similar to: variance inflation factor for linear mixed effects

Hello, Seeking a way to get the variance inflation factor VIF for a model of regression in complex survey, I have understood that without this package (SURVEY) RGui VIF obtained as follows: fit <- lm(mpg~disp+hp+wt+drat, data=mtcars) vif(fit) But I want to know if survey, Vif is obtained so vif( svyglm(api00~ell+meals+mobility, design=dstrat)) Thank you, happy day

Hi All, I need to calculate VIF (variance inflation factor) for my linear regression model. I found there was a function named vif in 'HH' package. I have two questions: 1) I was able to install that package in my R under windows. But while trying to install that package in UNIX, I got error: Package HH is not available. Does somebody know why? 2) Does

I have the following script, how can I implement to achieve that calculate the VIF. Thanks. U1.7km<-c(15:24) R<-c(1.2,0.2,3.6,2.5,4.8,6.3,2.3,4.1,7.2,6.1) Hm<-c(1:10) mod<-nls(R~a*(U1.7km^b)*(Hm^c), start=list(a=2.031, b=0.800, c=-0.255), trace=T) summary(mod) coef(mod) coef(summary(mod)) -- View this message in context:

Hi everyone, Running the vif() function from the car package like ---------------------------------------------------- > reg2 <- lm(CARsPur~Delay_max10+LawChange+MarketTrend_20d+MultiTrade, data=data.frame(VarVecPur)) > vif(reg2) Delay_max10 LawChange MarketTrend_20d MultiTrade 1.010572 1.009874 1.004278 1.003351

Has anyone programmed condition indexes in R? I know that there is a function for variance inflation factors available in the car package; however, Belsley (1991) Conditioning Diagnostics (Wiley) notes that there are several weaknesses of VIFs: e.g. 1) High VIFs are sufficient but not necessary conditions for collinearity 2) VIFs don't diagnose the number of collinearities and 3) No one has

Hi! I want to construct a path analysis model that can account for measurement error in totally aggregated parcels, which refer to parcels where all of the items in a scale are summed or averaged. If I am not mistaken, Bollen (1989) advocates the following formula for computing the error variance of each parcel: (1−α(parcel))×variance(parcel), such that α refers to Cronbach's alpha, which

Hi, I have a question about how I should report the results for a linear mixed effects model where the model includes as predictors three factors (facA, facB and facC), one of which (facA) interacts with the other two. facA and facB have two levels and facC has 3 levels. There are also several other continuous predictors (e.g. varA, varB, varC). My mixed model is specified with the following

Dear R-users, consider a 2-level linear mixed effects model (LME) with random intercept AND random slope for level 1 AND 2. Does anybody know how to calculate Intraclass-coefficient (ICC) for highest (innermost) level 2 ??? In the literature, I did not find an example for these kind of komplex models. For 1-level Random-Intercept models it would be easy: ICC = variance due to the clustering

Hello dear R users! I know this question is not strictly R-help, yet, maybe some of the guru's in statistics can help me out. I have a sample of data all from the same "population". Say my regression equation is now this: m1 <- lm(y ~ x1 + x2 + x3) I also regress on m2 <- lm(y ~ x1 + x2 + x3 + x4) The thing is, that I want to study the effect of

Let's assume that the columns of the model matrix, apart perhaps from an initial column that corresponds to the overall mean, have been centred. Then: 1) Normal equation methods give an accurate fit to the matrix of centred sums of squares and products. 2) QR methods give an accurate fit to the predicted values. QR will give better precision than normal equation methods (e.g., Cholesky) if

Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley

Hello, I would appreciate if somebody could help me clear my mind about the below issues. I have a factorial experiment to study the effects of Grazing and Fire on Forest biomass production. The experimental unit (to which the treatment combinations are applied) are PLOTs. The measures were made repeatedly for 13 years. I am planning to use the linear mixed effect model function lme in R for this.

We would like to announce the following statistics course: Course: Introduction to Linear Mixed Effects Models and GLMM with R Where:? Lisbon, Portugal When:?? 19-23 February 2018 Course website: http://highstat.com/index.php/courses Course flyer: http://highstat.com/Courses/Flyers/2018/Flyer2018_02LisbonV2.pdf Kind regards, Alain Zuur -- Dr. Alain F. Zuur Highland Statistics Ltd. 9 St

Hello, I am trying to fit a repeated measures linear mixed model (using lme) but I don't want to include any random effects. I'm having trouble (even after consulting Pinheiro & Bates 2000) figuring out how to specify the repeated measure without including it in the specification of a random effect. My data consist of repeated "counts" in "plots" that I wish

I have three columns of data, Xc, Trt and fish. This was a repeated measures design with 6 measurements taken from each of 5 fish. Xc is the actual measurement, Trt is the treatment, and fish is the fish number. Data can be seen below (hopefully it is in the column format). I would like to look for differences between treatments in a repeated measures format. I used the following code

All, How does one specify a model in lmer such that say the random effect for the intercept has a different variance per treatment group? Thus, in the model equation, we'd have say b_ij represent the random effect for patient j in treatment group i, with variance depending on i, i.e, var(b_ij) = tau_i. Didn't see this in the docs or Pinherio & Bates (section 5.2 is specific for

Hi folks I have some data to which I've been fitting linear mixed effects models. I am currently using a lme model in the nlme package, with terms for random effects due to repeated measures on individuals and the corCAR1 serial correlation structure. However, there is some suggestion in the data (and from theory) that a breakpoint (change point) model may be more appropriate. Scott, Norman

We would like to announce the following statistics course; Course: Introduction to Linear mixed effects models, GLMM and MCMC with R When: 10-14 February, 2014 Where: Pousada de juventude parque das nacoes. Rua de Moscavide, Lt 47 ? 101, 1998- 011. Lisbon, Portugal Info: http://www.highstat.com/statscourse.htm Flyer: http://www.highstat.com/Courses/Flyer2014_02SIM_LisbonV2.pdf Kind regards,

Dear R users I have built several glmm models using glmmPQL in the following structure: m1<-glmmPQL(dev~env*har*treat+dens, random = ~1|pop/rep, family = Gamma) (full script below, data attached) I have tried all the methods I can find to obtain some sort of model fit score or to compare between models using following the deletion of terms (i.e. AIC, logLik, anova.lme(m1,m2)), but I

Thanks to those who responded to my inquiry about generalized linear mixed models on R and S-plus. Before I summarize the software, I note that there are several ways of doing statistical inference for generalized linear mixed models: (1)Standard maximum likelihood estimation, computationally intensive due to intractable likelihood function (2) Penalized quasi likelihood or similar