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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

Hi all, I have some difficulties with the lme function and so this is my problem. Supoose i have the following model y_(ijk)=beta_j + e_i + epsilon_(ijk) where beta_j are fixed effects, e_i is a random effect and epsilon_(ijk) is the error. If i want to estimate a such model, i execute >lme(y~vec.J , random~1 |vec .I ) where y is the vector of my data, vec.J is a factor object

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-users Coming from a proc mixed (SAS) background I am trying to get into the use of (n)lme. In this connection, I have some (presumably stupid) questions which I am sure someone out there can answer: 1) With proc mixed it is easy to get a hold on the estimated variance parameters as they can be put out into a SAS data set. How do I do the same with lme-objects? For example, I can see the

Dear All, I have a model to predict time series data for example: data(LakeHuron) Lake.fit <- arima(LakeHuron,order=c(1,0,1)) then the function predict() can be used for predicting future data with the model: LakeH.pred <- predict(Lake.fit,n.ahead=5) I can see the result LakeH.pred$pred and LakeH.pred$se but I did not see residual in predict function. If I have a model: [\ Z_t =

This came up on r-help but indicates a bug. dnbinom(x,n,p) calls dbinom_raw(n-1,...) which returns 0 for n<1. -thomas ---------- Forwarded message ---------- Date: Thu, 08 Feb 2001 17:10:23 +0000 From: Yudi Pawitan <yudi@stat.ucc.ie> To: Mark Myatt <mark@myatt.demon.co.uk> Cc: R-Help <r-help@stat.math.ethz.ch> Subject: Re: [R] Goodness of fit to Poisson / NegBinomial

Folks: The following has NOTHING (obvious) to do with R. But I believe that all on this list would find it relevant and, I hope, informative. It is LONG. I apologize in advance to those who feel I have wasted their time. http://www.newyorker.com/reporting/2010/12/13/101213fa_fact_lehrer Best regards to all, Bert -- Bert Gunter Genentech Nonclinical Biostatistics

Hi! I am using the NLME package for R to modeling glucose-insuline response with Bergman's model, very similar to the example in the documentation for the NLME package. My question concerns the model for the residuals. I use a proportional model , Var(e_{ij})=(sigma_g*G(t))^2 for the glucose response and Var(e_{ij})=(sigma_i * I(t))^2 for the insulin response. Hence I have a varPower model,

Folks Has anyone any experience with these? http://www.automation.siemens.co.uk/main/business%20groups/power/ups/products/ups%20systems/single%20phase/ http://www.automation.siemens.co.uk/main/business%20groups/power/ups/products/ups%20systems/single%20phase/Siemens%20leaflet%20UPS%20Series%20A.pdf They look a very solid machine, I've got a chance to pick one up for NZ$50 with an extra

Dear all I need to run an anova from a factorial model y_{ijk}=\alpha_i+\beta_j+(\alpha\beta)_{ij}+e_{ijk} and calculate type II and III sums of square, but I have an empty cells, so anova function from package car fail. (I believe) y<-c(7,13,6,10,8,11,8,3,7,5,65) a<-as.factor(c(1,1,2,2,3,3,3,1,1,1,2)) b<-as.factor( c(rep(1,7),rep(2,4)) ) table(b,a) # cell (2,3) is empty

Dear All, I am trying to solve a Generalized Method of Moments problem which necessitate the gradient of moments computation to get the standard errors of estimates. I know optim does not output the gradient, but I can use numericDeriv to get that. My question is: is this the best function to do this? Thank you Jean,

Dear List, I'm just teaching myself semi-parametric techniques. Apologies in advance for the long post. I've got observational data and a longitudinal, semi-parametric model that I want to fit in GAM (or potentially something equivalent), and I'm not sure how to do it. I'm posting this to ask whether it is possible to do what I want to do using "canned" commands

> > Hi, > > On 11/11/2014 02:55, Ady wrote: > > Again, dd'ing your isohybrid image onto your (virtual or real) drive > > will fail to boot in EFI mode. Your "syslinux.efi-based" > > "/isolinux/efiboot.img" will not be capable of booting. This is one > > of the steps that corresponds to grub2, not to whichever other EFI > >

> 5.01 should be released by the beginning of next week. The following problems (among others) are present in 5.00. I hope they can be solved for 5.01. A_ LABEL directive with trail space characters fails: http://www.syslinux.org/archives/2012-December/019153.html B_ TEXT HELP directive hangs (easier to see when using Windows-like new lines, CR/LF):

...Bann%5D-syslinux-6-03-has-been-released-4175521209/ Here is the tree: . |-- EFI | `-- BOOT | |-- bootx64.efi | |-- bzImage | |-- f2.txt | |-- initrd.img | |-- ldlinux.c32 | |-- ldlinux.e64 | |-- ldlinux.sys | |-- memtest | |-- message.txt | |-- setpkg | |-- syslinux.cfg | `-- ter-i16v.psf |-- README_SLINT.TXT `-- SLINT_DATE Best regards, Didier

Hello. In order to practice compiler writing, I am implementing a simple compiler for the Tiger language. I am using llvm IR for the intermediate representation. Regarding llvm, I have started by reading the OCaml version of the LLVM Tutorial, where a compiler for the kaleidoscope language is built. The language I am implementing has a form of expression called "sequence expression".

---------- Forwarded message ---------- From: ma yuchao <ma.yuchao@gmail.com> Date: 2006-5-20 ÉÏÎç4:01 Subject: hello, everyone To: R-help@stat.math.ethz.ch Hello, R people: I have a question in using fSeries package--the funciton garchFit and garchOxFit if adding a regression to the mean formula, how to estimate the model in R? using garchFit or garchOxFit? For example,

Hello, I am interested in estimating this type of random effects panel: y_it = x'_it * beta + u_it + e_it u_it = rho * u_it-1 + d_it rho belongs to (-1, 1) where: u and e are independently normally zero-mean distributed. d is also independently normally zero-mean distributed. So, I want random effects for group i to be correlated in t, following an AR(1) process. Any idea of how

Dear List: I am estimating a gls model and am having to make some rather unconventional modifications to handle a particular problem I have identified. My aim is to fit a GLS with an AR1 structure, obtain the variance-covariance matrix (V), modify it as needed given my research problem, and then reestimate the GLS by brute force using matrix operations. All seems to be working almost perfectly,

How does one define PDFs as yet undefined in R, such as the ex- gaussian, the sum of two RVs, one exponential, one Gaussian? The PDF would then be the convolution of an exponential PDF, dexp(), and a normal, dnorm(). Kindly cc me in your reply to r-help. Thanks, _____________________________ Professor Michael Kubovy University of Virginia Department of Psychology USPS: P.O.Box 400400