similar to: Test for treatment effect in a logistic regression

Dear All, I am estimating a Cox proportional hazard model, with several interactions of the type a*z + a*y + a*x + b*z + b*y + b*x. I need to know if the first three (the "a"s) are jointly significantly different from the last three (the "b"s). I have tried several approaches, but have been unsuccessful. Here's the model, and the code I came up with, with the obvious

Buenos días, Gracias Carlos, siguiendo el ejemplo que comentas, esto es lo que he introducido en el Scrip de RStudio: *library(xlsx)* *library(xlsxjars)* *library(rJava)* *library(aod)* *R<-read.csv("2002.CSV", sep=";", dec=",", header=T)* *attach(R)* *group<-gl(2,670,1340,labels= c("AVE", "Log.Imports.Value.in.1000.USD"))*

Hello R usuer, The code given below superimposes a pie diagram on another plot containing some points. However, I would like to center the pie diagram on the xy location on the plot, but not on the center. is there any way to re-center pic diagram. Any suggestion or better alternative are highly appreciated. Thank you in advance for your help. Regards, Bibke library(visualFields) library(car)

Hello R user, I have a data set from a longitudinal study ( sample below) where subjects are followed over time. Second column (status) contains info about if subject is dead or still in the study and third column is time measured in the week. Here is what I need: if status is not dead or unknown take the last week, if status is dead or unknown I need to have corresponding week. Desired resulst:

Hola a todos y todas, Gracias por vuestro apoyo en cantidad de preguntas anteriores, de nuevo os escribo para compartir una duda: Estoy trabajando con un modelo bien sencillo, es una regresión simple, pero me gustaría comprobar la significación estadística de cada uno de los coeficientes de regresión en el modelo. La idea es hacer un contraste de hipótesis. Me he descargado el paquete

Hello, I am a new R -user and request your help for the following problem. I need to merge two dataset of longitudinal study which has two column (id and respose) common. when I used merge option to join the datas side be side, because of the repeated subject id, I got larger data set which is not accurate. I would like to connect twi data sets by id and response in such a way that data are

Hi, I am running a negative binomial model using Gamlss and when I try to include random effect, I get the following message: Warning messages: 1: In vcov.gamlss(object, "all") : addive terms exists in the mu formula standard errors for the linear terms maybe are not appropriate 2: In vcov.gamlss(object, "all") : addive terms exists in the sigma formula standard

Dear all, Is there a fairly general R implementation of the delta method that uses numerical derivatives? I realise that the delta method has been implemented using symbolic derivatives (e.g. alr3::delta.method, emdbook::deltamethod, msm::deltamethod and survey:::nlcon), however possibly non-linear estimators using the delta method with numerical derivatives can be quite useful (e.g. predictnl

I'm proposing to add something like this to the stats package : ---------------------------------------------------------- ### "The" sigma in lm/nls - "like" models: sigma <- function(object, ...) UseMethod("sigma") ## works whenever deviance(), nobs() and coef() do fine: sigma.default <- function (object, use.fallback=TRUE, ...)

Hi, I'm playing around with gamlss and don't entirely understand the sigma result from an attempted lognormal fit. In the example below, I've created lognormal data with mu=10 and sigma=2. When I try a gamlss fit, I get an estimated mu=9.947 and sigma=0.69 The mu estimate seems in the ballpark, but sigma is very low. I get similar results on repeated trials and with Normal and

In an attempt to learn to use the rv package, I have been working through the examples in Jouni Kerman and Andrew Gelman's "Using Random Variables to Manipulate and Summarize Simulations in R" (July 4, 2007). I am using a Dell Precision 380n computer running Gentoo Linux and R 2.2.1 (the latest available through Gentoo's portage/emerge system). Everything worked well until I

Dear R-help This is a follow-up to my previous post here: http://groups.google.com/group/r-help-archive/browse_thread/thread/d9b6f87ce06a9fb7/e9be30a4688f239c?lnk=gst&q=dobomode#e9be30a4688f239c I am working on developing an open-source automated system for running batch-regressions on very large datasets. In my previous post, I posed the question of obtaining VIF's from the output of

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

Dear All,   thanks to Berend, my question posted yesturday was solved succesfully here: http://r.789695.n4.nabble.com/hep-on-arithmetic-covariance-conversion-to-log-covariance-td4646068.html . I posted the question with the assumption of using the results with rlnorm.rplus() from compositions. Unfortunatelly, I am not getting reasonable enough outcome. Am I applying the results wrongfully? The

sigma(model)^2 will give the correct MSE. Also note that your model matrix has intercept at the end whereas vcov will have it at the beginning so you will need to permute the rows and columns to get them to be the same/ On Wed, Sep 4, 2024 at 3:34?PM Daniel Lobo <danielobo9976 at gmail.com> wrote: > > Hi, > > I am trying to replicate the R's result for VCV matrix of

Dear list members, I want to perform an MM-regression. This seems an easy task using the function lmrob(), however, this function provides me with NA coefficients. My data generating process is as follows: rho <- 0.15 # low interdependency Sigma <- matrix(rho, d, d); diag(Sigma) <- 1 x.clean <- mvrnorm(n, rep(0,d), Sigma) beta <- c(1.0, 2.0, 3.0, 4.0) error <- rnorm(n = n,

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

> On Mar 3, 2018, at 3:04 PM, Christien Kerbert <christienkerbert at gmail.com> wrote: > > Dear list members, > > I want to perform an MM-regression. This seems an easy task using the > function lmrob(), however, this function provides me with NA coefficients. > My data generating process is as follows: > > rho <- 0.15 # low interdependency > Sigma <-

Hi all, I was working with the "MEMSS" & "mle4" library's under R version 2.15.1. apparently some practical functions of do not work under R 2.15.1. After searching the archives i found a mail thread on this subject, stating that these problems were partialy solved for "R 2.12.0" but only for "lmer()" not for "glmer()". Is someone aware

Hard to help you if you don't provide a reproducible example. On Sun, Mar 4, 2018 at 1:05 PM, Christien Kerbert < christienkerbert at gmail.com> wrote: > d is the number of observed variables (d = 3 in this example). n is the > number of observations. > > 2018-03-04 11:30 GMT+01:00 Eric Berger <ericjberger at gmail.com>: > >> What is 'd'? What is