similar to: Goodness of fit tests

Has anyone got vim to have syntax highlighting with R function codes? I know there's something similar that works with emacs (ESS or something like that), but I was wondering if anyone knew an equivalent that worked with vim. Thank you, -- []'s mentus at gmx.de Bitte l?cheln! Fotogalerie online mit GMX ohne eigene Homepage!

I noticed some very weird behaviour of the function: options(digits=n), where n is the number of digits you would expect to get in R calculations. Let's take a example: > options(digits=4) > getdata(caso.pool.k3.r3.e2) [1] 6.053 2.641 -3.639 14.259 6.082 Which works fine... now, trying again, with different data: > options(digits=4) > getdata(controle.pool.k3.r3.e2)

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

Dear R users, I am writing to you because I have a few question on how to fix the error term variances in lme in the hope that you could help me. To my knowledge, the closest possibility is to fix the var-cov structure, but not the whole var-cov matrix. I found an old thread (a few years ago) about this, and it seems that the only alternative is to write the likelihood down and use optim or a

Hi, my doubt is very simple. I'm sure I've seen someone using something like this before, but unfortunatelly my searches in the archives were useless. Well, I have some objects called after a name that has a number attached to it, varying. Let's say I have: > ls poly1 poly2 poly3 poly4 poly5 poly6 ... I would like to access these objects using a for(), in which I could do

Is it possible in R to subset a dataframe by more than one factor, all at once? For instance, I have the dataframe: >data p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 pred 1 0 1 0 0 0 0 0 0 0 0 0.5862069 4 0 0 0 0 0 0 0 0 0 1 0.5862069 5 0 0 0 0 0 0 1 0 0 0 0.5862069 6 0 0 0 0 0 0 0 1 0 0 0.5862069 7 0 0 1 0 0 0 0 0 0

Dear all, Given a LME model (following the notation of Pinheiro and Bates 2000) y_i = X_i*beta + Z_i*b_i + e_i, is it possible to extract the variance-covariance matrix for the estimated beta_i hat and b_i hat from the lme fitted object? The reason for needing this is because I want to have interval prediction on the predicted values (at level = 0:1). The "predict.lme" seems to

Hello! I would like to use lmer() to fit data, which are some estimates and their standard errors i.e kind of a "meta" analysis. I wonder if weights argument is the right one to use to include uncertainty (standard errors) of "data" into the model. I would like to use lmer(), since I would like to have a "freedom" in modeling, if this is at all possible. For

Is it possible to use R as a data-mining tool? Here's the problem I've got. I have a couple of data sets consisting of results from a cDNA microarray experiment - the details about the biology don't really matter here, the same theory applies for any other data-mining task (that's why I thought it'd be more appropriate to post this on r-user). Each of these datasets consists

hi! i want to change the "robustness weights" used by loess. these are described on page 316 of chambers and hastie's "statistical models in S" book as r_i = B(e_i,6m) where B is tukey's biweight function, e_i are the residulas, and m is the median average distance from 0 of the residuals. i want to change 6m to, say, 3m. is there a way to do this? i cant

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite the bullet and

Hello R users! I've been experimenting with lmer to estimate a mixed model with a dichotomous dependent variable. The goal is to fit a hierarchical model in which we compare the effect of individual and city-level variables. I've run up against a conceptual problem that I expect one of you can clear up for me. The question is about random effects in the context of a model fit with a

Hi all, I am doing some experiment studies... It seems to me that with different combination of 5 parameters, the end results ultimately converged to two scalars. That's to say, some combinations of the 5 parameters lead to one end result and some other combinations of the 5 parameters lead to the other end result (scalar). I am thinking of this is sort of something like clustering or

Hi, If I have a vector: junk <- c(2,0,0,3,0) and want to access, say, all the elements that are greater than zero. I just do: junk[which(junk>0)] Now, If I have a list: jlist <- list(NULL,c(1,0),NULL,c(1,2,3), NULL) and want to access all the elements that have length greater than zero, I know how to find the elements with: which(sapply(jlist,length)>0) But how do I get a

Dear R-devel & Translation Teams, In order to monitor the progress of the translation for the pt_BR team I wrote a script to summarize the status of the translations. It wasn't difficult to extend it to the other languages so I decided to set up a page with the summaries of the translation for all languages for which currently exist a translation.

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests). I am following Smoothing Splines, D.G. Pollock (available online) where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) + e_i

Hi, Imagine a vector x with elements (1,2,1,1,3,5,3,3,1) and a vector y with elements (2,3). I need to find out what elements of x match any of the elements of y. Is there a simple command that will return a vector with elements (F,T,F,F,T,F,T,T,F). Ideally, I would like a solution that works with dataframe colums as well. I have tried x==y and it doesn't work. x==any(y) doesn't work

Dear all, I fitted a non-parametric model using GAM function in R. i.e., gam(y~s(x1)+s(x2)) #where s() is the smooth function Then I obtained the coefficients(a and b) for the non-parametric terms. i.e., y=a*s(x1)+b*s(x2) Now if I want to use this estimated model to do optimization or sensitivity analysis, I am not sure how to incorporate the smooth function since s() may not

Hello, I come from a non statistics background, but R is available to me, and I needed to test an implementation of smoothing spline that I have written in c++, so I would like to match the results with R (for my unit tests) I am following http://www.nabble.com/file/p25569553/SPLINES.PDF SPLINES.PDF where we have a list of points (xi, yi), the yi points are random such that: y_i = f(x_i) +