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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) # this is for the...

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 help mailing list archive at Nabble.com.

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 and g. Using optim, I can get the optimisation to work when the non-linearity is included but not constrained, but gives po...

Hi, I'd like to take difference for a sequence a between a_i and a_i-2, for instance, a<-c(2,3,4,8,1) I need (2, 5, -3) as a result. If not using a for loop, can anyone help me? Thanks a lot. Dot -- View this message in context: http://www.nabble.com/R-help--how-to-take-difference-in-next-two-elements-tp18927968p18927968.html Sent from the R help mailin...

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...

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 N_2) is between 0 and 1 so that N_1+N_2=1, so if N_1=(0,.166,.180,.250,.325,.374), then N_2=(1.000, 0.834, 0.820, 0.750, 0.675, 0.626) a_i (a_1 or a_2) So, for gamma_i (in thi...

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

Hi, I am solving a projection pursuit regression problem, of the form y = \sum_i f_i (a_i^T x), where a_i are unknown directions, while f_i are unknown univariate link functions. The following is known about each f_i: 1. f_i (0) = 0  (that is, each f_i passes through the origin) 2. f_i is monotonic. Is there a way to ensure that the function ppr() in R produces solutions that res...

Muy buenas, Tengo la siguiente duda/problema, He optimizado con éxito un problema de este tipo: \sum f(x_i) donde f es una curva exponencial (función no lineal) sujeto a: a_i < x_i < b_i y \sum f(x_i) < Presupuesto Vamos, es repartir un presupuesto forzando a que inviertas como poco a_i y como mucho b_i para cada i Esto lo hecho correctamente usando el paquete: http://cran.r-project.org/web/packages/nloptr/index.html Uso un algoritmo que no necesita gradien...

...nd 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 questions of the form (for continuous variables) sum over i a_i X_i < c where X_i are the cont. variables, a_i and c consts. However, this extended set is not implemented in either the R `tree' package nor the Splus one, to the best of my knowledge. (I believe there is some original CART package, but I don't know about that). Anyway, I was wondering...

Dear useRs (called Frank Harrell, most likely), after having preached for years to my medical colleagues to be cautious with stepwise selection procedures, they chanted back asking for an alternative when using mixed models. There is a half dozen laXXX packages around for all types of linear models, but as far I see there is none for mixed models such as lme. Even boot.stepAIC (which I

...m 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 factor with K levels). The ``usual'' structure (using ``treatment contrasts'') gives y = a + a_i + b*x + b_i*x + E i = 2, ..., K. (So that b is the slope for the baseline group, and...

....), ie. fits a random effect intercept term. The reason why I want to do this is test for the significance of the random effect intercept term. anova( , ) does not work for an lm model and lme model together. 2) Is there some nice way of handling linear models which are of the form response_ij = a_i + b_i x_ij + \epsilon_ij where a_i and b_i are fixed effects, x_ij is given (continuous) data, \epsilon_ij ~ N(0, \sigma^2), and the i's range over some group? This is basically a group of regression models, but I want them handled as one unit for the purposes of estimation of \sigma^2 etc. I...

Dear R community, Confused by some of my lab results I ask for the definition of the null hypothesis of a two-way analysis of variance in R (anova() and aov()). Starting with the following model y = a_i + b_j , i in A and j in B is the tested null hypothesis H_0: a_i = 0 for all i in A or H_0: a_m = a_n for any m and n in A? Consequently the same questions for interaction effects. Starting with the model y = a_i + b_j + f_ij , i in A and j in B is the tested null hypothesis H_0: f_ij = 0...

Hi all, I am doing some experiment studies... It seems to me that with different combination of 5 parameters, the end results ultimately converged to two scalars. That's to say, some combinations of the 5 parameters lead to one end result and some other combinations of the 5 parameters lead to the other end result (scalar). I am thinking of this is sort of something like clustering or

...t its usefulness is confined to the sort of data which arise in survival/proportional hazard applications. My question is: is 'clogit' capable of a general conditional logistic analysis? E.g. given a set of data on binomial experiments with Y=1 r_i times out of n_i, associated with levels A_i and B_i of factors A and B at N_A and N_B levels, would clogit(Y ~ A+B, method=c(Exact")) generate something sensible containing the results of a standard exact conditional logistic regression of Y on A and B? With thanks, Ted. -----------------------------------------------------------...

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, Mirko. -- Dr. M. Luedde <Mirko.Luedde at CellControl.De> CellControl Biomedical Laboratories AG Am Klopferspitz 19, 82152 Martinsried +49-89-895275-0 +49-1...

Dear R-help, I am new to optim function and need some help with optimization. Problem description: I am trying to optimize a weights vector such that it produce maximum value for a function maxVal. The optimization is subjected to constraint. The constraints are a) Min weight should be greater than or equal to Zero. b) Max weight should be less than or equal to 1 c) Sum of the

...> [2] %bb1 {1,2} > [2] %bb {3,4} > [2] %entry {5,6} > [2] %bb8 {7,20} > [3] %bb7 {8,9} > [3] %bb2 {10,11} > [3] %bb6 {12,13} > [3] %bb5 {14,19} > [4] %bb4 {15,16} > [4] %bb3 {17,18} > 0 opt 0x085643e8 > Stack dump: > 0. Program arguments: opt > -load=/home/a_i/llvm/llvm-2.7/Release/lib/ConsDumper.so -consdumper -f > -o pbzip2_2s.bc pbzip2.bc -debug > 1. Running pass 'dump constraints' on module 'pbzip2.bc'. > Segmentation fault/ > I have no hint about this. Does anyone know about the reason? Not yet. However I would love to...

...ed by class, and the number of SV are stored in the nSV component. So you should get the start indices with sth. like cumsum(c(1,model$nSV)). g., David. ---------------- For class prediction in the binary case, the class of a new data vector ``n'' is usually given by *the sign* of Sum(a_i * y_i * K(x_i, n)) + rho i where x_i is the i-th support vector, y_i the corresponding label, a_i the corresponding coefficiant, and K is the kernel (in your case, the linear one, so K(u,v) = u'v). Now, ``libsvm'' actually returns a_i * y_i as i-th coefficiant and the *negative* rh...