similar to: Chi square value of anova(binomialglmnull, binomglmmod, test="Chisq")

Hello R users, I'm working with a time-series of several years and to analyze it, I?m using GAM smoothers from the package mgcv. I?m constructing models where zooplankton biomass (bm) is the dependent variable and the continuous explanatory variables are: -time in Julian days (t), to creat a long-term linear trend -Julian days of the year (t_year) to create an annual cycle - Mean temperature

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA

Hi Dears, When I introduce an interaciton in a piecewise model I obtain some quite unusual results. If that would't take u such a problem I'd really appreciate an advise from you. I've reproduced an example below... Many thanks x<-rnorm(1000) y<-exp(-x)+rnorm(1000) plot(x,y) abline(v=-1,col=2,lty=2) mod<-lm(y~x+x*(x>-1)) summary(mod) yy<-predict(mod)

Greetings, I am interested in creating a stepwise fixed order regression function. There's a function for this already called add1( ). The F statistics are calculated using type 2 anova (the SS and the F changes don't match SPSS's). You can see my use of this at the very end of the email. What I want: a function to make an anova table with f changes and delt R^2. I ran into

#### I checked through every 3 factor * 3 loading case. #### While, 4 factor * 3 loading failed. #### the data is 6 factor * 3 loading require(sem); cor18<-read.moments(); 1 .68 1 .60 .58 1 .01 .10 .07 1 .12 .04 .06 .29 1 .06 .06 .01 .35 .24 1 .09 .13 .10 .05 .03 .07 1 .04 .08 .16 .10 .12 .06 .25 1 .06 .09 .02 .02 .09 .16 .29 .36 1 .23 .26 .19 .05 .04 .04 .08 .09 .09 1 .11 .13 .12 .03 .05 .03

Hello everyone, I hope this is a fair enough question, but I don’t have access to a copy of Bates and Pinheiro. It is probably quite obvious but the answer might be of general interest. If I fit a fixed effect with an added quadratic term and then do it as an orthogonal polynomial using maximum likelihood I get the expected result- they have the same logLik.

Hi all. if there is limitation for the registers to be used together in an instruction, should i try to change it in the register allocation pass or should i try it somewhere else?? example. lets say we have to add 2 registers addu rx ,ry ,rz there is a limitation that says that the two regs that will be added they can not have the same mod4 so we can add r1 , r2 but cannot add r1,r5.

I can use stepAIC on an lme object in 1.6.2, but I get the following error if I try to do the same in 1.7.0: Error in lme(fixed = resp ~ cov1 + cov2, data = a, random = structure(list( : unused argument(s) (formula ...) Does anybody know why? Here's an example: library(nlme) library(MASS) a <- data.frame( resp=rnorm(250), cov1=rnorm(250), cov2=rnorm(250),

Dear all, I am having a little trouble deciphering how to change the default x-axis labels in a lattice xyplot (or any type of lattice plot for that matter). I have tried using the "demo("labels") function but the code is truncated at precisely the wrong moment! All I am trying to do is to add superscript to two of the labels for which i tried using the expression function. It

Hi all, I run CentOS5.1 with all updates, and like to use metacity's keybindings (gconf-editor->/apps/metacity/global_keybindings and keybinding_commands) to set shortcut keys for xterm, nautilus, and the like, so <Mod4>x runs xterm and <Mod4>e runs nautilus, rather how Windows-XP keyboard shortcuts work. I always log on as 'neek', the only user other than

Dear Thierry, thank you for the quick reply. I have only one question about the approach you proposed. As you suggested, imagine that the model we end up after the model selection procedure is: mod2.1 <- lmer(dT_purs ~ T + Z + (1 +T+Z| subject), data =x, REML= FALSE) According to the common procedures specified in many manuals and recent papers, if I want to compute the p_values relative to

Dear R-help members, Apologies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we

Dear R help, I am trying to work out if I am justified in log-transforming data and specifying Gamma in the same glm. Does it have to be one or the other? I have attached an R script and the datafile to show what I mean. Also, I cannot find a mixed-model that allows Gamma errors (so I cannot find a way of including random effects). What should I do? Many thanks, Pete --------------

Dear List-members, I found a strange behaviour in the lqs function. Suppose I have the following data: y <- c(7.6, 7.7, 4.3, 5.9, 5.0, 6.5, 8.3, 8.2, 13.2, 12.6, 10.4, 10.8, 13.1, 12.3, 10.4, 10.5, 7.7, 9.5, 12.0, 12.6, 13.6, 14.1, 13.5, 11.5, 12.0, 13.0, 14.1, 15.1) x1 <- c(8.2, 7.6,, 4.6, 4.3, 5.9, 5.0, 6.5, 8.3, 10.1, 13.2, 12.6, 10.4, 10.8, 13.1, 13.3, 10.4, 10.5, 7.7, 10.0, 12.0,

Hi Nikos, You can model your requirement in the *.td using RegisterClass as def SrcRegs : RegisterClass<"Src", [i32], 4, (add R0, R2, R4, R6 )>; def DstRegs : RegisterClass<"Dst", [i32], 4, (add R1, R3, R5, R7 )>; Thanks ~Umesh On Thu, Nov 7, 2013 at 8:25 PM, Stavropoulos Nikos < n.stavropoulos at think-silicon.com> wrote: > Hi all.

Bonjour, Je suis d?sol? de poser ma question en fran?ais mais mon anglais n'est pas suffisamment correct pour le sujet que je veux aborder. Mon ?tablissement scolaire utilise un serveur de notes PRONOTE et chez moi j'ai install? un client sous Wine. La connexion au serveur se fait sans difficult?, je peux rentrer mes notes et j'ai acc?s ? tous les services propos?s par ce logiciel.

Hi, I do not use R lenguage for programming, hence Im trying to understand how are specified the models below. I been reading some webpages with information about the notation in the "lm" function, but havent understand exactly what does the ":" do. Can some one give me an explenation of the models below so I can replicate the results in other program. #common intercept

On Tue, Mar 25, 2014 at 6:11 PM, Martin Peres <martin.peres at free.fr> wrote: > Other kind of accesses are unreliable on Maxwell cards. As advised by NVIDIA, Maxwell or Kepler? > let's only use 32-bit accesses to fetch the vbios from PROM. > > This fixes vbios fetching on my nve7 which failed in certain specific > conditions. > > I suggest we Cc stable, for all

Hi, I am trying to use piecewise linear regression to approximate a nonlinear function. Actually, I don't know how many linear functions I need, therefore, I want build an array of regression models to automate the approximation job. Could you please give me any clue? Attached is ongoing code: rawData = scan("c:/zyang/mass/data/A01/1.PRN", what=list(numeric(),numeric())); len =

Hi folks, I am tearing my hair out on this one. I am using an example from Pinheiro and Bates. ### this works data(Orange) mod.lis <- nlsList(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange ) ### This works mod <- nlme(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange, fixed = Asymp + xmid + scal ~ 1, start =