similar to: function for linear regression with White std. errors

Using hccm() I got a heteroscedasticity correction factor on the diagonal of the return matrix, but I don't know how to incorporate this into my linear model: METHOD 1: > OLS1 <- lm(formula=uer92~uer+low2+mlo+spec+degree+hit) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0623377 0.0323461 -1.927 0.057217 . uer 0.2274742 0.0758720

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

Does anyone else find that using the Var.calc option (for heteroscedasticity consistent std. errors) in Match() (from the Matching library) slows down computation of the matching estimator by a lot? I don't really understand why when I use this option it slows down so much, but for me it does significantly. I want to use the heteroscedasticity consistent std. errors in my project, but as long

I am trying to calculate robust standard errors for a logit model. I installed the package "car" and tried using hccm.default, but that required an lm object. Is there some way to do a similar operation for a glm object? x <- hccm.default(glm(winner ~ racebl + racehi + raceas + inchi + incmed + edhs + edcol + edba + agec1 + agec4 + sex + margin + regla + regbay + regsc +

Your understanding isn't similar to mine. Mine says robust/resistant methods are for data with heavy tails, not heteroscedasticity. The common ways to approach heteroscedasticity are transformation and weighting. The first is easy and usually quite effective for dose-response data. The second is not much harder. Both can be done in R with nls(). Andy From: Quin Wills > > I am

To anyone who uses the Match() function in the Matching library... How do you go about deciding how many matches you will use? With my data, my standard errors generally get smaller if I use more matches. Speaking of standard errors, when correcting for heteroscedasticity, how many matches do you use (this is the Var.cal option). It seems to me that it might make sense to use the same number

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

Does anybody have any suggestions regarding applying standard regression packages lm(), hccm(), and others within a parallel environment? Most of the packages I've found only deal with iterative processes (bootstrap) or simple linear algebra. While the latter might help, I'd rather not program the estimation code. I'm currently using a IA-64 Teragrid system through UC San Diego.

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

Thanks to the help of many on this list, I am now an R user and have been able to write some functioning code to do matching estimation. I have two for loops (i in 1:3, and j in 0:2). Within the loops, I had been creating matrices of relevant estimation coefficents in order to make lots of LaTeX tables. Well, now I want to be able to combine the results of many different estimations from within

Hi, I would like to fit a model for a factorial design that allows for unequal variances in all groups. If I am not mistaken, this can be done in lm by specifying weights. A function intended to specify weights for unequal variance structures is provided in the nlme library with the varIdent function. Is it apropriate to use these weights with lm? If not, is there another possibility to do

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

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

Hi r-programmers, I performe Breusch-Pagan tests (bptest in package lmtest) to check the homoscedasticity of the residuals from a linear model and I carry out carry out White's test via bptest (formula, ~ x * z + I(x^2) + I(z^2)) include all regressors and the squares/cross-products in the auxiliary regression. But what can I do if I want find coefficient and p-values of variables x, z, x*z,

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

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

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

I am trying to compute the white heteroskedasticity-robust standard errors (also called the Huber standard errors) in a linear model, but I can't seem to find a function to do it. I know that the design library in S+ has something like this (robcov?), but I have not yet seen this library ported to R. Anyone know if there is already a function built into R to do this relatively simple job?

I have a dataset with 4 years of students, and normally I want to estimate things using each individual year, so I have a for loop as follows for (i in 1:4){} However, the only way I know how to calculate estimates using all four years of data is to put the estimations outside of the loop. Is there anyway to make a for loop that uses all four years at once, then uses each individual year?

Dear R users, I am trying to become a convert to R (from Stata), and I'm having some growing pains. Is there anyone would be willing to answer a few basic questions I have? Of course I am using the relevant help guides... Thanks, Brian Quinif ps. If there is anyone at UGA on this list, I'd love to hear from someone local.