similar to: REmove level with zero observations

Hi, I have a simple linear regression for which I want to obtain HAC corrected standard errors, since I have significant serial/auto correlation in my residuals, and also potential heteroskedasticity. Would anyone be able to direct me to the function that implements this in R? It's a basic question and I'm sure I'm missing something obvious here. I looked up this post:

I was wondering if someone might be able to tell me what formula R's influence.measures function uses for determining whether the hat value it computes is influential (i.e., the true/false value in the "hat" column of the returned is.inf data frame). The reason I'm asking is that its results disagree with what I've just learned in my statistics class, namely that a point

influence.measures gives several measures of influence for each observation (Cook's Distance, etc) and actually flags observations that it determines are influential by any of the measures. Looks good! But how does it discriminate between the influential and non- influential observations by each of the measures? Like does it do a Bonferroni-corrected t on the residuals identified by

Hello, I would like to estimate a VAR model with HAC corrected standard errors. I tried to do this by using the sandwich package, for example: > library(vars) > data(Canada) > myvar = VAR(Canada, p = 2, type = "const") > coeftest(myvar, vcov = vcovHAC) Error in umat - res : non-conformable arrays Which suggests that this function is not compatible with the VAR command.

Hello: My dataset set contains several thousand rows of data, with each row containing information for a house. The variables include the sale price of the house, the quarter and year of sale, the attributes of the house, and the attributes of the neighborhood and the city in which the house is located. The data is for a 10-year period. No house is repeated in the dataset. In summary, the dataset

On 7/27/2016 12:17 PM, Chris Bieneman wrote: > > This is a really bad argument for large influential changes like this. Quite the contrary---anybody can participate and anybody can express their concerns, explain their goals, their workflow, etc. For a large influential changes like this, "zoning out" is a poor choice of action. > I suspect this is why the idea of having a

Hi everyones, my function like; e <- rnorm(n=50, mean=0, sd=sqrt(0.5625)) x0 <- c(rep(1,50)) x1 <- rnorm(n=50,mean=2,sd=1) x2 <- rnorm(n=50,mean=2,sd=1) x3 <- rnorm(n=50,mean=2,sd=1) x4 <- rnorm(n=50,mean=2,sd=1) y <- 1+ 2*x1+4*x2+3*x3+2*x4+e x2[1] = 10 #influential observarion y[1] = 10 #influential observarion data.x <- matrix(c(x0,x1,x2,x3,x4),ncol=5) data.y

Hi, I' am interested in differences between sample's result when samples consist of full elements and consist of only distinct elements. When sample consist of full elements it take about 120 sec., but when consist of only distinct elements it take about 4.5 or 5 times more sec. I expected that opposite of this result, because unique(sample) has less elements than full sample. Code as

Dear all, I am looking for an equivalent to the "newey2"-extension in Stata, in order to compute Newey-West HAC standard errors in a regression using panel data. I would be very grateful for advice which R-package could do this. I thank you very much in advance. Dirius

Hi! I'm using a large panel data, and now I have faced some difficulties with my analysis. The predictors are not normally distributed and there are quite many outliers (some of them are influential though). I have tried to change the logarythm, but i'm not sure, how to do that. I want also draw a plot picture in which logarythms of predictors x and y are changed. How could I do that?

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645362 I am estimating a system of seemingly unrelated regressions (SUR) in R. Each of the equations has one unique regressor and one common regressor. I am using `gmm::sysGmm` and am experimenting with different weighting matrices. I get the same results (point estimates, standard errors and

(I'm not subscribed; Mail-Followup-To set.) Contrary to the claim in the output of ./configure --help, $CPPFLAGS is in fact not influential. --- rsync-2.5.5/Makefile.in~ 2002-03-24 23:36:34.000000000 -0500 +++ rsync-2.5.5/Makefile.in 2002-11-12 17:52:04.000000000 -0500 @@ -9,6 +9,7 @@ LIBS=@LIBS@ CC=@CC@ CFLAGS=@CFLAGS@ +CPPFLAGS=@CPPFLAGS@ LDFLAGS=@LDFLAGS@

Generally speaking, this sort of detailed statistical question about a speccial package in R does not get a reply on this general R programming help list. Instead, I suggest you either email the maintainer (found by ?maintainer) or ask a question on a relevant R task view, such as https://cran.r-project.org/web/views/Econometrics.html . (or any other that you judge to be more appropriate).

Hi All, I am using R 2.11.0 on a Ubuntu machine. I estimated a model using "tsls" from the package "sem". Is there a way to get Newey West standard errors for the parameter estimates? When estimating the model by OLS, I used "NeweyWest" from the package "sandwich" to get HAC standard errors. But, I am not able to use the same method with the results of the

Dear R-users, dear Ron I use pls from the pls.pcr package for classification. Since I need to know which variables are most influential onto the classification performance, what criteria shall I look at: a) B, the array of regression coefficients for a certain model (means a certain number of latent variables) (and: squared or absolute values?) OR b) the weight matrix RR (or R in the De

Hello All,I am Nirmal Singhania from NIIT University,India. I am interested in Clustering of search results Topic. I have been in field of practical machine learning and information retrieval from quite some time. I took various courses/MOOC on Information retrieval and Text Mining and have been working on real life datasets(KDD99,AWID,Movielens). Because the problems you face in real life ML/IR

Dear community, I uploaded an excel with read.xls. My xls file actually have a column which is an id, ("plot" is the id) : plot height area 34 7.6 5.4 85 3.2 4.1 89 5.4 8.4 121 6.7 6.2 ... 1325 2.1 1.5 However R uses another id, this way: r id plot height area 1 34 7.6 5.4 2 85 3.2 4.1 3 89 5.4 8.4 4 121

dear list! i am running an anlysis on proportion data using binomial (quasibinomial family) error structure. My data comprises of two continuous vars, body size and range size, as well as of feeding guild, nest placement, nest type and foragig strata as factors. I hope to model with these variables the preference of primary forests (#successes) by certain bird species. My code therefore looks

Bueno, realmente no es necesaria que la serie esté centrada en este caso, ya que estoy sumando un ruído blanco Un saludo From: fjroar en hotmail.com To: caaperezan en gmail.com; r-help-es en r-project.org Date: Wed, 5 Nov 2014 13:00:49 +0000 Subject: Re: [R-es] Agregar ruido a una serie de tiempo Hola buenos d?as: Yo cuando he tenido que hacer estos trabajos, lo que hac?a era coger la serie

As some of you R-devel readers may know, the plot() method for "lm" objects is based in large parts on contributions by John Maindonald, subsequently "massaged" by me and other R-core members. In the statistics litterature on applied regression, people have had diverse oppinions on what (and how many!) plots should be used for goodness-of-fit / residual diagnostics, and to my