similar to: MLE where loglikelihood function is a function of numerical solutions

Hi, I'm solve a problem where I want to select every 3rd row from a matrix. I do this for all rows in a loop. This gives me "i" matrices with different number of rows I want to stack these matrices in an array and fill the blanks with zero. Does anyone have any suggestions on how to go about this? So i want to go from [,1] [,2] [,3] [1,] 1 1 1 [2,] 2 2

I have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni

Dear R users, When running the program below I receive the following error message: fit <- optim(parm, objective, yt = tyield, hessian = TRUE) Error in as.vector(data) : no method for coercing this S4 class to a vector I can't figure out what the problem is exactly. I imagine that it has something to do with "tyield" being a matrix. Any help on explaining what's going on

Hi, I'm trying to estimate the parameters of a state space model of the following form measurement eq: z_t = a + b*y_t + eps_t transition eq y_t+h = (I -exp(-hL))theta + exp(-hL)y_t+ eta_{t+h}. The problem is that the distribution of the innovations of the transition equation depend on the previous value of the state variable. To be exact: y_t|y_{t-1} ~N(mu, Q_t) where Q is a diagonal

Dear All, have a general question about coefficients estimation of the mixed model. I simulated a very basic model: Y|b=X*\beta+Z*b +\sigma^2* diag(ni); b follows N(0,\psi) #i.e. bivariate normal where b is the latent variable, Z and X are ni*2 design matrices, sigma is the error variance, Y are longitudinal data, i.e. there are ni

Dear R-users I'm running a maximum likelihood procedure using the spg package. I'd like to save some output produced in each iteration to a file, but if I put the capture.output() within the function I get the following message; Error in spg(par = startval, fn = loglik, gr = NULL, method = 3, lower = lo, : Failure in initial function evaluation!Error in -fn(par, ...) : invalid argument

Dear R-users, I'm writing a program that constructs a dataset. I wish to save the dataset to a file. Here's a very simple example of what I'm trying to do function(x=peter){ y <- x/2 write.csv(y, file = "...\x") } The problem is that I want to name the dataset as whatever the name of the input is. In this case peter. How do I do this? Thank you in advance. Kristian

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))});

Hello I'm preparing a new fileserver, based on jessie + sernet 4.2.10 packages. the server is bound to a forest, "AD" where users account are stored, and subdomains "PSI" for computers and some local accounts The Active directory forest is managed by 2008R2 servers, with rfc2307 attributs filled for accounts. I'm using "winbind use default domain" because

Hi all, I'm trying to fit a nonlinear regression by "nlrob": model3=nlrob(y~a1*x^a2,data=transient,psi=psi.bisquare, start=list(a1=0.02,a2=0.7),maxit=1000) However an error message keeps popping up saying that the function psi.bisquare doesn't exist. I also tried psi.huber, which is supposed to be the default for nlrob: model3=nlrob(y~a1*x^a2,data=transient,psi=psi.huber,

Dear R list, I have a function specifying my log-likelihood, and now I need to set the constraint that *alpha > kappa*, could anyone help me with setting this in my function? the function is defined as follows: mll <- function(param){ n <- length(x) psi <- numeric(n) psi[1] <- 1.0 a0 <- exp(param[1]); a1 <-exp(param[2]); b1 <- exp(param[3]); *alpha *<-

Dear R experts, Attached you can find the expression of a loglikelihood function which I would like to maximize in R. So far, I have done maximization with the combined use of the mathematical programming language AMPL (www.ampl.com) and the solver SNOPT (http://www.sbsi-sol-optimize.com/manuals/SNOPT%20Manual.pdf). With these tools, maximization is carried out in a few seconds. I wonder if that

Hi all, To implement more profile-guided optimizations, we’d like to use ProfileSummaryInfo (PSI) and BlockFrequencyInfo (BFI) from more passes of various types, under the new pass manager. The following is what we came up with. Would appreciate feedback. Thanks. Issue It’s not obvious (to me) how to best do this, given that we cannot request an outer-scope analysis result from an inner-scope

Overall seems fine to me. On 3/11/19 8:12 PM, Hiroshi Yamauchi wrote: > Here's a revised approach based on the discussion: > > - Cache PSI right after the profile summary in the IR is written in > the pass pipeline. This would avoid the need to insert > RequireAnalysisPass for PSI before each non-module pass that needs it. > PSI can be technically invalidated but unlikely

I got some R code that I don't understand. Question as comment in code //where is t comming from, what is phi inverse rAC <- function(name, n, d, theta){ #generic function for Archimedean copula simulation illegalpar <- switch(name, clayton = (theta < 0), gumbel = (theta < 1), frank = (theta < 0), BB9 = ((theta[1] < 1) | (theta[2] < 0)), GIG = ((theta[2] < 0) |

On 3/14/19 2:04 AM, Hiroshi Yamauchi wrote: > > > On Wed, Mar 13, 2019 at 2:37 PM Fedor Sergeev <fedor.sergeev at azul.com > <mailto:fedor.sergeev at azul.com>> wrote: > >> >> - Add a new proxy ModuleAnalysisManagerLoopProxy for a loop pass >> to be able to get to the ModuleAnalysisManager in one step and >> PSI through it. >

Dear All, could someone please shed some light on the use of the .C or .Fortran function: I am trying load and running on R the following function // psi.cpp -- psi function for real arguments. // Algorithms and coefficient values from "Computation of Special // Functions", Zhang and Jin, John Wiley and Sons, 1996. // // (C) 2003, C. Bond. All rights reserved. // //

Hi all, following is my R -code and shows the error given below > n <- 100 > k<-2 > x1 <-c(1, 3);x2 <- c(2,5) > X <- matrix(c(0,0), nrow = 2, ncol = n) > for(i in 1:k) + X[i, ] <- mh1.epidemic(n,x1[i],x2[i]) Error in X[i, ] <- mh1.epidemic(n,x1[i],x2[i]):   number of items to replace is not a multiple of replacement length   > psi <-t(apply(X, c(1,2),

Hi R users, Is is possible for me to use the try function with boot? I would to do the bootstraping with a nonlinear model(it works well when R < 1000). But it does not work very well (when R is large) thus I try to use "try" to resolve. I put the try function in two cases: case1: put the try in front of the boot > c1.try<-try(boot(c1data, statistic = c1.fun,

On 3/4/19 10:49 PM, Hiroshi Yamauchi wrote: > > > On Mon, Mar 4, 2019 at 10:55 AM Hiroshi Yamauchi <yamauchi at google.com > <mailto:yamauchi at google.com>> wrote: > > > > On Sat, Mar 2, 2019 at 12:58 AM Fedor Sergeev > <fedor.sergeev at azul.com <mailto:fedor.sergeev at azul.com>> wrote: > > > > On 3/2/19 2:38 AM,