similar to: R-help Digest, Vol 128, Issue 29

I have a two part question one about statistical theory and the other about implementations in R. Thank you for all help in advance. (1) Am I correct in understanding that Heteroscedasticity is a problem for Generalized Additive Models as it is for standard linear models? I am asking particularly about the GAMs as implemented in the mgcv package. Based upon my online search it seems that some

Greetings, My question is more algorithmic than prectical. What I am trying to determine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity,

My apologies for the statistical naivete of my question but... Is there an established method or calulating the statistical power of a correlation test? And if so is there a method in R for it? Thank you, Collin Lynch.

Hi all, I'm getting contradictory results from bptest and ncvTest on a model calculated by GLS as: olslm = lm(log(rr)~log(aloi)*reg*inv, data) varlm = lm(I(residuals(olslm)^2)~log(aloi)*reg*inv, data) glslm = lm(log(rr)~log(aloi)*reg*inv, data, weights=1/fitted(varlm)) Testing both olslm and glslm with both ncvTest and bptest gives: > ncvTest(olslm) Non-constant Variance Score Test

Hello, Is there a way to perform a White test (testing heteroscedasticity) under R? Best regards, Afrae Hassouni

On Mon, 4 Apr 2016, varin sacha via R-help wrote: > Hi Deepak, > > In econometrics there is another test very often used : the white test. > The white test is based on the comparison of the estimated variances of > residuals when the model is estimated by OLS under the assumption of > homoscedasticity and when the model is estimated by OLS under the > assumption of

Hi, I need some answers regarding to R. In K-means clustering ,for K=2, How can I find the members of each cluster and What are the coordinates for the cluster centers? Please send me a reply soon. Thank You Sent from Windows Mail [[alternative HTML version deleted]]

Hi, I trying to determine the best way to compute the power for a one-sample one-sided binomial test. Specifically I need to sample a population of individuals and ask whether a sample rate of 0% is compatable with a minimum threshold of 3% and how many samples are needed. I have made use of power.prop.test but I am not sure if a) that is the correct (or best) function to use and b) if the

Dear R users, Can anyone tell me why I might get the error message "the array index is greater than the upper bound for t" in WinBUGS ? t is a vector I have defined. I have checked repeatedly that the array index for t does match with the upper bound till which the loop is run. I am facing this problem while calling the R2WinBUGS package and both in just WinBUGS as

Hi again. I'm putting together a little project with R, python, and a website. So I have an HTML file, a py file, an R file. Here is the HTML file: <form action="/cgi-bin/radio4.py" method="post" target="_blank"> <input type="radio" name="subject" value="Integrate" /> Integrate <input type="radio"

Hi everyone, I am fitting a simple linear regression model in R. My command is j=lm( Y ~ Sex + begsal + time + int) Call: lm(formula = Y ~ Sex + begsal + time + int) Coefficients: (Intercept) Sex begsal time int 191.916 -241.805 3.969 5.003 3.040 Now I wish to access the values of these coefficients for other purposes

Hi I am trying to install R-igraph, but when I issue the command : install.packages("igraph", lib="~/.R/library") in the R session, I get the following error: Warning in install.packages("igraph", lib = "~/.R/library") : 'lib = "~/.R/library"' is not writable Error in install.packages("igraph", lib =

# 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

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 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

Hi everybody, I'm trying to analyse a set of data with a non-normal response, 2 fixed effects and 1 nested random effect with strong heteroscedasticity in the model. I planned to use the function lmer : lmer(resp~var1*var2 + (1|rand)) and then use permutations based on the t-statistic given by lmer to get p-values. 1/ Is it a correct way to obtain p-values for my variables ? (see below)

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