similar to: confused about CORREP cor.LRtest

I have used merge regularly and thought I understood how it worked, but I must not. I have two dataframes with identical colnames from two different experiments, TL01 and LC01. Each dataframe has a column named "Entrez.Gene", which I have converted to "as.character" just to make sure merge is not looking at factor levels. Because I have done some filtering, the Entrez.Gene

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

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, 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) +

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

Hi, I've written a script that checks all bivariate correlations for variables in a matrix. I'm now trying to run a logistic regression on each pair (x,y) where y is a factor with 2 levels. I don't know how (or whether I want) to try to fathom what's up with glm. What I wrote is attached. Here's what I get. *****************************************************

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

Hola a tod en s, mi compañero y yo intentamos ver la correlación de nuestros datos mediante regresiones logísticas. Trabajamos con proporciones (1 variable dependiente y 1 independiente) mediante modelos mixtos (los datos están agrupados porque hay pseudoreplicación). Hemos usado el paquete "lme4" y la función "lmer". Encontramos "overdispersion" en el resultado

I'm running Mclust 3.0 in R-2.7.0 and have run into a situation where the optimal number of clusters, 12, seems to be greater than the number of colors available to Mclust. The code below, which demonstrates errors, does not if the max no of clusters is set at 10. sessionInfo, after a restart of R and loading of packages, follows the output. Is this a known problem with Mclust? Can I

Hi,   A question on something which normally should be easy !   I perform a linear regression using lm function:   > reg1 <- lm (a b+c+d, data = database1)   Then I try to perform the Chow (1960) test (structural change test) on my regression. I know the breakpoint date. I try the following code like it is described in the “Examples” section of the “strucchange” package :   > sctest(reg1,

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:

Hello! I'm doing a svar and when I make the estimation the next error message appears: In SVAR(x, Amat = amat, Bmat = bmat, start = NULL, max.iter = 1000, : The AB-model is just identified. No test possible. Could you help me to interpret it please. Also I have the identification assumption that one of my shocks is exogenous relative to the contemporaneous values of the other variables

I am running lrm() with a single factor. I then run anova() on the fitted model to obtain a p-value associated with having that factor in the model. I am noticing that the "Model L.R." in the lrm results is almost the same as the "Chi-Square" in the anova results, but not quite; the latter value is always slightly smaller. anova() calculates the p-value based on

Dear useRs, First off, sorry about the long post. Figured it's better to give context to get good answers (I hope!). Some time ago I wrote an R function that will get all pairwise interactions of variables in a data frame. This worked fine at the time, but now a colleague would like me to do this with a much larger dataset. They don't know how many variables they are going to have in the

I would like to test in R what regression fits my data best. My dependent variable is a count, and has a lot of zeros. And I would need some help to determine what model and family to use (poisson or quasipoisson, or zero-inflated poisson regression), and how to test the assumptions. 1) Poisson Regression: as far as I understand, the strong assumption is that dependent variable mean = variance.

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

Hi R, I wanted to know how do we access the elements of a list. In particular, v=list(c(1,2,3,4,5),c(1,2,33,4,5),c(1,2,333,4,5),c(1,2,3333,4,5)) I want to access all the thirds items of the elements of the list. i.e., I want to access the elements, 3,33,333,3333. This can be done through sapply as: sapply(v,function(x) x[3]) But I need to access this without using

Hello all, I would like to perform SVM-RFE (Guyon et al. 2002) in R and have only found one implementation of this algorithm. The function belongs to the MCRestimate package but when I try to use it I encounter a problem - the function appears to be missing a required package or other function that I simply cannot find available anywhere. Here is my session info followed by a simple example

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] <-