search for: reinsel

...r-help in 2004: https://stat.ethz.ch/pipermail/r-help/2004-May/051444.html Two more points: * the last sentence of the Description might need one fewer comma [after "statistic"] or one more [after "p-value"]. * in ?lme, Littell's name is misspelled at least twice and Reinsel's at least once. Is there a publicly accessible SVN server for recommended packages (in general) and nlme (in particular) anywhere? cheers Ben Bolker

Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. I used data Orthodont for the two packages. The commands used are below. require(nlme) data(Orthodont) fm1<-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method="REML") anova(fm1) numDF DenDF F-value p-value (Intercept) 1

...4) -0.020 ( 4) 142.824 ( 5) 0.004 ( 5) -211.711 ( 6) -0.010 ( 6) 201.856 ( 7) 0.058 ( 7) 286.079 ( 8) -0.035 ( 8) -134.057 ( 9) 0.032 ( 9) -18.200 ( 10) 0.019 ( 10) Since there are multiple ways of defining the pacf for multivariate time series (see e.g. G.C. Reinsel, Elements of multivariate time series analysis, II edition, Springer, section 3.3) and given that in ?pacf there is no reference to articles or books regarding its computation I do not know whether this behaviour is expected or it is a bug instead. In the first case could you provide a reference fo...

Hi all I tried to reproduce an example with lme and used the Orthodont dataset. library(nlme) fm2a.1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1 | Subject) anova(fm2a.1) > numDF denDF F-value p-value > (Intercept) 1 80 4123.156 <.0001 > age 1 80 114.838 <.0001 > Sex 1 25 9.292 0.0054 or alternatively

...hz.ch/pipermail/r-help/2004-May/051444.html > Two more points: > * the last sentence of the Description might need one fewer comma > [after "statistic"] or one more [after "p-value"]. > * in ?lme, Littell's name is misspelled at least twice and Reinsel's > at least once. We'd be grateful for patches, thank you Ben! Notably for 'nlme' and 'foreign', both of which are maintained by R-core (rather than individual R core or R Foundation members) we've also encouraged that R's bugzilla be used for non-trivial...

I can't understand the results from cross-correlation function ccf() even though it should be simple. Here's my short example: ********* a<-rnorm(5);b<-rnorm(5) a;b [1] 1.4429135 0.8470067 1.2263730 -1.8159190 -0.6997260 [1] -0.4227674 0.8602645 -0.6810602 -1.4858726 -0.7008563 cc<-ccf(a,b,lag.max=4,type="correlation") cc Autocorrelations of series 'X',

Hi, I know that is not possible make a stepwise procedure using REML in R, I can use ML for this. For nested design it may be very dangerous due the difference in variance structure, mainly in a splitplot design. ML make significative variables that REML dont make. I read an article that is made a stepwise procedure using GENSTAT. from article: "Terms were dropped from a model in a

Dear all, I am trying to write an R function that can estimate Transfer functions *with additive noise* i.e. Y_t = \delta^-1(B)\omega(B)X_{t-b} + N_t where B is the backward shift operator, b is the delay and N_t is a noisy component that can be modelled as an ARMA process. The parameters to both the impulse response function and the ARMA noisy component need to be estimated simultaneously. I

I've posted this before but have not been able to locate what I'm doing wrong. I cannot determine how the forecast is made using the estimated coefficients from a simple AR(2) model when there is an exogenous variable. Does anyone know what the problem is? The help file for arima doesn't show the model with any exogenous variables. I haven't been able to locate any documents

Hi, I'm a new member here in the list. I am a graduate from University of Georgia. Recently in doing analysis using lme on a dataset, I found several questions: 1. How to express the equation when the correlation structure is very complicated. For exmaple, if the fixed is y(t)=0.03x1(t)+1.5x2(t)(I omitted "hat" and others). And the model with corARMA(p=2,q=3) is proper. What will be

In short: I didn't take enough stats courses in college. Now I am working on scientific research and I feel somewhat lost when it comes to designing the statistical framework. I have looked through the books at: http://www.r-project.org/doc/bib/R-books.html I even tried to read [17] Julian J. Faraway. Linear Models with R. This book is too advanced. It helped a little bit but I still

Hello. I am trying to determine whether I should be using ML or REML methods to estimate a linear mixed model. In the book by Pinheiro & Bates (Mixed-effects models in S and S-PLUS, page 76) they state that one difference between REML and ML is that « LME models with different fixed-effects structures fit using REML cannot be compared on the basis of their restricted likelihoods. In