similar to: intervention analysis for time series

Hi R-users, I am using arima to fit a time series. Now I would like to include an intervention component "It (0 before intervention, 1 after)" using different types of impacts, that is, not only trying the simple abrupt permanent impact (yt = w It ) with the xreg option but also trying with a gradual permanent impact (yt= d * yt-1 + w * It ), following the filosophy of Box and Tiao

Hi I'm trying to measure the effect of a policy intervention (Box and Tiao, 1975). This query has to do with the coding of the model rather than with the particulars of my dataset, so I'm not providing the actual dataset (or a simulated one) in this case, apart from some general description. The time series are of length n=34 (annual observations between 1977 and 2010). The policy

UNIVERSITY OF ZURICH, PostDoc Position in Biostatistics (~80-100%) Starting date: May 1st 2014 or later Closing date: March 10 2014 The Division of Biostatistics at the Institute of Social and Preventive Medicine of the University of Zurich (UZH) invites applications for a PostDoc position in biostatistics. Length and salary will depend of the qualifications of the appointee, in accordance with

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I try to find the most important variables to divide my dataset as given in a categorical variable using randomForest. Is randomForest() able to deal with count data? Or is there no difference because only the ranks are used in the trees? Thanks in advance Birgit Birgit Lemcke Institut f?r

R version 2.6.2 PowerBook G4 Hello R User, I try to perform an ANCOVA using the glm function. I have a dataset with continuous and categorical data (explanatory variables) and my response variable is also binary categorical. Fehler: NA/NaN/Inf in externem Funktionsaufruf (arg 4) Zus?tzlich: Warning messages: 1: In Ops.factor(y, mu) : - nicht sinnvoll f?r Faktoren (makes no sense for

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I tried rfImpute (randomForest) and as far as I understood should it replace NA`s using a proximity matrix: > set.seed(100000) > Subset5Imputed<-rfImpute(Sex~., data=Subset5) ntree OOB 1 2 300: 11.78% 12.36% 11.21% ntree OOB 1 2 300: 12.07% 12.64%

Hello R-user community! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I did the following : pdf("InLnegthMaxHomogeneity.pdf") boxplot(inflorescence_length_Max~Sex, main="Bartletts Homogeneity for inflorescence length",data=FemMal_Sex) Homo<-bartlett.test(FemMal_Sex$inflorescence_length_Max,FemMal_Sex$Sex) text( 2, 500,

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I try to find the most important variables to divide my dataset as given in a categorical variable. code: Test.rf4<-randomForest(Sex~.,na.action=na.roughfix, data=Subset4, importance=TRUE, proximity=TRUE, ntree=10000, do.trace=1000, keep.forest=FALSE) My dataset contains also ordered

Hello R-Cracks, I am using R 2.6.1 on a PowerBook G4. I would like to perform a discriminant function analysis. I found lda in MASS but as far as I understood, is it only working with explanatory variables of the class factor. My dataset contains variables of the classes factor and numeric. Is there another function that is able to handle this? Thank you all in advance. Greetings Birgit

Hello R-user community! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) Presently I try to run the function metaMDS (vegan) using an existing dissimilarity-matrix. As I would like to start with this matrix I thought I could just give the matrix using the x= -argument Test<-metaMDS(x=Dist.Gower) Fehler in inherits(comm, "dist") :

We have put a new package sparsenet on CRAN. Sparsenet fits regularization paths for sparse model selection via coordinate descent, using a penalized least-squares framework and a non-convex penalty. The package is based on our JASA paper Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet : Coordinate Descent with Non-Convex Penalties. (JASA 2011)

We have put a new package sparsenet on CRAN. Sparsenet fits regularization paths for sparse model selection via coordinate descent, using a penalized least-squares framework and a non-convex penalty. The package is based on our JASA paper Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet : Coordinate Descent with Non-Convex Penalties. (JASA 2011)

Dear All, when I try to reproduce an example of survexp, taken from the help page of survdiff, I receive the error message "Error in floor(temp) : Non-numeric argument to mathematical function" . It seems to come from match.ratetable. I think, it has to do with character variables in a ratetable. I would be interested to know, if it works for others. With an older version of

Dear All, reposting, because I did not find a solution, maybe someone could check the example below. It's taken from the help page of survdiff. Executing it, gives the error "Error in floor(temp) : Non-numeric argument to mathematical function" best regards, Heinz library(survival) ## Example from help page of survdiff ## Expected survival for heart transplant patients based

On Thursday 05 May 2016 15:19:47 John Hodrien wrote: > > I'd take a stab at: > > journalctl -fu dovecot > > The full RHEL7 System Administrators Guide is well worth a read, but here's > the bit you're probably after. > > https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/7/ht >ml/System_Administrators_Guide/s1-Using_the_Journal.html

Hellow all R fans, I wrote a R program for SCAN: Smallest CANonical Correlation Method for ARIMA(p,d,q) identification. (by Tsay and Tiao (1985)) I don't know if there is already one for this, but since I was unable to find one, so I did it. It's ready to download at the following page http://netstat.stat.tku.edu.tw/download.php I've tested the program and comared the results

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On Thu, May 5, 2016 9:58 am, Gary Stainburn wrote: > On Thursday 05 May 2016 15:19:47 John Hodrien wrote: >> I'd take a stab at: >> journalctl -fu dovecot >> The full RHEL7 System Administrators Guide is well worth a read, but here's >> the bit you're probably after. >> https://access.redhat.com/documentation/en-US/Red_Hat_Enterprise_Linux/7/ht

Hi. I am trying to analyze with BRugs the Box-Tiao variance components example in WinBUGS. The output from BRugs, mean sd MC_error val2.5pc median val97.5pc start sample sigma2.btw 681.9 1161 10.89 0.7016 253.8 4232 25001 100000 sigma2.with 4266.0 1246 4.92 2480.0000 4057.0 7262 25001 100000 doesn't match the output from WinBUGS, node mean

Hello Stephen, I am happy that you help me. Thanks a million. It is a good feeling that you confirm my assumption that dsvdis is not able to deal with missing data, because it says me that I am not completely incapable. Okay now I have the problem what to do. I used this function ´cause there is an option to weight columns differently what I haven´t found in other functions. But now I