similar to: Lmer, heteroscedasticity and permutation, need help please

# Using Moving/Rolling Windows, here we do an OLS Regression with corrections for #Heteroscedasticity and Autocorrelations (HAC) using Newey West Method. This code is a #extension of Ajay Shah?s code for moving windows simple OLS regression. # The easiest way to adjust for Autocorrelations and Heteroscedasticity in the OLS residuals is to #use the coeftest function that is included in the

If you don't get a response it is because you did not read the Posting Guide which indicates that the R-sig-ME mailing list is where this question would have been on-topic. -- Sent from my phone. Please excuse my brevity. On August 16, 2017 6:17:03 AM PDT, b88207001 at ntu.edu.tw wrote: >Hello dear uesRs, > >I am working on modeling both level one and level two

A better place for this post would be on R's mixed models list: r-sig-mixed-models . Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Aug 16, 2017 at 6:17 AM, <b88207001 at ntu.edu.tw> wrote: > Hello dear

Hi, I am currently trying to do a GLMM on a dataset with percent cover of seagrass (dep. var) and a suite of explanatory variables including algal (AC) and epiphyte cover (EC), rainfall, temperature and sunshine hours. M2=glmer(SG~AC+EC+TP+SS+RF+(1|Location/fSi/fTr), family=binomial,data=data,nAGQ=1) As the dependent variable is percent cover, I used a binomial error structure. I also have a

Dear all, Sorry if this is too obvious. I am trying to fit my multiple regression model using lm() Before starting model simplification using step() I checked whether the model presented heteroscedasticity with ncv.test() from the CAR package. It presents it. I want to correct for it, I used hccm() from the CAR package as well and got the Heteroscedasticity-Corrected Covariance Matrix. I am not

Hello dear uesRs, I am working on modeling both level one and level two heteroscedasticity in HLM. In my model, both error variance and variance of random intercept / random slope are affected by some level two variables. I found that nlme is able to model heteroscedasticity. I learned how to use it for level one heteroscedasticity but don't know how to use it to model the level

Dear altogether, is it possible to integrate "weights" arguments within lmer to incorporate statements to handle heteroscedasticity as it is possible with lme? I searched the R-archive but found nothing, insofer I assume it is not possible, but as lmer is under heavy develpoment, maybe something changed or is solved differently. Thus my question: While encountering heavy

To detect heteroscedasticity for a multiple linear OLS regression (no time dependencies): What if the residuals vs. fitted values plot shows well behaved residuals (cloud) - but the some of the x versus residuals plots are a megaphone? Also, it seems that textbooks and internet tutorials in R do not agree what is the best plot for detecting heteroscedasticity. What do you use? I found so

Dear All, i want to use garch model in return of stock. and the data should presence volatility cluster (Heteroscedasticity). Do you know how to test volatility cluster (the presence of heteroscedasticity) in series data of stock return in R? Is it using Langrange Multiplier (LM) ARCH test? what package i should use? I really need the help. Thanks for the attention. Eko A P

Hello, I have just recently started using R and was wondering whether anybody had a code written for White's heteroscedasticity correction for standard errors. Also, can anybody share a code for the GARCH(1,1) and GARCH-in-mean models for modelling regression residuals? Thanks a lot in advance, Spyros --------------------------------- [[alternative HTML version

Hi, I would like to generate two correlated variables. I found that funktion for doing that: a <- rmvnorm(n=10000,mean=c(20,20),sigma=matrix(c(5,0.8*sqrt(50), 0.8*sqrt(50),10),2,2)) (using library(mvtnorm)) Now I also want to generate two correlated variables where the error variance vary over the variable-correlation. And I want to plot this for showing heteroscedasticity. Like shown

Dear All, I'm trying to model heteroscedasticity using a multilevel model. To do so, I make use of the nlme package and the weigths-parameter. Let's say that I hypothesize that the exam score of students (normexam) is influenced by their score on a standardized LR test (standLRT). Students are of course nested in "schools". These variables are contained in the

Hello, as far as I see, R reports type I sums of squares. I'd like to get R to print out type III sums of squares. e.g. I have the following model: vardep~factor1*factor2 to get the type III sum of squares for factor1 I've tried anova(lm(vardep~factor2+factor1:factor2),lm(vardep~factor1*factor2)) but that didn't yield the desired result. Could anyone give me a hint how to proceed?

Hi there, Does anyone know of a statistical test for heteroscedasticity for an object of class "gls"? (or alternative objective methods). Thanks in advance, Ben Gillespie, Research Postgraduate o-------------------------------------------------------------------o School of Geography, University of Leeds, Leeds, LS2 9JT o-------------------------------o http://www.geog.leeds.ac.uk/

Hello, First, sorry for my poor english, I will try to be understood. It's the first time I try this "r-help mailing list" and I hope it will be a success. I am working on heteroscedasticity analysis. I would like to get the "Box-Ljung" and the "Lagrange multipliers" test. I found the first one in the library "ts", but I can't find the second one.

Hello, Could anyone please tell me how to estimate Heteroscedasticity Consistent standard errors for a Spatial error model? All the functions I have looked at only works for lm objects. Thank you very much! - Oshadhi

Dear Listers, I have a regression problem (x->y) with biological data, where x influences y in two ways, (1) y increases with x and (2) the variation around the mean (residuals) decreases with increasing x, i.e. y becomes more 'predictable' as x increases. The relationship is saturating, y~a + bx + cx^2, gives a very good fit. I know basically how to test for heteroscedasticity. My

What is command for tobit Heteroscedasticity? FErnando Freitas [[alternative HTML version deleted]]

Hi I am actually running the White test for correcting Heteroscedasticity. I used sandwich() & car(), however the output shows the updated t test of coefficients, with revised Standard Errors, however the estimates remained same. My problem is that the residuals formed a pattern in the original regression equation. After running the White's test, I got some new standard errors - but

Dear lmer users, The experiment includes 15 groups of (3 males and 1 female). The female is characterized by its quality Q1 and Q2. Each male of a group is characterized by the number of MatingAttempts (with Poisson distribution). I want to examine if male mating attempts depend on female quality. I can see from graphic exploration that the within-group heterogeneity of male attempts increases