similar to: Simple question regarding to specifying model

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

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.

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

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 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

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

Hi I am running a structural equation model with R using the sem command; am getting the following error: "Error in sem.default : The model has negative degrees of freedom = -4" My model is as follows: s_model = specify.model() x1->m1, b1,NA x2->m1, b2,NA x3->m2, b3,NA x4->m2, b4,NA x5->m2, b5,NA x6->m2, b6,NA m1->y, a1,NA m2->y, a2,NA m1<->m1, v1,NA

Hi all, I've been working with a friend's model that is a spatial model consisting of 4 patches. She uses the code found below to add migration between the patches for the three species of concern. When I run a script incorporating this code, all four patches run independently without migration occuring. When I type m1[1,2] into the workspace to see if it will return a figure, it returns,

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

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]]

Hi John Thanks a lot for the reply. Could you suggest how I can correct this problem? I tried using a correlation matrix instead of raw moments, but still got the same error. I also fixed parameters v1,v2,v3,a1 at 1; then it gave me the error that the system is exatly singular. To answer the points that you raised: 1. x1-x6 are not causes; they are just indicatiors. Does that change my

Hello, I’ve fitted a Garch(2,1) model with function 'garchFit()' from the package 'fGarch': > m1 <- garchFit(formula = ~garch(2,1),data = X,trace = F) * See 'summary(m1)' OUTPUT BELOW * PROBLEM: My alpha1 term is not significant and I would like to make a NEW model, say m2, that does not contain alpha1, but I am not sure how to specify this with the garchFit()

Hi all, Very surprising (to me!) and mystifying result from predict.glm(): the predictions vary depending on whether or not I use ns() or splines::ns(). Reprex follows: library(splines) set.seed(12345) dat <- data.frame(claim = rbinom(1000, 1, 0.5)) mns <- c(3.4, 3.6) sds <- c(0.24, 0.35) dat$wind <- exp(rnorm(nrow(dat), mean = mns[dat$claim + 1], sd = sds[dat$claim + 1])) dat <-

Dear R-help list members, I forgot to copy my reply to the r-help list. Here's most of it. John -------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox -------------------------------- -----Original Message----- From: John Fox [mailto:jfox at mcmaster.ca] Sent: Sunday,

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,

Dear R-devel, I recent got a bug report from a locfit user about the use of the subset argument when calling locfit(). Basically the symptom is that the following two calls should produce the same result, but they don't: locfit(y ~ lp(x, h=1), data=subset(dat, x > 1)) locfit(y ~ lp(x, h=1), data=dat, subset= x > 1) I've tracked the problem down to something shown in the following

hello, i'd like to evaluate the 2 factors (f1 with 4, f2 with 2 levels) and its interactions in my model for writing up a summary table with likelihood-ratio tests for publication purpose. now i'm unsure about which terms of my model may be dropped from the full model and which not. mfull<-lme(resp~f1*f2,random=...) m1<-lme(resp~f1+f2,..) m2<-lme(resp~f1,..)

Hi, R or S+ users, I want to make a simple transformation for the model, but for the whole design matrix. The model is distance ~ age * Sex, where Sex is a factor. So the design matrix may look like the following: (Intercept) age SexFemale age:SexFemale 1 1 8 0 0 2 1 10 0 0 3 1 12 0 0 4

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),