similar to: solution design for a large scale (> 50G) R computing problem

Hi, I would appreciate if someone could help me on track with this problem. I want to compute some parameters from a system of equations given a number of sample observations. The system looks like this: sum_i( A+b_i>0 & A+b_i>C+d_i) = x sum_i( C+d_i>0 & C+d_i>A+b_i) = y sum_i( exp(E+f_i) * ( A+b_i>0 & A+b_i>C+d_i) = z A, C, E are free variables while the other

Dear all, Given a LME model (following the notation of Pinheiro and Bates 2000) y_i = X_i*beta + Z_i*b_i + e_i, is it possible to extract the variance-covariance matrix for the estimated beta_i hat and b_i hat from the lme fitted object? The reason for needing this is because I want to have interval prediction on the predicted values (at level = 0:1). The "predict.lme" seems to

I am 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

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:

Dear all, I am trying to repeat an example from Sokal and Rohlfs "Biometry" -- Box 11.4, example of a randomized-complete-blocks experiment. The data is fairly simple: series genotype weight 1 pp 0.958 1 pb 0.985 1 bb 0.925 2 pp 0.971 2 pb 1.051 2 bb 0.952 3 pp 0.927 3 pb 0.891 3 bb 0.892 4

Hi Lee, You need to specify the VM context that you are using.. so using your examples.. extensions.conf entry.. exten => 1000,1,Dial(SIP/1000,20) exten => 1000,2,Voicemail2(u1000) exten => 1000,102,Voicemail2(b1000) exten => 1000,103,Hangup should be.. exten => 1000,1,Dial(SIP/1000,20) exten => 1000,2,Voicemail2(u1000@sip) exten => 1000,102,Voicemail2(b1000@sip) exten

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

Dear R, I have a simple question concerning with a special case of spare matrix multiplications. Say A is a 200-by-10000 dense matrix. B is a 10000-by-10000 block- diagonal matrix, and each diagonal block B_i is 100-by-100. The usual way I did A%*%B will take about 30 seconds which is to time consuming because I have to do this thousands of times. I also tried to partition A into 100 small blocks

Dear R users, I have a couple of linear model related questions. 1) How do I produce a fixed effect linear model using lme? I saw somewhere (this may be Splus documentation since I use Splus and R interchangeably) that using lme(...,random= ~ -1 | groups,...) works, but it gives the same as lme(...,random= ~ 1 | groups,...), ie. fits a random effect intercept term. The reason why I want to do

I've got an interesting scenario where transfers while getting an invite seem to break. Here is the scenario: You have a receptionist who has a 6 line phone (in this case, a polycom ip600 - also tested with a Cisco 7960) the receptionist has all six lines available for use (in the case of the cisco I tried registering all lines as one number as well as registering multiple lines and

Hi, Looking for a function in R that can help me calculate a parameter that maximizes the likelihood over groups of observations. The general formula is: p = exp(xb) / sum(exp(xb)) So, according to the formulas I've seen published, to do this "by group" is product(p = exp(x_i * b_i) / sum(exp(x_i b_i))) Where i represents a "group" and we iterate through each group.

Dear all, I have a question about mixed effect model in R. The data set has 5 variables, X(response),subject, times, repeat, indicator The model is X_hijk=a_h+Z_h*b_i+r(ij)+e_hijk , where h=0,1(indicator), i=1,...,n(subject), j=1,...,n_i(times within subject; nested effect),k=1,2,3(repeat). Z_h=1 if h=1 =0 if h=0 b_i~N(0,c^2) random effect of subject r(ij)~N(0,d^2) random effect of times

Hello, I have a simulated data structure in which students are nested within teachers, and with each student are associated two test scores. There are 20 classrooms and 25 students per classroom, for a total of 500 students and two scores per student. Here are the first 10 lines of my dataframe "d": studid tchid Y time 1 1 1 -1.0833222 0 2 1 1

Dear list, I'm trying to fit a multilevel (mixed-effects) model using the lme function (package nlme) in R 2.4.0. As a mixed-effects newbie I'm neither sure about the modeling nor the correct R syntax. My data is structured as follows: For each subject, a quantity Y is measured at a number (>= 2) of time points. Moreover, at time point 0 ("baseline"), a quantity X is

I''m a physicist working on fusion energy and dabble in statistics only occasionally, so please excuse gaps in my statistical knowledge. I''d appreciate any help that a real statistics expert could provide. Most people in my field do only very simple statistics, and I am trying to extend some work on multivariate linear regression to account for significant between-group

Hi all, I'm using a certain  procedure to calculate the value of some variable(Bayes risk),B. So I got the values B1, B2, ........, B1000, each under certain input values and using a long procedure. Now, I want to put the values I got in a nummerical vector and find their minimum value. I think c( ) should work.For example if I have only 10 values I could have used

I have been struggling with my Asterisk setup for 3 days now and I think I have done well...apart from the small detail that I cannot dial out on my phone (PSTN) line. My setup is: Suse Linux 9.0 1 fxo card connected to a BT(UK) line 1 Cisco ATA186 sip v3.0 with two analogue phones attached to it Asterix CVS-HEAD-05/30/04-06:56:31 with the UK Userid patch applied. Asterisk loads without any

Hi all I'm pretty sure someone must have done this before but I couldnt find any trace of it on the web so I thought I would drop a note about how I ended up doing it. I have also posted this info on voip-info. Warning : This is not very elegant and I'm currently trying to write a patch in order to make it better but so far, this the only way I've gotten this to work. Scenario : I

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

Dear r-helpers, I am working with plm package. I am trying to fit a fixed effects (or a 'within') model of the form y_it = a_i + b_i*t + e_it, i.e. a model with an individual-specific intercept and an individual- specific slope. Does plm support this directly? Thanks in advance! Otto Kassi