AIC for a mixture model on galaxies data. So far we have found AIC works only for regression models, whereas we need AIC for a mixture of Normal with mean, sd and weights given by our EM algorithm. May you help us? thanks in advance

This is maybe not directly an R problem but I have used R to try to solve it so I think somebody may be able to help. I have a mixture model with three components and a quadratic Scheffe polynomial p1x1+p2x2+p3x3+p12x1x2+p13x1x3+p23x2x3 fitted to the response. Now I'd like to compute the mixture corresponding the maximum response. Model for Y1 has the parameters p1=124.02 p2=60.973 p3=41.479

Hi R-users,I'm trying to solve a non linear equation, to find the degrees of freedom of a mixture of t student. I'm sure i wrote the minimization equation in the right way, but when i try to run the EM algorithm to estimate the parameters of the mixture, the following error will appear: Error in uniroot(function(z) log(z/2) - digamma(z/2) + 1 - log((z + d)/2) + : f() values at end

Hello, I have some parameters from Mclust function. The parameters are in the form *parametersDf * * mu_1 mu_2 var_mc1 var_mc2 c1 c2 * *2 1.357283 2.962736 0.466154 0.1320129 0.5258975 0.4741025 * *21 8.357283 9.962736 0.466154 0.1320129 0.5258975 0.4741025 * Each row in the above data frame