similar to: Re: re| R-list| internal-fields

Hi, I am seeking help with an error when running the example from R Documentation for TukeyHSD. The error occurs with any example I run, from any text book or website. thank you... > plot(TukeyHSD(fm1, "tension")). Error in plot(confint(as.glht(x)), ylim = c(0.5, n.contrasts + 0.5), ...) : error in evaluating the argument 'x' in selecting a method for function

Dear all, I noticed the following behaviour of plot.lm: > fm1 <- lm(time~dist, data=hills, weights=c(0,0,rep(1,33))) > par(mfrow=c(2,2)) > plot(fm1) Warning messages: 1: longer object length is not a multiple of shorter object length in: res/(sd * (1 - hat)) 2: longer object length is not a multiple of shorter object length in: (res/(sd * (1 - hat)))^2 * hat which seems to be

Stephen, thanks for your effort. The more appropriate list for this discussion is probably R-devel (as far as I understand it) so I've moved the discussion there. Related topics have already been discussed in the past. Specifically, I remember contributions by Paul Johnson ("rockchalk" package) and John Fox ("effects" and "car" package) as their packages

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 <- ##

Hello: I am trying to understand why glm() does not replicate the results in Dobson, "Introduction to Generalized Linear Models," pp. 17-20. I set up the following model. The variable CONDT is assumed as Poisson and the objective is to estimate the expected value. The data (chronic medical conditions among women in Australia) is as follows: CONDT <- c(0, 1, 1, 0, 2, 3, 0, 1,

I''m a physicist working on fusion energy and dabble in statistics only occasionally, so please excuse gaps in my statistical knowledge. I''d appreciate any help that a real statistics expert could provide. Most people in my field do only very simple statistics, and I am trying to extend some work on multivariate linear regression to account for significant between-group

Dear friends. Below is ouput directly from the help on qqnorm.lme.html It do not seem to work as expected - on win98, R 1.1, although the regression is undertaken, so qqnorm misunderstands, or what ? library(nlme) data(Orthodont) fm1 <- lme(distance ~ age, Orthodont, random = ~ age | Subject) # normal plot of standardized residuals by gender qqnorm(fm1, ~ resid(., type = "p") | Sex,

Dear Terry, It's not surprising that different modeling functions behave differently in this respect because there's no articulated standard. Please see my response to Martin for my take on the singular.ok argument. For a highly sophisticated user like you, singular.ok=TRUE isn't problematic -- you're not going to fail to notice an NA in the coefficient vector -- but I've

>>>>> Fox, John <jfox at mcmaster.ca> >>>>> on Wed, 13 Sep 2017 22:45:07 +0000 writes: > Dear Terry, > Even the behaviour of lm() and glm() isn't entirely consistent. In both cases, singularity results in NA coefficients by default, and these are reported in the model summary and coefficient vector, but not in the coefficient covariance

Dear Martin, I made three points which likely got lost because of the way I presented them: (1) Singularity is an unusual situation and should be made more prominent. It typically reflects a problem with the data or the specification of the model. That's not to say that it *never* makes sense to allow singular fits (as in the situations you mentions). I'd favour setting

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use

hello, I have programmed this function to calculate the Neuman-Keuls test but I have a problem the function return an empty list and I don't know why. summary(fm1) E <- sqrt((summary(fm1)[[1]]["Residuals","Mean Sq"])/length(LR)) lst <- list() lst1 <- list() lst2 <- list() NK <- function (x) { if (length(x) == 2) { Tstudent <- t.test(subset(exple,

How does one coerce predict.gls to incorporate the fitted correlation structure from the gls object into predictions? In the example below the AR(1) process with phi=0.545 is not used with predict.gls. Is there another function that does this? I'm going to want to fit a few dozen models varying in order from AR(1) to AR(3) and would like to look at the fits with the correlation structure

>>>>> Fox, John <jfox at mcmaster.ca> >>>>> on Thu, 14 Sep 2017 13:46:44 +0000 writes: > Dear Martin, I made three points which likely got lost > because of the way I presented them: > (1) Singularity is an unusual situation and should be made > more prominent. It typically reflects a problem with the > data or the

In a newly downloaded version (today) of R-2-4-0alpha, with all packages from CRAN also installed today, I get: > library(Matrix) Erro en loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'Matrix' methods specified for export, but none defined: BIC, anova, coef, confint, deviance, fitted, fixef, formula, head, lmer, logLik, mcmcsamp, plot,

R developers, I have a first attempt to make an aov function. Eventually I want to build in Error() structure, but first I am trying to get this presentable for balanced data with only a single stratum, just using residual error. I am following R. M. Heiberger's Computation for the Analysis of Designed Experiments, Wiley (1989) I a using a wrapper (aov.bal) to call the

When I try to run the example from Variogram with an lme object, I get an error (although summary works): R : Copyright 2005, The R Foundation for Statistical Computing Version 2.2.1 (2005-12-20 r36812) ISBN 3-900051-07-0 ... > fm1 <- lme(weight ~ Time * Diet, BodyWeight, ~ Time | Rat) Error: couldn't find function "lme" > Variogram(fm1, form = ~ Time | Rat, nint =

coef() and vcov() have different dimensions if a model contains alised parameters as the following example illustrates. A search on "alised" gave noting as far as I could see. Is this a known bug? Bendix C ---------------------- Bendix Carstensen Senior Statistician Steno Diabetes Center Niels Steensens Vej 2 DK-2820 Gentofte Denmark tel: +45 44 43 87 38 mob: +45 30 75 87 38 fax: +45

(A repost of this request with a bit more detail) Hi, All. I'd like to calculate effect sizes for aov or lme and seem to have a bit of a problem. partial-eta squared would be my first choice, but I'm open to suggestions. I have a completely within design with 2 conditions (condition and palette). Here is the aov version: > fit.aov <- (aov(correct ~ cond * palette +

> Date: Thu, 29 Jun 2000 14:22:24 +0000 > From: Lilla Di Scala <lilla at dimat.unipv.it> > I have a problem regarding the anova() output. When I apply it to a > single regression model, I do not understand how the values > corresponding to the F statistics are obtained by the software. I > believe that they are computed using differences between residual sums > of