similar to: problem assigning an array to a variable in a data frame

Dear R list members, I've turned up a strange discrepancy between results obtained from the lme function in the nlme package in R and results obtained with lme in S-PLUS. I'm using version 3.1-24 of nlme in R 1.4.1 under Windows 2000, and both S-PLUS 2000 and 6.0, again under Windows 2000. I've noticed discrepancies in a couple of instances. Here's one, using data from Bryk

Hi all, Could you please help me? I am trying to understand why this line works: lm1x = lm(y~X-1, tmp) Here it seems that I was combining the design matrix and the data frame... And X below is not a single column, in fact, it's a bunch of columns in matrix form... I don't understand why this line works... Is it just luck, i.e. if we change the data-set and/or formulas to something

In our recent workshop on "Multilevel Modeling in R" we discussed handling data for multilevel modeling. An classic example of such data are test scores of students grouped into schools. We may wish to model the scores as functions of both student-level covariates and school-level covariates. Such data are often organized in a multi-table format with a separate table for each level of

Buenas noches, mi pregunta es tal vez sencilla. tengo esta libreria y estos datos library(nlme) data(MathAchieve) Infoest = MathAchieve en esta base quiero contar la cantidad de casos en la que la columna Sex, toma el valor Female, como puedo hacerle el filtro para ver solo los Females. y como puedo dentro del mismo conjunto de datos agregar la variable numerica que me cuente 1 si es Female y 0

Hey I'm not really sure what I should put on here, but I am having trouble with my R code. I am trying to get the p-values, R^2s etc for a number of different groups of variables that are all in one dataset. This is the code: #Stand counter st<-1 #Collections stands<-numeric(67) slopes<-numeric(67) intercepts<-numeric(67) mses<-numeric(67) rsquares<-numeric(67)

Dear community, I'm trying to examine my grouped data following page 6 http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-mixed-models.pdf. I'm trying lmList this way: model.list <- lmList(log(v.dep) ~ log(v2) +log(v4) + v3 | v5, subset = v6=0, data=data) and obtain this error message: In Ops.factor(v4, v4) : | not meaningful for factors My original model is

Can I ask a small stat. related question here? Suppose I have two predictors for a time series processes and accuracy of predictor is measured from MSEs. My question is, if two predictors give same MSE then, necessarily they have to be identical? Can anyone provide me any counter example? Thanks. -- View this message in context:

Dear all, I have the following question. I have to fit the hierarchical model for the hypothesis concern the individual-level effects by controlling for the individual -level attributes and national-level contextual effects on individuals by using R. O have to obtain the estimates of the impact of the second-level (national: GDP per capita) effects on individuals ( in this instance the impact

Hola Alexander. Para filtrar y obtener el subconjunto que necesitas, puedes hacerlo de la siguiente (una de ellas) manera: > Female <- subset(MathAchieve, subset=Sex=="Female") Crear una nueva variable recodificando "Sex" puedes hacerlo así: > Sex_recode <- Recode(MathAchieve$Sex, '"Female"=1; "Male"=0; ;', as.factor.result=TRUE) >

I'm looking at Pinheiro & Bates "Mixed-Effects Models in S and S-PLUS" at the moment. Several datasets are used, one of which is called "PBIB" (a partially balanced incomplete block design). All the other datasets can be found somewhere or other in R. However, I cannot locate PBIB, and it does not seem to be mentioned in the latest edition of the R Full Reference

I am running the R-0.62.0 snapshot on a Linux system. This system has 128 Mb of real memory and almost never swaps. I am trying to use lme() on a very large example (7200 observations, 10 columns in the fixed effects, 2 columns in the random effects). We have the optimization nicely tuned so that goes relatively quickly. It seems, though, that all the data manipulation steps before the

Dear listers, I can't find the variance or se of the coefficients in a multilevel model using lme. I want to calculate a Chi square test statistics for the variability of the coefficients across levels. I have a simple 2-level problem, where I want to check weather a certain covariate varies across level 2 units. Pinheiro Bates suggest just looking at the intervals or doing a rather

I sent this to Bioconductor, but I think it is more of an R listserv question, because of memory allocation trouble I am having trouble getting the function heatmap() to work on the following gene expression > dim(SAMPLES_log) [1] 12626 20 sample1 sample2...................sample20 gen1 gen2 gen3 .... gen12626 I have converted SAMPLES_log to a numeric matrix using:

Hi, all. I'm trying to use some linear regression models in which both the dependent and independent variables are measured with some error. To make things worse, while the errors in the dependent variable are uniform, the errors in the independent (or explanatory, or "x") variables can be heteroskedastic. I've been looking at the book _Measurement Error Models_ by Fuller

Hi, does anyone out there have a recommendation for multilevel / random effects and longitudinal analysis? My dream book would be something that's both accessible to a non-statistician but rigorous (because I seem to be slowly turning into a statistician) and ideally would use R. Peter

Hi, I'm performing (blocked) 10-fold cross-validation of a several time series forecasting methods, measuring their mean squared error (MSE). I know that the MSE_cv is the average over the 10 MSEs. Is there a way to calculate the standard error as well? The usual SD/sqrt(n) formula probably doesn't apply here as the 10 observations aren't independent. Thanks, Gad -- Gad

A new version of the randomForest package is now available on CRAN. The DESCRIPTION is: Package: randomForest Title: Breiman's random forest for classification and regression Version: 3.4-1 Depends: R (>= 1.5.0) Author: Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener. Description: Classification and regression based on a forest of trees using

My son is taking some programming courses in school and it would be very helpful if he could run Visual Studio on his Mac. I own older VB5 and Visual Studio 6 (from my old PC days). [Embarassed] I cannot figure out how to install Wine on his Mac. Can someone please guide me (e.g. show me all the steps I need to follow to install Wine) so that I can then install VB5 and VS6? He's not

> importance(rfor.pdp11_t25.comb1,type=1) %IncMSE v1 -0.28956401263 v2 1.92865561147 v3 -0.63443929130 v4 1.58949137047 v5 0.03190940065 I wasn't entirely confident with interpreting these results based on the documentation. Could you please interpret? [[alternative HTML version deleted]]

Greetings. I'm running some models under R using glmmPQL from MASS. These are three-level models (two grouped levels and the individual level) with dichotomous outcomes. There are several statistics of interest; for the moment, I have two specific questions: 1.) This question refers to the following model (I present first the call, then the output of summary():