similar to: How to show the definition of a S3 function?

Dear All, I would like to be able to estimate confidence intervals for a linear combination of coefficients for a GLS model. I am familiar with John Foxton's helpful paper on Time Series Regression and Generalised Least Squares (GLS) and have learnt a bit about the gls function. I have downloaded the gmodels package so I can use the estimable function. The estimable function is very

Trying to use contrast to look at differences within an lme lme.fnl.REML <- lme(Max ~ S + Tr + Yr + Tr:Yr, random = ~1 |TID, method = "REML") I have three levels of Tr I'm trying to contrast among different years (R, T97, T98), years = 1997-1999, so I'm interested in contrasts of the interaction term. > anova(lme.fnl.REML) numDF denDF F-value

Hi! I try to explain the efffect of (1) forest where i took samples's soils (* Lugar*: categorical variable with three levels), (2) nitrogen addition treatments (*Tra*: categorical variable with two levels) on total carbon concentration's soil samples (*C: *continue* *variable) during four months of sampling (*Time:* categorical and ordered variable with four levels). I fitted the

Hi, all: Two examples are shown below. I want to use the multiple comparison of Dunnett. It succeeded in upper case "example 1". However, the lower case "example 2" went wrong. In "example 2", the function pmvt return NaN, so I cannot show this simtest result. Is there any solution? (I changed the variable "maxpts" to a large number in front of the

Dear all I use the gls function but in contrast to the lm function in which when I type summary(lm(...))$coef I receive all the coefficients (estimate, Std. Error, t-value and pvalue), with gls when I type summary(gls(...))$coef I only receive the estimate of the reg. coefficient without std. error and t- and p-values. Dou you have any suggestion how to solve my problem? With kind regards

I found this paper on ANOVA on unequal error variance. Has this be incorporated to any R package? Is there any textbook that discuss the problem of ANOVA on unequal error variance in general? http://www.jstor.org/stable/2532947?cookieSet=1

I am cleaning up the rms package to not export functions not to be called directly by users. rms uses generic functions defined in other packages. For example there is a latex method in the Hmisc package, and rms has a latex method for objects of class "anova.rms" so there are anova.rms and latex.anova.rms functions in rms. I use:

Hi A.K Regarding my question on comparing normal/ obese/overweight with blood pressure change, I did finally as per the first suggestion of stacking the data and creating a normal category . This only gives me a obese not obese 14, but when I did with the wide format hoping to get a obese14,normal14,overweight 14 Vs hibp 21, i could not complete any of the models. This time I classified obese=1

Hello everybody, Please help with connecting the AIC and BIC numbers printed by summary.gls to the logLik number. 1. is the logLik number the true ML or density scaling constants have been omitted? 2. what is the formula for calculating the AIC and BIC from logLik (and how can I see it)? I tried printing summary.gls but it says object not found. Thank you very much. Stephen [[alternative

I found a tutorial for creating classes using generic functions ? S3 way ! It was short description so I couldn't grok in full its usage ... So far so good, what i currently do is something like this : Blah <- function(data,...) UseMethod('Blah') Blah.default <- function( ...... ) { self = .... class(self) <- 'Blah' self } Blah.some_method <-

I just installed R-devel to check my package before submitting. I got an error in my vignette in regards to as.vector. When I looked at the code for as.vector in R-devel it is standardGeneric for "as.vector" defined from package "base" function (x, mode) standardGeneric("as.vector") <environment: 0x0918ad70> Methods may be defined for arguments: x, mode Use

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Dear all, I am developing a package, which bundles my most frequently used functions. One of those is a modified version of droplevels from the base package (basically, it preserves any contrast function which was used to create the factor, contrast matrices are not kept, for they could be wrong if a level is dropped). In my NAMESPACE file I've the following directives, which should export

The contrast and desirability packages are available for all platforms at cran.r-project.org (and coming soon to a mirror near you). The contrast package extends Frank Harrell's contrast.Design function for one degree of freedom contrasts of model parameters to other types of models, such as lm, glm, lme, gls and geese models. Fold-changes are also calculated for all contrasts. There is a

The contrast and desirability packages are available for all platforms at cran.r-project.org (and coming soon to a mirror near you). The contrast package extends Frank Harrell's contrast.Design function for one degree of freedom contrasts of model parameters to other types of models, such as lm, glm, lme, gls and geese models. Fold-changes are also calculated for all contrasts. There is a

First of all thank you for the responses. I appreciate the suggestions i have received thus far. Just to reiterate I am trying to analyze a data set that has been collected from a hierarchical sampling design. The model should be a mixed model nested ANOVA. The purpose of my study is to analyze the variability at each spatial scale in my design (random factors, variance components), and say

When using the PLM package (version 1.2-8), I encounter the probem that calling the FGLS estimator evokes strange behavior, when choosing the "random" effects model. After calling the PGGLS function to estimate FGLS, PLM gives me a warning, stating that the "random" model has been replaced with the "pooling" model. I would, however, really like to estimate the random

I have included data at the bottom of this email. It can be read in by highlighting the data and then using this command: dat <- read.table("clipboard", header = TRUE,sep="\t") I can obtain solutions with both of these: library(gee) fit.gee<-gee(score ~ chem + time, id=id, family=gaussian,corstr="exchangeable",data=dat) and library(yags) fit.yags <-

Hi all I am analyzing a data set containing information about the behaviour of marine molluscs on a vertical wall. Since I have replicate observations on the same individuals I was thinking to use the geepack library. The data are organised in a dataframe with the following variables Date = date of sampling, Size = dimensions (mm) Activity duration of activity (min) Water = duration of

Hi R-users, I have a problem for updating the estimates of correlation coefficient in simulation loop. I want to get the matrix of correlation coefficients (matrix, name: est) from geese by using loop(500 times) . I used following code to update, nsim<-500 est<-matrix(ncol=2, nrow=nsim) for(i in 1:nsim){ fit <- geese(x ~ trt, id=subject, data=data_gee, family=binomial,