similar to: refitting lm() with same x, different y

Christoph -- There was just a thread on this earlier this week. You can search in the archives for the title: "refitting lm() with same x, different y". (Actually, it doesn't turn up in the R site search yet, at least for me. But if you just go to the archive of recent messages, available through CRAN, you can search on refitting and find it. The original post was from William

Hi. I'm using lmer() from the lme4 package (version 0.8-3) and I can't get anova() to group variables properly. I'm fitting the mixed model Response ~ Weight + Experimenter + (1|SUBJECT.NAME) + (1|Date.StudyDay) where Weight is numeric and Experimenter is a factor, ie, > str(data.df) `data.frame': 4266 obs. of 5 variables: $ SUBJECT.NAME : Factor w/ 2133 levels

Dear All, I want to test whether a method exists for given object. For example, whether a function "deviance" is defined for an object of the "lm" class. My imperfect understanding leads me to think something like hasMethod("deviance", object) hasMethod("deviance", "lm") existsMethod("deviance", signature(class="lm"))

Dear All, I have been trying to simulate data from a fitted glm using the simulate() function (version details at the bottom). This works for lm() fits and even for lmer() fits (in lme4). However, for glm() fits its output does not make sense to me -- am I missing something or is this a bug? Consider the following count data, modelled as gaussian, poisson and binomial responses: counts

Hi, While exploring how summary.lm generated its output I came across a section that left me puzzled. at around line 57 R <- chol2inv(Qr$qr[p1, p1, drop = FALSE]) se <- sqrt(diag(R) * resvar) I'm hoping somebody could explain the logic of these to steps or alternatively point me in the direction of a text that will explain these steps. In particular I'm puzzled

Dear All, I wish to model random effects that have known between-group covariance structure using the lme() function from library nlme. However, I have yet to get even a simple example to work. No doubt this is because I am confusing my syntax, but I would appreciate any guidance as to how. I have studied Pinheiro & Bates carefully (though it's always possible I've missed

Dear R experts, I have a Matlab code which I am translating to R in order to examine and enhance it. First of all, I need to reproduce in R the results which were already obtained in Matlab (to make sure that everything is correct). There are some matrix manipulations and '\' operation among them in the code. I have the following data frame > ABS.df Pro syn

I am trying to show some techniques to my graduate regression class. The textbook mentioned using bootstrap samples of regression coefficients for assessing variability. I decided to show them reasonably effective ways of doing the resampling. The following is a function I wrote to create bootstrap samples of coefficients from a fitted linear regression model. bsCoefSample <- ##

I have been struggling to find some informaation on what lm exactly does. I know it uses the QR decomp. However, I was recently faced with a somewhat badly scaled matrix and summary(lm) said Coefficients: ( 4 not defined because of singularities) does anyone know how lm chooses these 4 coef. is it forward building of the model --> drop last when qr sends a non full rank design matrix? My

Hi all, I have one array of predictors, one observation per row, and one array of responses, also arranged one observation per row. I arrange these into a data.frame and call lm() with a pasted-together formula. I would like to call lm() with a number of responses in excess of 100, but for some reason, 39 seems to be a limit. Why do I get an "invalid variable names" error from

# r-bugs@r-project.org The problem occurs when the model matrix has a single column. > elastic <- data.frame(stretch=c(46,54,48,50,44,42,52), distance=c(183,217,189,208,178,150,249)) > elastic.lm <- lm(distance ~ -1 + stretch, data=elastic) > predict(elastic.lm,se=T) Error in XRinv^2 %*% rep(res.var, p) : non-conformable arguments The fix is to replace XRinv <-

I think one needs an LU decomposition rather than QR. However, I couldn't find anything off the shelf to do an LU, other than learning that determinant() now uses LU instead of QR or SVD, so the code to do it must be in there for those that want it. You'll probably need to divide rows of U by the first entry if you insist on the unique reduced REF. However, I can't see any reason

Greetings! Recently when I tried to use lm.influence I get the following error: Error in .Fortran("lminfl", model$qr$qr, n, n, k, as.integer(do.coef), : Fortran symbol name "lminfl" not in DLL for package "base" This occurs on both Linux and Windows platforms (details below). Searching the mail lists and other sources indicates that the fortran code for

Dear R help, I've encountered a problem with survreg's anova(). I am currently writing general code to fit a variety of models using different fitting functions. Here's a simple example of what I'm trying to do: ---begin code--- # general function to analyse data analyse.data <- function(formula, FUN, data, ...) { fit <- FUN(formula, data=data, ...) anova(fit)

On Thu, 19 Apr 2007, brech at delphioutpost.com wrote: > Full_Name: Christian Brechbuehler > Version: 2.4.1 Patched (2007-03-25 r40917) > OS: Linux 2.6.15-27-adm64-xeon; Ubuntu 6.06.1 LTS > Submission from: (NULL) (24.61.47.236) > > > Splus and R have different ideas about what qr.coef(qr()) should return, > which is fine... but I believe that R has a bug in that it is not

Dear R people, I do not have much knowledge about linear algebra but currently I need to understand what the function qr.qty is actually doing. The documentation states that it calculates t(Q) %*% y via a previously performed QR matrix decomposition. In order to do that, I tried following basic example: m<-matrix(c(1,0,0,0,1,0,0,0,1,0,0,1),ncol=3) # 4x3 matrix

I suggest adding a 'pivot' argument to qr.R, to obtain columns in the same order as the original x, so that a <- qr(x) qr.Q(a) %*% qr.R(a, pivot=TRUE) returns x. -------------------------------------------------- # File src/library/base/R/qr.R qr.R <- function(qr, complete = FALSE, pivot = FALSE) { # Args: # qr: a QR decomposition, produced by qr() # complete:

Dear R users, I'm trying to use function 'refit' from lme4 and I get this error that I can't understand: > refit(dolo4.model4,cbind(uu,50-uu)) Error in function (classes, fdef, mtable) : unable to find an inherited method for function "refit", for signature "mer", "matrix" if I try: > refit(dolo4.model4,uu) Error in asMethod(object) :

Hi R-helpers, Can some one tell me how to train 'mynn' of this type?: mynn <- nnet(y ~ x1 + ..+ x8, data = lgist, size = 2, rang = 0.1, decay = 5e-4, maxit = 200) I assume that this nn is untrained, and to train I have to split the original data into train:test data set, do leave-one-out refitting to refine the weights (please straighten this up if I was wrong). I just don't know

Le 23/01/2018 ? 08:47, Martin Maechler a ?crit?: >>>>>> Serguei Sokol <sokol at insa-toulouse.fr> >>>>>> on Mon, 22 Jan 2018 17:57:47 +0100 writes: > > Le 22/01/2018 ? 17:40, Keith O'Hara a ?crit?: > >> This behavior is noted in the qr documentation, no? > >> > >> rank - the rank of x as