similar to: R-help? how to take difference in next two elements

Hi all, I have a problem using rdirichlet{gtools}. For Dir( a1, a2, ..., a_n), its mode can be found at $( a_i -1)/ ( \sum_{i}a_i - n)$; The means are $a_i / (\sum_{i} a_i ) $; I tried to study the above properties using rdirichlet from gtools. The code are: ############## library(gtools) alpha = c(1,3,9) #totoal=13 mean.expect = c(1/13, 3/13, 9/13) mode.expect = c(0, 2/10, 8/10) #

I'm relatively new to R, but I'm attempting to do a non-linear maximum likelihood estimation (mle) in R, with the added problem that I have a non-linear constraint. The basic problem is linear in the parameters (a_i) and has only one non-linear component, b, with the problem being linear when b = 0 and non-linear otherwise. Furthermore, f(a_i) <= b <= g(a_i) for some (simple) f

I would like to code the following in R: a1(b1+b2+b3) + a2(b1+b3+b4) + a3(b1+b2+b4) + a4(b1+b2+b3) or in summation notation: sum_{i=1, j\neq i}^{4} a_i * b_i I realise this is the same as: sum_{i=1, j=1}^{4} a_i * b_i - sum_{i=j} a_i * b_i would appreciate some help. Thank you. -- View this message in context: http://r.789695.n4.nabble.com/summation-coding-tp4646678.html Sent from the R

Dear R-Users, I need to find a smooth function f() and coefficients a_i that give the best fit to y ~ a_0 + a_1*f(x_1) + a_2*f(x_2) Note that it is the same non-linear transformation f() that is applied to both x_1 and x_2. So my first question is how can I do it in R? A more general question is this: suppose I have a utility function U(a_i, f()), where f() is say a spline. Is there a general

Hello, I'm very new to using R, but I was told it could do what I want. I'm not sure how best to enter the information but here goes... I'm trying to transfer the following integral into R to solve for ln(gamma_1), on the left, for multiple instances of gamma_i and variable N_i. gamma_i is, for example, (0, 0.03012048, 0.05000000, 0.19200000, 0.44000000, 0.62566845) N_i (N_1 or

Dear R people, I was recently reading the book `Classification and Regression Trees' by Breiman. This book talks about the CART program. Both Splus and R have implementations of this. However, the book talks about the possibility of extending the existing `standard' set of questions (for continuous variables, these are of the form X < c where X is the variable, c some const) to

I am interested in whether the slopes in a linear model are different from 0. I.e. I would like to obtain the slope estimates, and their standard errors, ``relative to 0'' for each group, rather than relative to some baseline. Explicitly I would like to write/represent the model as y = a_i + b_i*x + E i = 1, ..., K, where x is a continuous variate and i indexes groups (levels of a

Dear R users, I have a couple of linear model related questions. 1) How do I produce a fixed effect linear model using lme? I saw somewhere (this may be Splus documentation since I use Splus and R interchangeably) that using lme(...,random= ~ -1 | groups,...) works, but it gives the same as lme(...,random= ~ 1 | groups,...), ie. fits a random effect intercept term. The reason why I want to do

Hi there, I'm quite new to R (and statistics), and I like it (both)! But I'm a bit lost in all these packages, so could someone please give me a hint whether there exists a package for fitting exponential curves (of the type t --> \sum_i a_i \exp( - b_i t)) on a noisy signal? In fact monoexponential decay + polynomial growth is what I'd like to try. Thanks in advance,

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

On 08/05/2010 06:46 AM, Wenbin Zhang wrote: > Hi all, > I'm using postDominatorTree to do some program analysis. My code works > well for small tests, but when I run it on real applications, the > following error occurs: > /Inorder PostDominator Tree: DFSNumbers invalid: 0 slow queries. > [1] <<exit node>> {0,21} > [2] %bb1 {1,2} > [2] %bb {3,4} > [2]

I'm working with a orthogonal and nested model (mixed). I have four factors, A,B,C,D; A and B are fixed and orthogonal C is nested in AB interaction and finally, D is nested in C. I would like to model the following Y_ijklm=Mu+A_i+B_j+AB_ij+C_k(ij)+D_l(k(ij))+Error_m(...) I used the next command >summary(aov(abund~A*B + C % in % A:B + D % in % C % in % A:B ,datos)) Is it the correct

Dear r-helpers, I am working with plm package. I am trying to fit a fixed effects (or a 'within') model of the form y_it = a_i + b_i*t + e_it, i.e. a model with an individual-specific intercept and an individual- specific slope. Does plm support this directly? Thanks in advance! Otto Kassi

Hi, If I do a standard svm regression with e1071 x <- seq(0.1, 5, by = 0.05) y <- log(x) + rnorm(x, sd = 0.2) m <- svm(x, y) we can do predict(m,x) to get the fitted values. But what if I wan tho get them by hand? Seem to me like it should be w = t(m$coefs)%*%m$SV x.scaled = scale(x, m$x.scale[[1]], m$x.scale[[2]]) t(w %*% t(as.matrix(x.scaled))) - m$rho but this is wrong If i

Hello, Apologies if this question has already arised, hope you can help me to the find the solution to this or point the place to look at. I have a multidimensional piece-wise regression linear problem, i.e. to find not only the regression coefficients for each "interval" but also the beginning and ends of the intervals. To simplify it to the one dimensional case and two intervals,

I'll try the trunk, as well as check my code again. If indeed it's not fixed, I'll try to post a triggering case here. Thanks for the advice~ Best, --Wenbin ----- Original Message ----- From: "Tobias Grosser" <grosser at fim.uni-passau.de> To: "Wenbin Zhang" <zhangwen at cse.ohio-state.edu> Cc: <llvmdev at cs.uiuc.edu> Sent: Thursday, August

Hi there, I am looking to fit a two-way random effects model to an *unblalanced* layout, y_ijk = mu + a_i + b_j + eps_ijk, i=1,...,R, j=1,...,C, k=1,...,K_ij. I am interested first of all in estimates for the variance components, sigsq_a, sigsq_b and sigsq_error. In the balanced case, there are simple (MM, MLE) estimates for these; In the unbalanced setup,

Wenbin Zhang wrote: > I'll try the trunk, as well as check my code again. If indeed it's not > fixed, I'll try to post a triggering case here. > Thanks for the advice~ > Did you run the -mergereturn pass (it might also be called UnifyExitNodes in the source code)? This is the pass that ensures that each function has exactly one basic block that returns control to the

I'm using the lme function in nmle to estimate the variance components of a fully nested two-level model: Y_ijk = mu + a_i + b_j(i) + e_k(j(i)) lme computes estimates of the variances for a, b, and e, call them v_a, v_b, and v_e, and I can use the intervals function to get confidence intervals. My understanding is that these intervals are probably not that robust plus I need intervals on the

I am new to generalized linear models and studying McCullagh & Nelder (1989). Especially, I have a problem resembling the \"cheese taste\" example (5.3.1. p. 109) of the book. I tried to analyse the cheese example with R but failed to do so because R allowed me to use logit link function only with binary family that supposes 0 <= y <= 1. Do I need to scale the y\'s or