similar to: package to do inverse probability weighting in longitudinal data

Hello, I've got a question concerning the usage of robust standard errors in regression using lm() and exporting the summaries to LaTeX using the memisc-packages function mtable(): Is there any possibility to use robust errors which are obtained by vcovHC() when generating the LateX-output by mtable()? I tried to manipulate the lm-object by appending the "new" covariance

Hello, first of all: I'm new to R and have only used SPSS befor this (which can't do this at all...). I'm trying to output some regression results to latex. The regressions are normal OLS and I'm trying to output the results with standardized regression coefficients and t-statistics based on "Huber-White sandwich estimator for variance". The final result should be

Hello Everyone (Quick) question: Does anyone know a package/command or simply a way of creating a table of different panel data estimations (estimated using /*plm()*/ ) just as *mtable()* does for models estimated with /*lm()*/? It seems *mtable* (and *apsrtable* equally) only support /*lm*/ and some other classes but unfortunately not /*plm*/. I am pretty sure others must have encountered this

Hello, I am VERY new to R (one week) and I am trying to run a multinomial logit model. The model I am using is > model1 <- multinom(Y ~ X1 + X2 + , ..., Xn) if I put in > summary(model1) I get #Error in function (classes, fdef, mtable) : unable to find an inherited method for function "fitted", for signature "multinom" and if I put in > coef(model1)

Here is an example given from ?yags library(methods) data(stackloss) Y1 <- yags(stack.loss~Air.Flow,id=1:21, data=stackloss) How can I access parts of the output. I tried: > str(Y1) Formal class 'yagsResult' [package "yags"] with 25 slots ..@ coefficients : num [1:2] -44.13 1.02 ..@ coefnames : chr(0) > Y1$coefnames Error in Y1$coefnames : $

Anyone know how to get p-values for the t-values from the coefficients produced in vglm? Attached is the code and output ? see comment added to output to show where I need p-values + print(paste("********** Using VGAM function gamma2 **********")) + modl2<- vglm(MidPoint~Count,gamma2,data=modl.subset,trace=TRUE,crit="c") + print(coef(modl2,matrix=TRUE))

R-Experts: I am using the vglm function of the VGAM library to perform proportional odds ordinal logistic regression. The issue that I would like help with concerns the format in which the response variable must be provided for this function to work correctly. Consider the following example: ------ library(VGAM) library(MASS) attach(pneumo) pneumo # Inspect the format of the original dataset

Dear, When i multiply the y of a regression by 10, I would expect that the coefficient would be multiply by 10 and the z value to stay constant. Here some reproducible code to support the case. *Ex 1* library(mvtnorm) library(VGAM) set.seed(1) x=rmvnorm(1000,sigma=matrix(c(1,0.75,0.75,1),2,2))

Dear R-help gurus (and T.Yee, the VGAM maintainer) - I've been banging my head against the keyboard for too long now, hopefully someone can pick up on the errors of my ways... I am trying to use optim to fit a zero-inflated negative binomial distribution. No matter what I try I can't get optim to recognize my initial parameters. I think the problem is that dnbinom allows either

Hi All, could anybody help me to understand what is this error means ? mydata=read.table("C:/Documents and Settings/angieb/Desktop/CommercialGL/cl_ilf_claimdata.csv",header=TRUE,sep=",") > names(mydata) [1] "ILFTable" "liabLimit" "AnnAggLimit" "DedAmt" "Loss" "TIL" >

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Hi All, I'm fitting an proportional odds model using vglm() from VGAM. My response variable is the severity of diseases, going from 0 to 5 (the severity is actually an ordered factor). The independent variables are: 1 genetic marker, time of medical observation, age, sex. What I *need* is a p-value for the genetic marker. Because I have ~1.5 million markers I'd rather not faffing

The symptom triggering this email is that an S4 summary method sometimes refuses to be invoked, even when a package is explicitly loaded, if the first load of the package is implicit. It may or may not be specific to 'summary' methods and/or the 'VGAM' package. I've sent to R-devel because (i) it looks like some kind of bug to me, but I'm not sure; (ii) it's not

Dear List, I am pleased to announce the release of version 1.0.0 of DPpackage on CRAN. DPpackage covers some important models using Dirichlet process priors. The package includes: Semiparametric Bernoulli regression Semiparametric Density estimation Semiparametric Linear mixed models Semiparametric Generalized linear mixed models Semiparametric AFT model for interval-censored data I

Hi all - my first time here and am having an issue with the Predict function. I am using a tutorial as a guide, locate here: http://www.ats.ucla.edu/STAT/R/dae/mlogit.htm My code gives this error > newdata1$predicted <- predict(mlogit,newdata=newdata1,type="response") Error in `$<-.data.frame`(`*tmp*`, "predicted", value = c(0.332822934960197, : replacement has

Hello R users, I am performing a multinomial logit regression and would like to constrain a few model coefficients to be equal. Here is my model: multi <- vglm(case123con ~ SNP_A1+SNP_A2+age, multinomial, work.analy) where case123con is a four level categorical variable (case 1, case 2, case 3, control) and SNP_A1 and SNP_A2 are indicator functions (yes/no). The output of this

Dear List's Members, I'm trying to implement "1. Roger Klein and Francis Vella, ?A semiparametric model for binary response and continuous outcomes under index heteroscedasticity,? Journal of Applied Econometrics 24, no. 5 (2009): 735-762. " estimator. I have a technical doubt about the choice of the optimizer for the likelihood function maximization. That of pg. 743, the

Hi Folks, I wonderif someone who is familiar with the details of vglm in the VGAM package can assist me. I'm new to using it, and there doesn;t seem much in the documentation that's relevant to the question below. Say I have a vector x of 0/1 responses and another vector y of 0/1 responses, these in fact being a bivariate set of 0/1 responses equivalent to cbind(x,y). E.g.

I want to test whether the proportional odds assumption for an ordered regression is met. The UCLA website points out that there is no mathematical way to test the proportional odds assumption (http://www.ats.ucla.edu/stat//R/dae/ologit.htm), and use graphical inspection ("We were unable to locate a facility in R to perform any of the tests commonly used to test the parallel slopes

Dear R-Users, I want to use mtable from package "memisc" to produce Latex-style estimation output. However, mtable() only gives me a p-value and not the corresponding test-statistic. Does anyone know how to extract it, either from a glm/anova object or mtable? Here is a short example: # Run this #################### install.packages("memisc") library(memisc) set.seed(1)