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Hallo, I would like to add the marginal distributions along the X and the Y axis to a scatter plot. Can anybody help me, please? Thank you, Paolo -- Paolo Bulla Istituto di Metodi Quantitativi Universit?? "L. Bocconi" viale Isonzo 25 20136 Milano paolo.bulla at unibocconi.it

...ion of gcc (4.x), so I downgraded it to gcc 3.4.5 but it does not work either. Could anyone help me with this installation, please? Thanks, Paolo -- Paolo Bulla Istituto di Metodi Quantitativi Universit?? "L. Bocconi" Tel. +39 02.5836.5651 viale Isonzo 25 20136 Milano paolo.bulla at unibocconi.it

Hi, I woul like to know if it is possible to have a "R code" to generate EM Algorithm for a normal bivariate mixture. Best regard, S.F.

...llocation of 512 Mb. I ran the function memory.size() and obtained 392000000 more or less. Can anyone help me? Thanks in advance. Massimiliano Copetti -- Massimiliano Copetti, PhD Student Institute of Quantitative Methods L.Bocconi University Viale Isonzo 25 20135 Milano (Italy) http://www.unibocconi.it http://spazioinwind.libero.it/maxcop78

Hello, I have to compute a multiple summation (not an integration because the independent variables a are discrete) for all the values of a function of several variables f (x_1,...,x_n), that is sum ... sum f(x_1,...,x_n) x_1 x_n have you some suggestion? Is it possible? I know that for multiple integration there is the function adapt, but it has at most n=20. In my case n depends on the

Dear R-users, I'm using lmer to fit two-level logistic models and I'm interested in predicted probabilities that I get in this way (using "fitted"): glm1 = lmer(XY$T1~X1 + X2 + X3 + (1|Cind), family=binomial) #estimation of a two-level logit model fit1=fitted(glm1) # I get the fitted linear predictor ilog = function(x) { 1/(1 + exp(-x)) } ps1=ilog(fit1) # In

hi i'm looking for a gibbs sampling algorithm for R for the case of mixture of K normals, and in particular for the case of bivariate normals. i'd be grateful if anyone could send its own R-routine, at least for the univariate case. thank you in advance matteo

Dear R-users, I'm using lmer to fit two-level logistic models and I'm interested in predicted probabilities that I get in this way (using "fitted"): glm1 = lmer(XY$T1~X1 + X2 + X3 + (1|Cind), family=binomial) #estimation of a two-level logit model fit1=fitted(glm1) # I get the fitted linear predictor ilog = function(x) { 1/(1 + exp(-x)) } ps1=ilog(fit1) # In