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...quot;teacher effect" applies to only one year. One can think of the first score on the student as a score from a prior year (for which I have no teacher links), and the second score is from the current year and is linked to the teacher. The model for student j in class i is: Y_{ij0} = a_0 + e_{ij0} Y_{ij1} = a_1 + b_i + e_{ij1} with Var(b_i) the teacher variance component and Cov(e_{ij0},e_{ij1}) unstructured. That is, if the data are organized by student, the "Z" matrix in the usual linear mixed model notation has every other row equal to a row of zeros. I am wondering whet...

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 and vec.I is the vector for the i indice. N...

...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), and with the variance specified by something like sigma^2_t = exp(beta_t * X_t), where X_t is my exogenous variable. I would be very grateful if somebody could point me in the direction of a library that could fit this (or a similar...

...put out into a SAS data set. How do I do the same with lme-objects? For example, I can see the estimated variance matrix when typing the name of the lme-object, but that does not give me a "handle" on the matrix. 2) I am trying to fit a mixed model, say y_{ij} = X_{ij}\beta + U_i + e_{ij} where Y_{ij}, U_i and e_{ij} all are D-dimensional, and U_i ~ N_D(0,G), and e_{ij} ~ N(0,R). This is easy to do in proc mixed by including a factor keeping track of how the elements of y_{ij} are "stacked" on top of each other. This factor is then used in the model, the random and th...

...eHuron,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 = Z_{t-1} + A + e_t + B*e_{t-1} \] How could I find $e_t$ dan $e_{t-1}$ ? Best, XY

...r_ p[1] th_ p[2] ll _ -sum(x[1:max(kk)]*log(dnbinom(kk[-length(kk)],r,th))) - x[length(kk)]*log(1-pnbinom((max(kk)-1),r,th)) return(ll) } a_ nlm(fn,p=c(.9,.5),hes=T) est_ a$est phat_ c(dnbinom(kk[-length(kk)],est[1],est[2]), 1-pnbinom((max(kk)-1),est[1],est[2])) E_ n*phat chi2_ sum((x-E)^2/E) cat('Chi2 goodness-of-fit = ',chi2,'\n') cat('P-value=',1-pchisq(chi2,df=length(kk)-1-2),'\n') print(cbind(O=x,E=round(E,1),err=round((x-E)^2/E,1))) =========================================== At 02:31 PM 2/8/01 +0000, Mark Wyatt wrote:...

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, but I know that the power coefficient is 2, but with different sigma coefficients for the two responses. If I fit a model I get an estimation of the standard d...

...hemselves, or is there any chance of hooking one up? Siemens (a v large business) aren't mentioned on the Compatibility Page: http://www.networkupstools.org/compat/stable.html But, their Masterguard brand is: http://www.medical.siemens.com/webapp/wcs/stores/servlet/CategoryDisplay~q_catalogId~e_-11~a_categoryId~e_1014490~a_catTree~e_100010,1012315,1014543,1014490~a_langId~e_-11~a_storeId~e_10001.htm Any ideas much appreciated. Regards, Morgan. -- Getting errors: "There are problems with the signature" (or similar)? Update your system by installing certificates from CAcert Inc,...

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 mod<-lm(...

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,

...e econometrics sense of the term -- an individual-specific intercept. I also want to model the continuous variables flexibly -- I have no good priors on the proper specification for the function form. The model is the following: y_{it} = \alpha_i + \beta_1(T_{it}) + f(continuous.vars_{it}) + e_{it} To control for unobserved time-invariant heterogeneity, I want to de-mean the data as follows: y_{it}-\bar{y_i} = \beta_1(T_{it}-\bar{T_i}) + f(continuous.vars_{it}-\bar{continuous.vars_i}) + e_{it} - \bar{e}_i Fitting the demeaned model should give me coefficient estimates equal to the n...

...ed to a (USB) (flash) drive, which then can be used to boot BIOS systems; and, C_ can be dd'ed to a (USB) (flash) drive, which then can be used to boot UEFI systems; and, D_ can be dd'ed to a (USB) (flash) drive, which then can be used to boot either/both BIOS systems or UEFI systems. E_ can be expanded (I mean, its content) onto a (USB) (flash) drive with GPT and a FAT ESP (and legacy "MBR"), which then can be used to boot either/both BIOS systems or UEFI systems. The possibility to perform #B/#C/#D/#E (in addition to #A) depends on the content of the isohybrid image...

...ready solved in git, but I have not re-tested it.) Getting out from HDT (version from Syslinux 5.00) using the "exit" option (instead of rebooting) so to return back to syslinux (menu.c32) fails (hangs). How to reproduce: boot to menu.c32, select hdt.c32 entry, exit back to menu.c32. E_ Under FreeDOS, SYSLINUX.COM doesn't seem to install ldlinux.* (specially when used with "--directory"), and there are no ([error]) messages; not even when executed with no parameters, or with "--help", or with "--version". The messages disappeared _after_ 5.00-...

...ot; will not be capable of booting. This is one > of the steps that corresponds to grub2, not to whichever other EFI > bootloader you use (e.g. syslinux.efi). So, it's not possible (yet?) to provide an hybrid ISO image able to boot in UEFI and BIOS modes using Syslinux. It took me _some_ time to realize that, but at least that helped me understand a bit more Syslinux' and co features, thanks Ady and Thomas. But as my goal is actually to to uniformize the tools and apperance for booting an installer regardless of the firlwre's kind, instead I have cooked an USB installer th...

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".

...ng 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, Observations is {x,y}_t,there may be some relation between x and y. the model is y_t=gamma0 + *gamma1*x_t*+psi*e_{t-1}+e_t the gamma1*x_t is regression. e_t=sqrt(h_t)*N(0,1) h_t=alpha0+alpha1*e_t^2+beta*h_{t_1}~~~~~~~GARCH(1,1). I didn't know how to estimate the model using function garchFit or garchOxFit or other functions? because the argument in garchFit/garchOxFit is formular.mean...

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 to estim...

...ade 4","Grade 5") long<-reshape(data, idvar="stuid", varying=list(names(data)[2:5]), v.names="score", direction="long") long$time<-long$time-1 With these data I then use the gls function to estimate the following model Y_{ti} = mu + beta(time) + e_{ti} fm1 <- gls(score ~ time, long, correlation=corAR1(form=~1|stuid), method='ML') >From here I can obtain V, the variance covariance matrix using the getVarCov function as follows: var.mat<-getVarCov(fm1) I<-diag(1,sample.size) # The following 2 steps are needed to make V c...

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