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Hi, I'm trying to fit the following model to data using 'nls': y = alpha_1 * beta_1 * exp(-beta_1 * x) + alpha_2 * beta_2 * exp(-beta_2 * x) and the call I've been using is: nls(y ~ alpha_1 * beta_1 * exp(-beta_1 * x) + alpha_2 * beta_2 * exp(-beta_2 * x), start=list(alpha_1=4, alpha_2=2, beta_1=3.5, beta_2=2.5), trace=TRUE, control=nls.control(maxiter = 200)) So the model has 4 para...

...9; 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 1.873 on 11 degrees of freedom Multiple R-Squared: 0.9403, Adjusted R-squared: 0.9241 F-statistic: 57.78 on 3 and 11 DF, p-value: 5.082e-07 In the book, the estimates are From this book, mu=33.8 alpha_1=6.017 alpha_2=0.942 beta=0.899 Is it possible to obtain this estimates and their standard errors from the model I fitted? Thanks in advance Tomas Goicoa [[alternative HTML version deleted]]

Dear Sir/Madam, I apologize for any cross-posting. I got a simple question, which I thought the R list may help me to find an answer. Suppose we have Y_1, Y_2, ., Y_n ~ Poisson (Lambda_i) and Lambda_i ~Gamma(alpha_i, beta_i). Empirical Bayes Estimator for hyper-parameters of the gamma distr, i.e. (alpha_t, beta_t) are needed. y=c(12,5,17,14) n=4 What about a Hierarchal B ayes

...lysis of Financial Time Series" by Ruey S. Tsay, and I was succesfull, but I had to use "for" loop, which is quite slow. The loop is necessary, since you need to calculate recursive sequence. Is there a faster way to do this in R, without using loops? The model is such: r_t = \mu + \alpha_2 r_{t-2} + a_t a_t = \sigma_t\varepsilon_t \sigma_t^2 = \beta_1a_{t-1}^2+\beta_2\sigma_{t-1}^2+ 1_{\{a_{t-1}>0\}}(\gamma_0+ \gamma_1a_{t-1}^2+\gamma_2\sigma^2_{t-1}) It is asummed that \varepsilon_t are iid and normal with zero mean and variance one. The data given is r_t, and you have to estim...

.../12 -1/12 -1/12 1/4 (ginverse() will be available in S+4.0 and fractions(), now available in the MASS2 library, simply displays numbers in fractional form so that patterns are more obvious). Thus the phi's are identified by requiring that they add to zero, and *alpha_l = (phi_2 - phi_l )/2, *alpha_2 = [phi_3 - (phi_l + phi_2)/2] / 3 &c. When the columns of C are not mutually orthogonal the story is not quite so obvious, though. For example take a C matrix using contr.sdif (available in the MASS2 library) > contr.sdif(4) [2-1] [3-2] [4-3] 1 -0.75 -0.5 -0.25 2 0.25 -0.5 -0.25 3 0.25 0.5...