search for: gaussian

Dear R-user: I am using survreg(Surv()) for fitting a Tobit model of left-censored longitudinal data. For logarithmic transformation of y data, I am trying use survreg.distributions in the following way: tfit=survreg(Surv(y, y>=-5, type="left")~x + cluster(id), dist="gaussian", data=y.data, scale=0, weights=w) my.gaussian<-survreg.distributions$gaussian my.gaussian$name="lognormal" my.gaussian$dist<-my.gaussian tfit=survreg(Surv(y, y>=-5, type="left")~x + cluster(id), dist=my.gaussian, data=y.data, scale=0, weights=w) If I run these...

...x DBUS_SESSION_BUS_ADDRESS="unix:abstract=/tmp/dbus-adjIuc8A5l,guid=d2289573889e3cae1ac3908354de3192" declare -x DESKTOP_SESSION="gnome-classic" declare -x DISPLAY=":0" declare -x FTP_PROXY="http://hproxy.iitm.ac.in:3128/" declare -x G09BASIS="/usr/local/gaussian/g09/basis" declare -x GAUSS_ARCHDIR="/usr/local/gaussian/g09/arch" declare -x GAUSS_BSDDIR="/usr/local/gaussian/g09/bsd" declare -x GAUSS_EXEDIR="/usr/local/gaussian/g09/bsd:/usr/local/gaussian/g09/local:/usr/local/gaussian/g09/extras:/usr/local/gaussian/g09" decl...

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...

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...

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. [[alternative...

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 am trying with this question again: I would like to test few distributional assumptions for some behavioral response data. There are few theories about true distribution of those data, like: normal, lognormal, gamma, ex-Gaussian (exponential-Gaussian), Wald (inverse Gaussian) etc. The best way would be via qq-plot, to show to students differences. First two are trivial: qqnorm(dat$X) qqnorm(log(dat$X)) Then, things are getting more "hairy". I am not sure how to make plots for the rest. I tried gamma with: qqma...

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,...

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]]

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 senten...

...(Intercept) -1.341254 0.089969 -14.908 <2e-16 *** herbc -0.007303 0.003469 -2.105 0.0353 * herbht 0.024064 0.002659 9.051 <2e-16 *** --- Null deviance: 1699.0 on 1180 degrees of freedom Residual deviance: 1569.8 on 1178 degrees of freedom AIC: 2311.4 (2) Gaussian with sqrt link model > gaus.sqrt<-glm(cnt~herbc+herbht,family=gaussian(link="sqrt"),data=sotr,start=c(0.1,-0.004,0.01)) > summary(gaus.sqrt) Call: glm(formula = cnt ~ herbc + herbht, family = gaussian(link = "sqrt"), data = sotr, start = c(0.1, -0.004, 0.01)) Coef...

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) logY<--2-0.5*logX+rnorm(10...

...an = 1, sd =2) mu <- rep(m, rep(10,10)) test1 <- data.frame(Time = qsurvreg(x, mean = mu, scale= 0.5, distribution = "weibull"), Status = rep(1, 100), Unit = gl(10,10) ) mod1 <- survreg(Surv(Time, Status) ~ 1 + frailty.gaussian(Unit), data = test1) > mod1 ... coef se(coef) se2 Chisq DF p (Intercept) 0.987 0.582 0.0457 2.87 1.00 9.0e-02 frailty.gaussian(Unit) 85.26 8.95 1.4e-14 Scale= 0.434 Iterations: 5 outer, 17 Newton-Raphson Variance of...

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 to decompose that mixture into its 2 components which will need to be plotted. What I don't know how to do is: (1) restrict the number of components to...

...ich produces prediction intervals for each sample. I would be grateful if anybody could inform me about it. Thank you. ________________________________ From: Bert Gunter <bgunter.4567 at gmail.com> Sent: 11 December 2017 15:50 To: Damjan Krstajic Cc: r-help at r-project.org Subject: Re: [R] Gaussian Process Classification R packages Google it! "R Gaussian process model binary classification." Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom C...

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: http://r.789695.n4.nabble.com/tri-cube-and-gaussian-weights-in-loess-tp3263934p3263934...

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 &lt...

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 hav...

...capabilities of a package or failed to grasp an inobvious way to use the package to reach your goal. In any case, providing some background of why you think the obvious leads do not work in your case can be helpful. Doing that search myself I see links to R packages, R functions, and to "The Gaussian Processes Web Site" which has a table of possibly relevant softwares. It seems like there is a lot there to digest. HTH, Chuck > 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...