search for: lambda_i

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 estimators? Any relevant work and codes in R (or S+) is highly appreciated. Kind reg...

...;invalid labels; length", nl, "should be 1 or", length(levels))) class(f) <- c(if (ordered) "ordered", "factor") f } ----------------------------------------------------------------------------- The model and Assumptions log(m_ij)= lambda + lambda_i + lambda_j + lambda_ij where, lambda_ij = lambda_ji for i not equal to j and lambda_i(A) = lambda_i(B) Likelihood equation is m_ij =(n_ij + n_ji)/2 For symmetry m_(ij)=m_(ji) "R program" does not recognised "symm" pathern, that is (1,1), (1,2) and so on but "S-plus&quo...

..., for each patient I know his/her age and whether he/she exhibits certain symptoms: age symptom1 symptom2 50 0 1 53 0 0 70 1 1 ... I have started off by fitting simple models with forms like Prob(patient of age t shows symptom i) = 1 - Exp(-lambda_i * t) or Prob(patient of age t shows symptom i) = 1 - A_i * Exp(-lambda_i * t) Now, I want to plot my functional forms against the data, to get a rough idea of how they look. If I do something simple like xyplot(symptom1 ~ age) I get the data points, but it's hard to see what's going...

Hi, has anyone ever seen implemented in R the following "geodesic" distance between positive definite pxp matrices A and B? d(A,B) = \sum_{i=1}^p (\log \lambda_i)^2 were \lambda is the solution of det(A -\lambda B) = 0 thanks stefano

Dear R-users, I would like to fit a fully parametric proportional hazard model with a weibull baseline hazard and a logit link function. This is, the hazard function is: lambda_i (t) = lambda_0 (t) psi (x_i* beta) where lambda_0 is a weibull distribution and psi a logistic distribution. Does someone know a package and/or function on R to do this? Thanks. -- M.L. Avendaño [[alternative HTML version deleted]]

Hi, Given a positive integer N, and a real number \lambda such that 0 < \lambda < 1, I would like to generate an N by N stochastic matrix (a matrix with all the rows summing to 1), such that it has the second largest eigenvalue equal to \lambda (Note: the dominant eigenvalue of a stochastic matrix is 1). I don't care what the other eigenvalues are. The second eigenvalue is

Hi, If I set df=2 in my smooth.spline function, is that equivalent to running a linear regression through my data? It appears that df=# of data points gives the interpolating spline and that df = 2 gives the linear regression, but I just want to confirm this. Thank you, Steven

Hi, I would like to know whether there exist algorithms to compute the coefficients or, at least, the degree of the minimal polynomial of a square matrix A (over the field of complex numbers)? I don't know whether this would require symbolic computation. If not, has any of the algorithms been implemented in R? Thanks very much, Ravi. P.S. Just for the sake of completeness, a

Dear R-listers, I want to reperfrom a poisson distribution test that presented in a recent-published biological research paper (Plant Physiology 2008, vol 148, pp. 1189-1200). That test is about the occurrence number of a kind of gene in separate chromosomes. For instance: The observed gene number in chromosome A is 36. The expected gene number in chromosome A is 30. Then, the authors got a

For example, PcaHubert, how to get the variance explained which are similar to those concepts in traditional PCA? In traditional PCA, you have a bunch of eigenvalue lambdas... and you sort the lambdas from the biggest to the smallest, the lambda_i / (sum of all lambdas) is the variance explained by that principal component... how to obtain the equivalent concepts in PcaHubert? Thanks a lot! [[alternative HTML version deleted]]

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =