similar to: maximum likelihood with constraints in R

Hi,I know only mle function but it seems that in mle one can only specify the bound of the unknowns forming the likelihood function. But I would like to specify something like, a = 2b or a <= 2b where 'a' and 'b' could be my parameters in the likelihood function. Any help would be really appreciated. Thank you!- adschai [[alternative HTML version deleted]]

Hi there, I am trying to use linear regression to solve the following equation - y <- c(0.2525, 0.3448, 0.2358, 0.3696, 0.2708, 0.1667, 0.2941, 0.2333, 0.1500, 0.3077, 0.3462, 0.1667, 0.2500, 0.3214, 0.1364) x2 <- c(0.368, 0.537, 0.379, 0.472, 0.401, 0.361, 0.644, 0.444, 0.440, 0.676, 0.679, 0.622, 0.450, 0.379, 0.620) x1 <- 1-x2 # equation lmFit <- lm(y ~ x1 + x2) lmFit Call:

Hi there I am trying to learn how to compute mle in R for a multinomial negative log likelihood function. I am using for this the book by B. Bolker "Ecological models and data in R", chapter 6: "Likelihood an all that". But he has no example for multinomial functions. What I did is the following: I first defined a function for the negative log likelihood:

Dear All,   I could use a bit of help here, this function is hard to figure out (for me at least) I have the following so far:   PKindex<-data.frame(Subject=c(1),time=c(1,2,3,4,6,10,12),conc=c(32,28,25,22,18,14,11)) Dose<-200 Tinf <-0.5   defun<- function(time, y, parms) {  dCpdt <- -parms["kel"] * y[1]  list(dCpdt)  } modfun <- function(time,kel, Vd) {   out <-

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions

Dear R users: I encountered difficulties in michaelis-menten equation. I found that when I use right model definiens, I got wrong Km vlaue, and I got right Km value when i use wrong model definiens. The value of Vd and Vmax are correct in these two models. #-----right model definiens-------- PKindex<-data.frame(time=c(0,1,2,4,6,8,10,12,16,20,24),

Dear R-Help, I also send the following post by e-mail to you, however I try to post it here aswell. My name is Henrik and I am currently trying to solve a Maximum Likelihood optimization problem in R. Below you can find the output from R, when I use the "BFGS" method: The problem is that the parameters that I get are very unreasonable, I would expect the absolute value of each

Dear list, I'm migrating a project from Matlab to R, and I'm facing a relatively complicated problem for nls. My objective function is below: >> objFun <- function(yEx,xEx,tEx,gamma,theta,kappa){ yTh <- pdfDY(xEx,tEx,gamma,theta,kappa) sum(log(yEx/yTh)^2) } The equation is yTh=P(xEx,tEx) + noise. I collect my data in: >> data <-

Hello, I am struggling for some time now to estimate AR(1) process for commodity price time series. I did it in STATA but cannot get a result in R. The equation I want to estimate is: p(t)=a+b*p(t-1)+error Using STATA I get 0.92 for a, and 0.73 for b. Code that I use in R is: p<-matrix(data$p) # price at time t lp<-cbind(1,data$lp) # price at time t-1

Dear R users, I am new to R. I would like to find *maximum likelihood estimators for psi and alpha* based on the following *log likelihood function*, c is consumption data comprising 148 entries: fn<-function(c,psi,alpha) { s1<-sum(for(i in 1:n){(c[i]-(psi^(-1/alpha)*(lag(c[i],-1))))^2* (lag(c[i],-1)^((-2)*(alpha+1)) )}); s2<- sum(for(m in 1:n){log(lag(c[m],-1)^(((2)*alpha)+2))});

I have an optimization question that I was hoping to get some suggestions on how best to go about sovling it. I would think there is probably a package that addresses this problem. This is an ordering optimzation problem. Best to describe it with a simple example. Say I have 100 "bins" each with a ball in it numbered from 1 to 100. Each bin can only hold one ball. This optimization is

Kia ora I'm a using lme (from nlme package) with data similar to the Orthodont dataset and am getting positive log-likelihoods (>100). This seems usual and I wondered if someone could offer a possible explanation. I can supply a sample dataset if requested, but I feel almost certain that this question has been asked and answered recently. However, I can find no trace of it in the mail

Well, I could argue that it's not *completely* OT since my question is motivated by an enquiry that I received in respect of a CRAN package "Iso" that I wrote and maintain. The question is this: Given observations y_1, ..., y_n, what is the solution to the problem: minimise \sum_{i=1}^n (y_i - y_i^*)^2 with respect to y_1^*, ..., y_n^* subject to the "isotonic"

I use smooth.spline(x, y) in package modreg and I would like to get value of penalized log likelihood and preferable also its two parts. To make clear what I am asking for (and make sure that I am asking for the right thing) I clarify my problem trying to use the same notation as in help(smooth.spline): I want to find the natural cubic spline f(x) such that L(f) = \sum_{k=1}{n} w[k](y[k] -

Is it possible to use some form of robust regression with the breakpoints routine so that it is less sensitive to outliers? --Rich Richard Kittler Advanced Micro Devices, Inc. Sunnyvale, CA

Hello, I am trying to plot confidence bands on the mean and prediction bands for the following nonlinear regression, using maximum likelihood via optim. A toy example with data and code of what I am trying to accomplish is: VOL<-c(0.01591475, 1.19147935 ,6.34102460, 53.68809287, 91.90143074, 116.21397007, 146.41843056, 215.64535337, 256.53149673, 315.73609232) Age <-c(1.622222, 2.833333

Hi, I would like estimate a model for function of production's Coob-Douglas using maximum likelihood. The model is log(Y)= beta[1]+beta[2]*log(L)+beta[3]*log(K). I tried estimate this model using the tools nlm ( ) and optim ( ) using the log-likelihood function below: > mloglik <- function (beta, Y, L, K) { + n <- length(Y) + sum ( (log(Y)-

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

Hi Everybody I want to print a latex table containing math characters in the column heading These are the formulae I want to use as column headings. It prints OK from TeX $\sum_{i}\sum_{j}C_{P,i,j,y}\times\mathit{FC}_{i}$, $XU_{alt,y}$, $n$, $\bar{C}_{P,y}$ My plan was to create a character vector with these and later rbind the values to them. When I create the vector like:

Those two should always be delivered first imo. Signed-off-by: Jan Beulich <jbeulich@suse.com> --- a/xen/arch/x86/hvm/irq.c +++ b/xen/arch/x86/hvm/irq.c @@ -395,16 +395,16 @@ struct hvm_intack hvm_vcpu_has_pending_i struct hvm_domain *plat = &v->domain->arch.hvm_domain; int vector; - if ( (plat->irq.callback_via_type == HVMIRQ_callback_vector) -