similar to: Simple question regarding to specifying model

Hello there This is the first time I have used r-help message board so I hope I have got the right address. I am trying to check the residuals of a GLMM model(run using the package lme4). I have been able to check the residiuals of REMLs in lme4 using the following: m1<-lmer(vTotal~Week+fCollar+ (1|fCat), collars) res<-resid(m1) plot(res) qqnorm(res) library(MASS) par(mfrow=c(2,3))

I'll address two issues. The first is today's error message and the other is change management for contributed packages on CRAN. TODAY'S ERROR MESSAGE I switched from the 0.995-1 versions of lme4 and Matrix to those referenced in the subject line this afternoon. Prior to using these packages on anything else, I applied them to code that 'worked' (provided numerical results

Dear R users, I'm trying to fit a gamma-frailty model on a simulated dataset, with 6 covariates, and I'm running into some results I do not understand. I constructed an example from my simulation code, where I fit a coxph model without frailty (M1) and with frailty (M2) on a number of data samples with a varying degree of heterogeneity (I'm running R 2.3.1, running takes ~1 min).

Qusetion #1 ********* Model selection in GAM can be done by using: 1. step.gam {gam} : A directional stepwise search 2. gam {mgcv} : Smoothness estimation using GCV or UBRE/AIC criterion Suppose my model starts with a additive model (linear part + spline part). Using gam() {mgcv} i got estimated degrees of freedom(edf) for the smoothing splines. Now I want to use the functional form of my model

I may be missing something obvious here, but consider the following simple dataset simulating repeated measures on 5 individuals with pretty strong between-individual variance. set.seed(1003) n<-5 v<-rep(1:n,each=2) d<-data.frame(factor(v),v+rnorm(2*n)) names(d)<-c("id","y") Now consider the following two linear models that provide identical fitted values,

My grasp of R and statistics are both seriously lacking, so if this question is completely naive, I apologize in advance. I've hunted for a couple hours on the internet and none of the methods I've found have produced the result I'm looking for. I'm currently a student in a Statistics class and we are learning the ANOVA. We had to do one by hand and then reproduce our work in SAS.

Hola. Hay alguna función que haga un Friedman test (digamos 4 tratamientos o tiempos relacionados/dependientes) y que después haga una comparación de un tratamiento contra el resto, digamos el primero, como un contratase simple, o un Dunnett? o simplemente ¿como hago un Dunnett para unos tratamientos relacionados? -- Antonio M [[alternative HTML version deleted]]

Dear professor Fox and R helpers, I have a quick question about the Anova function in the car package. When using the default "type II" SS I get results that I don't understand (see below). library(car) Data <- data.frame(y=rnorm(10), x1=factor(c(rep("a",4), rep("b",6))), x2 = factor(c(rep("j", 2), rep("k", 3), rep("j", 2),

I am used to java (well, i dont remember it really well, but anyway) I have having a really difficult time making simple loops to work. I got the following to work: ## ##Creates objects Ux1, Ux2, Ux2 etc. that all contain n numbers in a random distribution ## m<-c(m1,m2,m3,m4,m5,m6,m7,m8,m9,m10)#these are defined as numbers (means) v<-c(v1,v2,v3,v4,v5,v6,v7,v8,v9,v10)#these

Hello Ben Skeggs, The patch 88524bc06926: "drm/nouveau/devinit: move simple pll setting routines to devinit" from Mar 5, 2013, leads to the following static checker warning: drivers/gpu/drm/nouveau/nvkm/subdev/devinit/nv50.c:53 nv50_devinit_pll_set() info: return a literal instead of 'ret' drivers/gpu/drm/nouveau/nvkm/subdev/devinit/nv50.c 34 int 35

Hi, I have been using lm in R to do a linear regression and find the slope coefficients and value for R-squared. The R-squared value reported by R (R^2 = 0.9558) is very different than the R-squared value when I use the same equation in Exce (R^2 = 0.328). I manually computed R-squared and the Excel value is correct. I show my code for the determination of R^2 in R. When I do not set 0 as the

I'm pasting below a working R file featuring a function I'd like to polish up. I'm teaching regression this semester and every time I come to something that is very difficult to explain in class, I try to simplify it by writing an R function (eventually into my package "rockchalk"). Students have a difficult time with predict and newdata objects, so right now I'm

I've tried without success to calculate interaction and main effects contrasts using R. I've found the functions C(), contrasts(), se.contrasts() and fit.contrasts() in package gmodels. Given the url for a small dataset and the two-way anova model below, I'd like to reproduce the results from appended SAS code. Thanks. --Dale. ## the dataset (from Montgomery) twoway <-

Dear all, I've got a problem in fitting multilevel model in lme. I don't know to much about that but suspect that something is wrong with my model. I'm trying to fit: m1<-lme(X~Y,~1|group,data=data,na.action=na.exclude,method="ML") m2<-lme(X~Y+Z,~1|group,data=data,na.action=na.exclude,method="ML") where: X - dependent var. measured on a scale ranging from

There is a very useful and apparently fundamental feature of R (or of the package pls) which I don't understand. For datasets with many independent (X) variables such as chemometric datasets there is a convenient formula and dataframe construction that allows one to access the entire X matrix with a single term. Consider the gasoline dataset available in the pls package. For the model

Hi, Does anyone know what the distribution for the product of two correlated normal? Say I have X~N(a, \sigma1^2) and Y~N(b, \sigma2^2), and the \rou(X,Y) is not equal to 0, I want to know the pdf or cdf of XY. Thanks a lot in advance. yu [[alternative HTML version deleted]]

Hi, I''m trying to get a handle on hfsc. Here is my configuration: root@jmnrouter:/jmn# tc class show dev vlan1 class hfsc 1: root class hfsc 1:1 parent 1: ls m1 0bit d 0us m2 225000bit ul m1 0bit d 0us m2 225000bit class hfsc 1:10 parent 1:1 rt m1 191000bit d 25.0ms m2 135000bit ls m1 0bit d 0us m2 135000bit ul m1 0bit d 0us m2 225000bit class hfsc 1:20 parent 1:1 rt m1 22008bit d

I have been using lmer in lme4() to analyse the effect of dropping a term from the model as below anova.name<-anova(m1,m2) Where m1 is an original model and m2 has one term removed. I can then create my own type III tables for each variable in the model. However with many variables this becomes exceedingly time consuming! anova.lme in nlme() and Anova() in car dont seem to work for lme

Dear list, I have the following problem. I have a data frame like this CLUSTER YEAR variable Delta R_pivot M1 2005 EC01 NA NA M1 2006 EC01 2 NA M1 2007 EC01 4 5 M2 2005

Dear R community, is there a way to get correct t- and p-values and R squared for linear regression models specified without an intercept? example model: summary(lm(y ~ 0 + x)) This gives too low p-values and too high R squared. Is there a way to correct it? Or should I specify with intercept to get the correct values? Thank you in advance! Wojtek Musial -- View this message in context: