similar to: Is glm weird or am I?

I am able to split my df into two like so: dataset <- trainset index <- 1:nrow(dataset) testindex <- sample(index, trunc(length(index)*30/100)) trainset <- dataset[-testindex,] testset <- dataset[testindex,-1] So I have the index information, how could I re-combine the data using that back into a single df? I tried what I thought might work, but failed with:

Dear R People: Hello! I'm putting together another RcmdrPlugin package and need to add a FORTRAN subroutine to speed things up a bit. I've never added compiled code to a package. Anyhow, I put my code into a /src directory. Here are the statements: * using log directory 'c:/R/R-2.11.1/bin/RcmdrPlugin.push.Rcheck' * using R version 2.11.1 (2010-05-31) * using session charset:

Dear R users, The code below splits a dataset by "target_species" and then further splits each target species group by year. Finally the sum of a variable called "total_hook_num" is calculated and then assigned a name based on (paste(a$target_species[1],a$year[1],sep=""). newsplit<-split(newdataset,list(newdataset$target_species)) for(i in newsplit){

In my own package, I want to use the default S3 method of the generic function lrtest() from the lmtest package. Since I need only one function from lmtest, I tried to use importFrom in my NAMESPACE: importFrom(lmtest, lrtest) However, this fails R CMD check in the examples: Error in UseMethod("lrtest") : no applicable method for 'lrtest' applied to an object of class

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

Hello. One of my teaching assistants was experimenting and encountered unexpected behaviour with the lrtest function in the rms package. It appears that when you have a pair of non-nested models that employ an RCS, the error checking for non-nested models appears not to work. Here is a reproducible example. > library(rms) Loading required package: Hmisc Loading required package: lattice

Hi, I'm trying to extract a histogram over the results from a bootstrap. However I keep receiving the error message "Error in hist.default(boot.lrtest$ll, breaks = "scott") : 'x' must be numeric". The bootstrap I'm running looks like: > boot.test <- function(data, indeces, maxit=20) { + y1 <- fit1+e1[indeces] + mod1 <- glm(y1 ~ X1-1, maxit=maxit) +

After some struggling with the data format, non-standard in BioConductor, I have gotten cor.balance in package CORREP to work. My desire was to obtain maximum-likelihood p-values from the same data object using cor.LRtest, but it appears that this function wants something different, which I can't figure out from the documentation. Briefly, my dataset consists of 36 samples from 12

Hi. Thanks for your help with my previous question (comparing two lm() models with a maximum likelihood ratio test) I had a look at lrtest from the package lmtest as it has been suggested to me, but I am not 100% sure if that is the right thing to do ... lrtest uses the same log likelihoods as you can extract by hand from lm() with logLik - are this the maximum log likelihoods? How does R

Hello. I am still a newbie in R. Excuse me if I am asking something obvious. My efforts to get an answer through browsing the mailing archives failed. I want to perform an augmented Dickey-Fuller test and to obtain AIC and BIC and to be able to impose some linear restrictions on the ADF regression so as to decide the correct order of autoregression. However I could find no obvious way to impose

I would like to perform a permutation test for Cox proportional hazards regression model. I only find it for t-test and other tests (e.g. comparing two medians). Is there a way that I can perform a Cox PH model in R or SAS for the LR-test? I am doing the following B <- 1000; LRtestx <- rep(NA,B); Srv <- Surv(Time, Event); for(j in 1:B){ LRtestx[j] <-

Hoping someone out there can help me...this seems like an easy task but I can't figure it out... I want to replace variables in one dataset (Dataset1) with a variable from another dataset (Dataset2). All the values for variables x1 and x2 in Dataset1 have a unique match to the variable uniquenum in Dataset2. For example, in Dataset1 grpnum A has a value of 343 for variable x1. In Dataset2,

Dear List, I have two questions about how to do predictions using lrm, specifically how to predict the ordinal response for each observation *individually*. I'm very new to cumulative odds models, so my apologies if my questions are too basic. I have a dataset with 4000 observations. Each observation consists of an ordinal outcome y (i.e., rating of a stimulus with four possible

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 all I have developed a zero-inflated negative binomial model using the zeroinfl function from the pscl package, which I have carried out model selection based on AIC and have used likelihood ratio tests (lrtest from the lmtest package) to compare the nested models [My end model contains 2 factors and 4 continuous variables in the count model plus one continuous variable in the zero-inflated

Hi, I'm having trouble understanding how to construct a random number generator for a parametric bootstrap. My aim is to bootstrap a Likelihood Ratio statistic (under the null) for a linear model. The function at this point is given by boot.test.n01 <- function(data, indeces, maxit=20) { y1 <- fit1+se(e2)*rnorm(314) mod1 <- glm(y1 ~ X1-1, maxit=maxit) y2 <-

Hello folks! I'm using R-Package {vars} and I'm trying to estimate an A-Model. I have serious problems regarding the restrictions. 1) My A-Matrix needs (!) to have the following form: # 1 NA NA NA # 0 1 NA NA # 0 0 1 NA # 0 0 0 1 That is done in R by: A_Matrix <- diag(4) # main diagonal = 4 restrictions A_Matrix [1, 2] <- NA # A_Matrix [1, 3] <- NA #

Hi Version 0.7 of package tsDyn presented at useR! 2009 is now on CRAN, extended with several new features. The package tsDyn is aimed at estimating nonlinear time series models which exhibit regime specific properties. The regime switching dynamics can either be described by smooth transition (STAR and LSTAR) or threshold effects (SETAR). The package furthermore offers nonlinear models

Hi Version 0.7 of package tsDyn presented at useR! 2009 is now on CRAN, extended with several new features. The package tsDyn is aimed at estimating nonlinear time series models which exhibit regime specific properties. The regime switching dynamics can either be described by smooth transition (STAR and LSTAR) or threshold effects (SETAR). The package furthermore offers nonlinear models

First I have to say I am sorry because I have not been so clear in my previous e-mails. I will try to explain clearer what it is my problem. I have the following model: lnP=Sc+Ag+Ag2+Var+R+D In this model the variable Sc is endogenous and the rest are all objective exogenous variables. I verified that Sc is endogenous through a standard Hausman test. To determine this I defined before a new