similar to: Using capture.output within a function

Dear R-users, I'm running an MLE procedure where some ODEs are solved for each iteration in the maximization process. I use mle2 for the Maximum likelihood and deSolve for the ODEs. The problem is that somewhere along the way the ODE solver crashes and I get the following error message: DLSODA- Warning..Internal T (=R1) and H (=R2) are such that in the machine, T + H = T on the next

Hi there, I'm trying to solve 2 nonlinear equations in 2 unknowns using the BB package. The first part of my program solves 3 ODEs using the deSolve package. This part works. The output is used as parameter values in the functions I need to solve. The second part is to solve 2 equations in 2 unknowns. This does not work. I get the error message "unexpected end of input". So what

Hi, I'm using the package quadprog to solve the following quadratic programming problem. I want to minimize the function (b_1-b_2)^2+(b_3-b_4)^2 by the following constraints b_i, i=1,...,4: b_1+b_3=1 b_2+b_4=1 0.1<=b_1<=0.2 0.2<=b_2<=0.4 0.8<=b_3<=0.9 0.6<=b_4<=0.8 In my opinion the solution should be b_1=b_2=0.2 und b_3=b_4=0.8. Unfortunately R doesn't find

I'd like to compare tests based on the mixed model representation of additive models, testing among others y=f(x1)+f(x2) vs y=f(x1)+f(x2)+f(x1,x2) (testing for additivity) In mixed model representation, where X represents the unpenalized part of the spline functions and Z the "wiggly" parts, this would be: y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 vs y=X%*%beta+ Z_1%*%b_1+ Z_2%*%b_2 + Z_12

Hi there! Today I tried to estimate models using both plm and pgmm functions, with an interaction between X1 and lag(X2, 1). And I notice two issues. Let "Y=b_1 * X_1 + b_2 * X_2 + b_3 * X_1 * x_2 + e" be our model. 1) When using plm, I got different results when I coded the interaction term with I(X1 * lag(X2, 1)) and when I just saved this multiplication X1 * lag(X2, 1) in a

Hi, See example. for (i in 1:2) { for (j in 1:3) { b_1[i,j]<-rank(c(a1[i,j],a2[i,j],a3[i,j]))[1] b_2[i,j]<-rank(c(a1[i,j],a2[i,j],a3[i,j]))[2] b_3[i,j]<-rank(c(a1[i,j],a2[i,j],a3[i,j]))[3] } } The inner codes is really repeated, so i want to change the inner codes into loops. Take nn is from 1 to 3, something like, for (nn in 1:3) {

Hi Everyone, I am trying to figure out the spatstat package for the first time and am having some trouble. Unfortunately, I can't post my data set but I'll hopefully post enough details for some help. I want to model the intensity of a spatial point process using 2 covariates from my data. After reading through the documentation, I have successfully created 2 "ppp" objects. The

Hello, I have a data set which I am using to find a model with the most significant parameters included and most importantly, the p-values. The full model is of the form: sad[,1]~b_1 sad[,2]+b_2 sad[,3]+b_3 sad[,4]+b_4 sad[,5]+b_5 sad[,6]+b_6 sad[,7]+b_7 sad[,8]+b_8 sad[,9]+b_9 sad[,10], where the 9 variables on the right hand side are all indicator variables. The thing I don't understand

Do Users of Nonlinear Mixed Effects Models Know Whether Their Software Really Works? Lesaffre et. al. (Appl. Statist. (2001) 50, Part3, pp 325-335) analyzed some simple clinical trials data using a logistic random effects model. Several packages and methods MIXOR, SAS NLMIXED were employed. They reported obtaining very different parameter estimates and P

Hi, this might be basic but can't get it to work and it is hampering my R usage: #the loop is checking variance of rows, and cutting out rows with var>numVec[i] #I define outMat as object names I want to output to (does this make sense? how else #can I define sequential numbered output?) #numVec is numbers I use in the loop head(Counts) AN1 AN2 AN3 AN4 var GENE1

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. I'm so sorry. In the last email, I forgot to say that W is also a unknown parameter in the mixed beta regression model. In any case, here I send you the correct formulation. ** Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~

Dear All, I've been searching relevant topics about replacing values, none seemed to be applicable to me... I have a file with many many varieties, and want to replace some of them into different names. I tried various of ways, still don't know how to do that most efficiently.. Here is part of the example data: Gen Rep A_1 1 A_1 2 A_2 1 A_2 2 B_1 1 B_1

*Dear R-help: My name is Rodrigo and I have a question with nlme package in R to fit a mixed beta regression model. The details of the model are: Suppose that:* *j in {1, ..., J}* *(level 1)* *i in {1, ..., n_j}* *(level 2)* *y_{ij} ~ Beta(mu_{ij} * phi_{ij}; (1 - mu_{ij}) * phi_{ij}) y_{ij} = mu_{ij} + w_{ij} * *with* *logit(mu_{ij}) = Beta_{0i} + Beta_{1i} * x1_{ij} + b2 * x2_{ij}

On Oct 23, 2013, at 3:10 PM, Renato Golin <renato.golin at linaro.org> wrote: > On 23 October 2013 16:05, Arnold Schwaighofer <aschwaighofer at apple.com> wrote: > In the examples you gave there are no reduction variables in the loop vectorizer’s sense. But, they all have memory accesses that are strided. > > This is what I don't get. As far as I understood, a

On 23 October 2013 16:05, Arnold Schwaighofer <aschwaighofer at apple.com>wrote: > In the examples you gave there are no reduction variables in the loop > vectorizer’s sense. But, they all have memory accesses that are strided. > This is what I don't get. As far as I understood, a reduction variable is the one that aggregates the computation done by the loop, and is used

I'm getting lots of grief from reviewers about figures generated with the envfit function in the Vegan package. Has anyone else struggled to effectively explain this analysis? If so, can you share any helpful tips? The most recent comment I've gotten back: "What this shows is which NMDS axis separates the communities, not the relationship between the edaphic factor and the

Dear readers, Is it possible to specify a model y=X %*% beta + Z %*% b ; b=(b_1,..,b_k) and b_i~N(0,v^2) for i=1,..,k that is, a model where the random slopes for different covariates are i.i.d., in lmer() and how? In lme() one needs a constant grouping factor (e.g.: all=rep(1,n)) and would then specify: lme(fixed= y~X, random= list(all=pdIdent(~Z-1)) ) , that?s how it's done in the

Hi R-helpers, I'm new to time series modelling, but my requirement seems to fall just outside the capabilities of the arima function in R. I'd like to fit an ARMA model where the variance of the disturbances is a function of some exogenous variable. So something like: Y_t = a_0 + a_1 * Y_(t-1) +...+ a_p * Y_(t-p) + b_1 * e_(t-1) +...+ b_q * e_(t-q) + e_t, where e_t ~ N(0, sigma^2_t),

Dear R Help, I have two nested negative log-likelihood functions that I am optimizing with the spg function [BB package]. I would like to perform model selection on these two objective functions using AIC (and possibly anova() too). However, the spg() function returns a list and I need a fitted model object for AIC(), ICtab() [bbmle package], or anova(). How can I perform AIC-based model

Hi all - I am trying to install ROracle for linux machines ... I have read the INSTALL documentation and followed the directions for setting the paths as follows: export PATH=/app/oracle/product/10.2.0/db_1/bin:$PATH [manthony@diamondback ~]$ export LD_LIBRARY_PATH=/usr/lib:/usr/lib64:/lib:/app/oracle/product/10.1.0/db_1