similar to: Variance of a complex estimator using survey package ...

Hi, I am trying to fit a model with an ordered response variable (3 levels) and 13 predictor variables. The sample has complex survey design and I've used 'svydesign' command from the survey package to specify the sampling design. After reading the manual of 'svyglm' command, I've found that you can fit a logistic regression (binary response variable) by specifying the

Is there a way to create a BRR svrepdesign from a survey design when the number of PSUs is odd in one or more stratum? Creating a JKn svrepdesign with that condition works okay, but when I tried to create a svrepdesign with type="BRR" I get an error and this message: "Can't split with odd numbers of PSUs in a stratum" I get that message when I tell it to merge the

Hello, How I use the function as.svrepdesign without memory.size problems? desenho_npc_JK <- as.svrepdesign(desenho_npc,type="JKn") Error: cannot allocate vector of size 161.3 Mb In addition: Warning messages: 1: Reached total allocation of 1022Mb: see help(memory.size) 2: Reached total allocation of 1022Mb: see help(memory.size) 3: Reached total allocation of 1022Mb:

Hi all, I am using quantile regression with svy design. I want to retrieve summary regression statistics (std error, p-value), since I don't have any in my output: Commands: clus1_d<- svydesign(id=~cd002_co, weights=~wtper, strata=~str, data=data) bclus1<-as.svrepdesign(clus1_d,type="bootstrap",replicates=100) fit1<-

Hi, This is most likely only a minor technicality, but I saw the following: On page 6 of the 'parallel' vignette (http://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf), the random-number generator "L'Ecuyer-CMRG" is said to have seed "(x_n, x_{n-1}, x_{n-2}, y_n, y_{n-1}, y_{n-2})". However, in L'Ecuyer et al. (2002), the seed is given with

Hi all, I'm new with R (and S), and relatively new to statistics (I'm a computer scientist), so I ask sorry in advance if my question is silly. My problem is this: I have a (sample of a) discrete time stochastic process {X_t} and I want to estimate Pr{ X_t | X_{t-l_1}, X_{t-l_2}, ..., X_{t-l_k} } where l_1, l_2, ..., l_k are some fixed time lags. It will be enough for me to compute

Dear R users, I'd like to compute X like below. X_{i,t} = 0.1*t + 2*X_{i,t-1} + W_{i,t} where W_{i,t} are from Uniform(0,2) and X_{i,0} = 1+5*W_{i,0} Of course, I can do this with "for" statement, but I don't think it's good idea because "i" and "t" are too big. So, my question is that Is there any better idea to avoid "for" statement

Dear All, Suppose I have a vector X = (x_1, x_2, ...., x_n), X_sort = sort(X) = (x_(1), x_(2), ... , x(n) ), and I would like to know the original position of these ordered x_(i) in X, how can I do it? case 1: all values are unique x <- c( 3, 5, 4, 6) x.sort <- sort(x) # # I would like to obtain a vector (1, 3, 2, 4) which indicates that 3 in x is still the 1st element in x.sort, 5 is at

I'm observing that base::sum(x, na.rm=FALSE) for typeof(x) == "double" is much more time consuming when there are missing values versus when there are not. I'm observing this on both Window and Linux, but it's quite surprising to me. Currently, my main suspect is settings in on how R was built. The second suspect is my brain. I hope that someone can clarify the below

Hi all I recently started to write a matrix exponentiation operator for R (by adding a new operator definition to names.c, and adding the following code to arrays.c). It is not finished yet, but I would like to solicit some comments, as there are a few areas of R's internals that I am still feeling my way around. Firstly: 1) Would there be interest in adding a new operator %^% that performs

Hi, Can Someone inform me how to generate RV's using the below CDF, by inverse technique. Thanks for your help and time. My CDF is as follows \[ F(x)=0 \ \text{if} \ x < 0\]\[ F(x)=\{\frac{x-x_i}{x_{i+1}-x_{i}}*(p_{i+1}-p_{i})\}+p_{i}\ \forall \ x_{i}\leq x < x_{i+1} \] \[ F(x)=1 \ \text{if} \ x > x_{i+1} \] Regards Murthy

Hello! Is it possible to use R time series to identificate a process which is subjected to known input? I.e. I have 2 sequences - one is measurements of black box's state and the second is the "force" by which this black box is driven (which is known too) and I want to fit thist two series with AR-process. The "ar" procedure from stats package expects that the force is

Hi, I just started with setting up a new asterisk system, that will operate on a sip trunk, but I wonder, how to transfer the calls to different extensions, because all calls appear as being send to the base number of the trunk. E.g. given the trunk range of 1234567800-12345678099, a call to 1234567800 is matched by the same pattern as a call to 12345678099. ; matches 12345678099, too exten

Dear mailing list, I have found some strange behaviour which I think relates to parent frames and eval. Can anyone explain what's going on here? First example: > test.parent.funcs_ function() { outer.var_ 5 subfunc1_ function() substitute( outer.var, envir=parent.frame()) print( subfunc1()) subfunc2b_ function() eval( quote( outer.var), envir=parent.frame()) print(

Dear all, I know that my question is somewhat special but I tried several times to solve the problems on my own but I am unfortunately not able to compute the following test statistic using the quantreg package. Well, here we go, I appreciate every little comment or help as I really do not know how to tell R what I want it to do^^ My situation is as follows: I have a data set containing a

Hi, there. How do I calculate the cross-product in the form of \sum_{i=1}^{n}X_{i}^{t} \Sigma X_{i} using R code without using do loop? X_{i} is the covariate matrix for subject I, \Sigma is the covariance matrix. Thanks for your help. Yulei [[alternative HTML version deleted]]

This has ABSOLUTELY nothing to do with R but I was hoping that someone might know because there are obviously a lot of very bright people on this list. Suppose I had a time series of data and at each point in time t, I was calculating x bar + plus minus sigma where x bar was based on a moving window of size n and so was sigma. So, if I was at time t , then x bar t plus minus sigma_t would be

Hola. Intento crear un archivo .html a partir de un .Rmd que tenga Toc, LaTeX y tablas. Empleo RStudio (v. 0.97.551), pero aparece un error en la función "markdownToHTML" Pasos: 1) creo un fichero Rmd (adjunto ejemplo) => con knitr (v. 1.0.5) crea un fichero ".md" 2) Si quiero añadirle el TOC (table of contents) necesito emplear la función "markdownToHTML" del

Hello, Guys: I'm from China, my English is poor and I'm new to R. The first message I sent to R help meets some problems, so I send again. Hope that I can get useful suggestions from you warm-hearted guys. Thanks. I builded a multiplicative seasonal ARMA model to a series named "cDownRange". And the order is (1,1)*(0,1)45 The regular AR=1; regular MA=1; seasonal AR=0; seasonal

Hello, I was wondering if someone knows the formula used by the function lm to compute the t-values. I am trying to implement a linear regression myself. Assuming that I have K variables, and N observations, the formula I am using is: For the k-th variable, t-value= b_k/sigma_k With b_k is the coefficient for the k-th variable, and sigma_k =(t(x) x )^(-1) _kk is its standard deviation.