similar to: ifthen() question

OK, I got it figured out. I was not keying into a length greater than 1, so: # I added this object and placed it into the iftelse statement: lid <- sum(match(id, st[i], nomatch = 0)) out$var.g[i]<-ifelse(lid ==1, meta$var.g[id==st[i]], aggs(g=g[id==st[i]], n.1= n.1[id==st[i]], n.2 = n.2[id==st[i]], cor)[2]) #full

Hi (R 2.13.1, OSX 10.6.8) I am trying to use predict.rma with continuous and categorical variables. The argument newmods in predict.rma seems to handle coviariates, but appears to falter on factors. While I realise that the coefficients for factors provide the answers, the goal is to eventually use predict.rma with ANCOVA type model with an interaction. Here is a self contained example

hi guys and gals ... How are you all ... i have to do something in robust regression by R programm , and i have some problems as following: *the first :* suppose w(r) =1/(1 r^2) and r <- c(7.01,2.07,7.061,5.607,8.502,54.909,12.222) and i want to exclude some values from r so that (abs(r)>4.9 )... after ,i want to used (w) to get on coefficients beta0 and beta1 (B1 <-

Hi, I came across the below error when I try to do ifelse condition:- Error in "[<-"(`*tmp*`, test, value = rep(yes, length = length(ans))[test]) : incompatible types What I am trying to accomplish is :- for(x in 1:ncol(filterpred)){ sumno<-sum(filterpred[no,x]) sumyes<-sum(filterpred[yes,x]) ifelse(sumno==0 && sumyes !=0,

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, 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 *<-

Hi there, I'm trying to solve a ML problem where the likelihood function is a function of two numerical procedures and I'm having some problems figuring out how to do this. The log-likelihood function is of the form L(c,psi) = 1/T sum [log (f(c, psi)) - log(g(c,psi))], where c is a 2xT matrix of data and psi is the parameter vector. f(c, psi) is the transition density which can be

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

Hi all, I am also confused about the manual: a. The input arguments: wt.method are the weights case weights (giving the relative importance of case, so a weight of 2 means there are two of these) or the inverse of the variances, so a weight of two means this error is half as variable? w (optional) initial down-weighting for each case. init (optional) initial values for the

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),

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,

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 Sat, Mar 2, 2019 at 12:58 AM Fedor Sergeev <fedor.sergeev at azul.com> wrote: > > > On 3/2/19 2:38 AM, Hiroshi Yamauchi wrote: > > Here's a sketch of the proposed approach for just one pass (but imagine > more) > > https://reviews.llvm.org/D58845 > > On Fri, Mar 1, 2019 at 12:54 PM Fedor Sergeev via llvm-dev < > llvm-dev at lists.llvm.org>