similar to: How to get the intercept from lm?

Hello: Let me rephrase my question to attract interest in the problem I'm having. When I appply anova() to two equations estimated using glmmPQL, I get a complaint, > anova(fm1, fm2) Error in anova.lme(fm1, fm2) : Objects must inherit from classes "gls", "gnls" "lm","lmList", "lme","nlme","nlsList", or "nls"

Hi I am sure this is simple - but how can I convert one charecter vector into one string? example: x <- c("This ", "is ", "one ", "sentence.") should become "This is one entence" Thanks Rainer -- NEW EMAIL ADDRESS AND ADDRESS: Rainer.Krug at uct.ac.za RKrug at sun.ac.za WILL BE DISCONTINUED END OF MARCH Rainer M. Krug, Dipl. Phys.

Hi I know how to referenc R in a scientific paper - but is there a standardised way to reference packages? Thanks Rainer -- NEW EMAIL ADDRESS AND ADDRESS: Rainer.Krug at uct.ac.za RKrug at sun.ac.za WILL BE DISCONTINUED END OF MARCH Rainer M. Krug, Dipl. Phys. (Germany), MSc Conservation Biology (UCT) Plant Conservation Unit Department of Botany University of Cape Town Rondebosch 7701

Hi I want to use order() to get the order of a vector. But I would need a different behavior when ties occur: similar to the parameter ties.method = "random" in the rank() function, I would need to randomise the ties. Is this possible? Example: x <- rep(1:10, 2) order(x) [1] 1 11 2 12 3 13 4 14 5 15 6 16 7 17 8 18 9 19 10 20 order(x) [1] 1 11 2 12 3 13 4 14 5 15

Hi I use the following code and it stores the results of density() in the list dr: dens <- function(run) { density( positions$X[positions$run==run], bw=3, cut=-2 ) } dr <- lapply(1:5, dens) but the results are stored in dr[[i]] and not dr[i], i.e. plot(dr[[1]]) works, but plot([1]) doesn't. Is there any way that I can store them in dr[i]? Thanks a lot, Rainer -- Rainer M. Krug,

I need to use Alt-F2 shortcut in Far Manager running with wineconsole. But Alt-F2 invokes standard Linux "run command" dialog. How can I intercept the Alt-F2 so that it is processed by Wine application?

I realize that the following has been talked about on this list many times before in some related way but I am going to ask for help anyway because I still don't know what to do. Suppose I have no intercept models such as the following : Y = B*X_1 + error Y = B*X_2 + error Y = B*X_3 + error Y = B*X_4 + error and I run regressions on each ( over the same sample of Y ) and now I want to

Hi all, Dear Dr. Wheeler, I am trying to use the lmPerm package to perform multiple regression on microarray data with certain empirical variables associated with treatments of the experiment. In order the circumvent the very conservative multiple test corrections such as Bonferroni and BH, I try to use permutated probabilities to assess associations. In addition to mu previous posting I

Hello, I'm implementing a function using non uniform B-Splines. Looking at the code of the bs() function, I realized that if the intercept was set to FALSE, the behavior of the function was the following (df is the number of degrees of freedom that I believe can be interpreted as the number of control points): - Compute df- ord + 1 _internal_ knots (ord is the order of the spline) - Add ord

Dear list, I'm trying to fit a glm model using family=poisson(link = "identity"). The problem is that the glm function fits a model with a negative intercept, which sounds like a nonsense to me, being the response a Poisson variable. >From a previous discussion on this list I've understood that the glm function uses IRLS for the fitting without any constraint so it is

It's easy to run a linear regression on a simple model without an intercept just by doing this: lm(y ~ x1 + x2 -1) Is there a similar trick to suppress the intercept when your model is in a large dataframe and you don't want to write out the names of individual columns? -- View this message in context:

I would appreciate if someone could tell me how to fit a non-intercept model using lrm (and not glm). The -1 in the formula of the glm does not work with lrm. Thanks, Clarissa [[alternative HTML version deleted]]

Dear R users, glm.fit() gave me the same AIC's regardless of TRUE or FALSE intercept option. > myX <- as.matrix(1:10) > myY <- 3+5*myX > foo <- glm.fit(x=myX, y=myY, family = gaussian(link = "identity"), intercept=TRUE) > foo$aic [1] 38.94657 > foo <- glm.fit(x=myX, y=myY, family = gaussian(link = "identity"), intercept=FALSE) > foo$aic [1]

Hi, dear R-help, I want to fit a Buckley-James censored regression without intercept. The function bj() inside the Design library have to have an intercept, I try Surv(y,d)~ 0 + x , or -1+x, or x-1, none works. Any suggestions? Is there another BJ() code out there that do not have to have an intercept? Or may be it is easier to modify the bj()? I need the estimator only, no need of var estimate

SVM patch to add add init intercept handler. Applies cleanly to 9622. Please apply. Signed-off-by: Tom Woller <thomas.woller@amd.com> _______________________________________________ Xen-devel mailing list Xen-devel@lists.xensource.com http://lists.xensource.com/xen-devel

Hi, This might be a silly question, but how do I create a pooled model (binomial family) as a baseline for a likelihood-ratio test for a random-intercept lmer model? R won't compare a glm model with an lmer model (or glmmPQL ). Thanks in advance. -Bobby

This very likely falls in the category of an unexpected result due to user ignorance. I generated the following data: time <- 0:10 set.seed(4302007) y <- 268 + -9*time + .4*(time^2) + rnorm(11, 0, .1) I then fit models using both orthogonal and raw polynomials: fit1 <- lm(y ~ poly(time, 2)) fit2 <- lm(y ~ poly(time, degree=2, raw=TRUE)) > predict(fit1, data.frame(time =

Hello I am running a regression without the intercept, and want to compute dfbetas. How do I do this? The dfbetas function only works when the intercept is included in the model. Regards K [[alternative HTML version deleted]]

Does anyone have all the Telco intercept prompts (numbers and such) with voice inflections to simulate number referrals and disconnects I could download? TIA, Bart -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.digium.com/pipermail/asterisk-users/attachments/20080522/08b001ce/attachment.htm

Hello, I have encountered a problem which may be arising from details of my data and or the statistics I am trying to do, or may be arising due to the way leaps works internally. Unfortunately, I am not yet savvy enough to tell why. I can say that this statement works (or at least works to the degree I expect): b <- regsubsets(FUND~.,data=all, intercept=TRUE, nbest=1, nvmax=8, really.big=T,