similar to: multiple comparisons after glm

Dear R Users, Here is another probelm/question. I would like to run some panel regressions with R. Therefore I have combined several time periods of data for different individuals in my database. I have already run pooled OLS but I would need to calculate a Fixed Effects Estimator (within estimator). Unfortunately I couldn't find anything like that in the RSearch and I suppose that lme

Hi, I am running glm logit regressions with R and I would like to test a linear combination of coefficients (H0: beta1=beta2 against H1: beta1<>beta2). Is there a package for such a test or how can I perform it otherwise (perhaps with logLik() ???)? Additionally I was wondering if there was no routine to calculate pseudo R2s for logit regressions. Currently I am calculating the pseudo R2

> From: Siegfried.Macho > > Dear R-helpers, > > 3 questions: > 1. Is there a package that contains a routine for computing > Kendall's W > (coefficient of concordance), with and without ties ? Is that the same as Kendall's tau, as in cor(..., method="kendall")? > 2. Is there a package that contains a routine for computing > Goodman' s

Please do give reproducible example. The code you gave, which you claimed `works correctly' doesn't: > SDT.Optim <- function(crit = NULL, Hess = F) + { + q <- length(par); x <- data + if(hasArg(crit)) + cat("\n Crit present\n") + else +

I wanted to use the nls() module to solve a Problem from Sen & Srivastava (1990, p.209). Here is the (basic) code used to perform the estimation: library(SenSrivastava) library(nls) data(E9.8) # Use Linear Least Square for estimating start values lm.obj <- lm(R.1 ~ I.1 + S.1, data = E9.8) nls1.obj <- nls(R.1 ~ b.0 + b.1*(I.1^a.1-1)/a.1 + b.2*(S.1^a.2-1)/a.2,

Hello, I'm currently dealing with a rather strange problem when using the function "stepAIC" ("MASS" package). The setting is the following: From model learning data sets ("learndata"), I want to be able to build prediction functions (in order to save them in a file for further use). This is done by the function "pred.function" (see below). Therein,

Hi all, I have scoured the archives of this forum but nothing quite seems to fit the bill... I would like to plot a graph displaying two variables (y axes) that share date as the x axis. However, the date values for each variable are not the same - for example, some parasitoids were not released on days that collections from the trap took place, whilst sometimes releases did occur on the same

Hello I am using support vector machine (from package kernlab) for a classification task (with RBF-Kernel). My data has dozens of variables and I need to identify which variables contribute most to the classification performance. What I did so far is comparing the classification performance (measured for example with the proportion of misclassified cases) of different sets of variables with

This example is caused by R's messing with formula environments. That's explained in ?formula, but should it not be explained in ?model.frame ? Simple test: data <- data.frame(y=rnorm(100), x=1:100) testit <- function(formula) { weights <- runif(100) glm(formula, weights=weights, data=data) } testit(y ~ x) weights is looked for in the environment of the formula, not of

Hello Everybody, I have a problem with the output of lm. In fact, when I compare the R output and the Splus output, the results appears to be different : I have a dataframe with 5 factors , 16 rows and a response. First, I tried to fit this response to a linear model : lm(rep~(A+B+C+D+E),data). The results beetween SPlus and R are different. In a second time, I tried a new fit :

Dear Isabelle, for most of my work I have switched from S-Plus to R, because it is easier to make scripts with due to the better documentation and some other more reasonable implementations. And there was also a cost issue with the S-Plus Server, which is not for free at all. In R I use the its package for time series, which is quite sufficient for my needs. The only problem I encountered was

Dear R developpers, I am a user of Splus since many years and I have developped lots of functions to plot graph of data and model results of irregular or regular times series. In Splus regular times series are created using the rts function and irregular time series using the its functions. In both cases, times is given as a numeric vector. There is no problem in plotting a regular and an

Hello R-users, I would like to install the Java package delivered by the Omegahat project. I'm working on a Windows NT4.0 workstation. Can someone explain me ( in details please) the way to install this package. I've already read the doc relative to this topic, but I'm afraid to be too bad in Informatic to understand really well the install process. I hope someone has an answer for

Dear ladies and gentlemen, I have recently installed the latest version of R (4.3.3) for windows. Now I have the following problems with mutated vowels like ?, ?, etc. Here is an example: If I type the command: Dir <- "C/Users/macho/Documents/_LVn/Experimentelle ?bungen" in the R console there is no problem. However, if I put the same command into a source file (e.g. Test.r) and call

Hello, I would like to know if there is a possibility to "clean" the R memory during a R session. In fact, I realize a lot of instruction with large objects (matrix of 500*5000), and I can not manage to achieve the end of my script due to memory lack. Of course, I've tried to remove all "temporary object" during the script execution and to perform a garbage collector ...

Hello, I would like to treat a very simple case : to fit a linear model with 5 parameters (including main terms, interactions and quadratic terms using a central composite design with 27 runs) for a constant response (e.g resp = 100.0). The fitting process works and return me a good intercept value (the value of my constant) and some negligeable effects (around e-15). But, I don't understand

Hello, I am try to perform a modeling which is relevant in a strongly heteroscedastic context. So I perform a dual modeling (modeling of both mean and variance of a response) in using the following loop: jointmod <- function(formula, data, itercrit=10,devcrit=0.0001) { # # Init step # init <- glm(formula=formula,family=gaussian, data=data) response <-

Hello, I wonder if anybody has some idea about how to solve my problem : I am working , I would say trough an experimental design approach (perform experiments, get responses, make regression, sensitivity analysis, risk analysis ...). The problem is now that I have to face with not only a response but a vector or a matrix (typically a spatial distribution of a physical property ... pressure).

Hello, I would like to know if there is a possibility to open an R session via Java (using the SJava package), then to terminate it, and re-run another. It seems not to be possible. If this is the case, I would like to understand where is the problem or the limitation (is it due to the SJava implementation, to the Java behavior, or to the R application). In fact, I am interesting in re-starting

Hi group, I'm trying to do a Tukey test to compare the means of a factor ("treatment") with three levels in an lme model that also contains the factors "site" and "time": model = response ~ treatment * (site + time) When I enter this model in csimtest, it takes all but the main factor "treatment" as covariables, not as factors (see below). Is it